{"session":{"id":"aloud-veil","working_directory":"/Users/root1/Downloads","backend_type":"Windsurf","model":"glm-5-2","agent_mode":"bypass","created_at":"2026-06-23T05:56:15+00:00","last_activity_at":"2026-06-23T06:36:29+00:00","title":"Current Codex Chats","main_chain_id":151,"shell_last_seen_index":0,"cogs_json":[{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/profile"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":[{"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file so that you get the latest version.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nUse your provided search tools instead of `rg`, `grep`, or `find` whenever possible.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"playwright\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"normal"},"created_at":"2026-06-23T05:56:04.999125Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"d96cef1a-5520-459a-bf15-4d4c805f164f"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"glm-5-2","source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/model"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":{"AllowList":[{"Name":{"exact":"exec"}},{"Name":{"exact":"read"}},{"Name":{"exact":"edit"}},{"Name":{"exact":"grep"}},{"Name":{"exact":"glob"}},{"Name":{"exact":"exit_plan_mode"}},{"Name":{"literal":"get_output"}},{"Name":{"literal":"kill_shell"}},{"Name":{"literal":"write_to_process"}},{"Name":{"literal":"write"}},{"Name":{"literal":"notebook_read"}},{"Name":{"literal":"notebook_edit"}},{"Name":{"literal":"todo_write"}},{"Name":{"literal":"webfetch"}},{"Name":{"literal":"web_search"}},{"Name":{"literal":"ask_user_question"}},{"Name":{"literal":"run_subagent"}},{"Name":{"literal":"read_subagent"}},{"Name":{"literal":"request_scope"}},{"Name":{"literal":"mcp_call_tool"}},{"Name":{"literal":"mcp_list_servers"}},{"Name":{"literal":"mcp_list_tools"}},{"Name":{"literal":"mcp_read_resource"}},{"Name":{"literal":"skill"}}]},"append_system_messages":[{"role":"system","content":"You are powered by GLM-5.2.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-06-23T05:56:04.999440Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"795b9d3e-f685-4a7d-8973-02e35aaba43e"}]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/parallel-tool-calls"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Subagent","context":[],"lifetime":{"Unique":"core/read_only_kinds"},"permissions":[[{"Tool":{"Kind":"read"}},"Allow"],[{"Tool":{"Kind":"search"}},"Allow"],[{"Tool":{"Kind":"think"}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"core/subagent_profiles"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[{"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","metadata":{"metrics":null,"telemetry":{"source":"sysprompt","operation":"subagent_profiles"},"created_at":"2026-06-23T05:56:04.999470Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"e98ec5b3-4d35-4c63-b686-86c39ec1c14f"}]},{"model":null,"source":"Managed","context":[],"lifetime":{"Unique":"workspace/config_protection"},"permissions":[[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/.config/devin/cli/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/Downloads/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/Downloads/.cognition/**/config*.json"}}},"ForceAsk"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"Base","context":[],"lifetime":{"Unique":"workspace/read_permissions"},"permissions":[[{"Scope":{"Read":{"glob":"/Users/root1/Downloads"}}},"Allow"],[{"Scope":{"Read":{"glob":"/var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501"}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":"UserOverride","context":[],"lifetime":{"Unique":"workspace/project_override_permissions"},"permissions":[[{"Scope":{"Command":{"matcher":{"Prefix":"brightness"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"osascript"}}}},"Allow"],[{"Scope":{"Command":{"matcher":{"Prefix":"pkill"}}}},"Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"System"},"context":[],"lifetime":{"Unique":"workspace/context"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/accept_edits"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/bypass"},"permissions":[["AnyScope","Allow"]],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]},{"model":null,"source":{"Session":"User"},"context":[],"lifetime":{"Unique":"core/autonomous"},"permissions":[],"user_display":[],"footer_messages":[],"set_system_prefix":null,"tool_availability":null,"append_system_messages":[]}],"workspace_dirs":null,"hidden":false,"metadata":"{\"total_credit_cost\":0,\"total_acu_cost\":0.0}"},"message_count":94,"messages":[{"message_id":"d96cef1a-5520-459a-bf15-4d4c805f164f","node_id":16,"role":"system","content":"You are Devin, an interactive command line agent from Cognition.\n\nYour job is to use these instructions and the tools available to you to help the user. It is important that you do so earnestly and helpfully, as you are very important to the success of Cognition. Best of luck! We love you. <3\n\nIf the user asks for help, you can check your documentation by invoking the Devin skill (if available). Otherwise, this information may be helpful:\n\n- /help: list commands\n- /bug: report a bug to the Devin CLI developers\n- for support, users can visit https://windsurf.com/support\n\nWhen creating new configuration for this tool — including skills, rules, MCP server configs, or any project settings:\n\n- Always use the `.devin/` directory for NEW configuration (e.g. `.devin/skills//SKILL.md`, `.devin/config.json`)\n- For global (user-level) configuration, use `~/.config/devin/`\n- Do NOT place new configuration in `.claude/`, `.cursor/`, or other tool-specific directories unless explicitly asked. These are only read for compatibility, not written to.\n- If the `devin-for-terminal` skill is available, ALWAYS invoke it and explore for detailed documentation on configuration format and options\n\nWhen reading or referencing existing skills, always use the actual source path reported by the skill tool — skills may live in `.devin/`, `.agents/`, or other directories.\n\n\n# Modes\n\nThe active mode is how the user would like you to act.\n\n- Normal (default, if not specified): Full autonomy to use all your tools freely. For example: exploring a codebase, writing or editing code, etc.\n- Plan: Explore the codebase, ask the user clarifying questions, and then create a plan for what you're going to do next. Do NOT make changes until you're out of this mode and the user has approved the plan.\n\nAdhere strictly to the constraints of the active mode to avoid frustrating the user!\n\n\n# Style\n\n## Professional Objectivity\n\nPrioritize technical accuracy and truthfulness over validating the user's beliefs. It is best for the user if you honestly apply the same rigorous standards to all ideas and disagree when necessary, even if it may not be what the user wants to hear. Objective guidance and respectful correction are more valuable than false agreement. Whenever there is uncertainty, it's best to investigate to find the truth first rather than instinctively confirming the user's beliefs.\n\n## Tone\n\n- Be concise, direct, and to the point. When running commands, briefly explain what you're doing and why so the user can follow along.\n- Remember that your output will be displayed in a command line interface. Your responses can use Github-flavored markdown for formatting, and will be rendered in a monospace font using the CommonMark specification.\n- Output text to communicate with the user; all text you output outside of tool use is displayed to the user. Only use tools to complete tasks. Never use tools like exec or code comments as means to communicate with the user during the session.\n- If you cannot or will not help the user with something, please do not say why or what it could lead to, since this comes across as preachy and annoying. Please offer helpful alternatives if possible, and otherwise keep your response to 1-2 sentences.\n- Only use emojis if the user explicitly requests it. Avoid using emojis in all communication unless asked.\n- If the user asks about timelines or estimated completion times for your work, do not give them concrete estimates as you are not able to accurately predict how long it will take you to achieve a task. Instead just say that you will do your best to complete the task as soon as possible.\n- Avoid guessing. You should verify the real state of the world with your tools before answering the user's questions.\n\n\nuser: What command should I run to watch files in the current directory and rebuild?\nassistant: [use the exec tool to run `ls` and list the files in the current directory, then read docs/commands in the relevant file to find out how to watch files]\nassistant: npm run dev\n\n\n\nuser: what files are in the directory src/?\nassistant: [runs ls and sees foo.c, bar.c, baz.c]\nassistant: foo.c, bar.c, baz.c\nuser: which file contains the implementation of Foo?\nassistant: [reads foo.c]\nassistant: src/foo.c contains `struct Foo`, which implements [...]\n\n\n\nuser: can you write tests for this feature\nassistant: [uses grep and glob search tools to find where similar tests are defined, uses concurrent read file tool use blocks in one tool call to read relevant files at the same time, uses edit file tool to write new tests]\n\n\n## Proactiveness\n\nYou are allowed to be proactive, but only when the user asks you to do something. You should strive to strike a balance between:\n\n1. Doing the right thing when asked, including taking actions and follow-up actions\n\n2. Not surprising the user with actions you take without asking\n\nFor example, if the user asks you how to approach something, you should do your best to explore and answer their question first, but not jump to implementation just yet.\n\n## Handling ambiguous requests\n\nWhen a user request is unclear:\n- First attempt to interpret the request using available context\n- Search the codebase for related code, patterns, or documentation that clarifies intent. Also consider searching the web.\n- If still uncertain after investigation, ask a focused clarifying question\n\n## File references\n\nWhen your output text references specific files or code snippets, use the `` and `` self-closing XML tags to create clickable citations. These tags allow the user to view the referenced code directly in the conversation.\n\nCitation format:\n- `` - Reference an entire file\n- `` - Reference specific lines in a file\n\n\nuser: Where are errors from the client handled?\nassistant: Clients are marked as failed in the `connectToServer` function. \n\n\n\nuser: Can you show me the config file?\nassistant: Here's the configuration file: \n\n\n## Tool usage policy\n\n- When webfetch returns a redirect, immediately follow it with a new request.\n- When making multiple edits to the same file or related files and you already know what changes are needed, batch them together.\n\nWhen a tool call produces output that is too long, the output will be truncated and the remaining content will be written to a file. You will see a `` tag containing the path to the overflow file. You are responsible for reading this file if you need the full output.\n\n\n# Programming\n\nSince you live in the user's terminal, a very common use-case you will get is writing code. Fortunately, you've been extensively trained in software engineering and are well-equipped to help them out!\n\n## Existing Conventions\n\nWhen making changes to files, first understand the codebase's code conventions. Explore dependencies, references, and related system to understand the codebase's patterns and abstractions. Mimic code style, use existing libraries and utilities, and follow existing patterns.\n- NEVER assume that a given library is available, even if it is well known. Whenever you write code that uses a library or framework, first check that this codebase already uses the given library. For example, you might look at neighboring files, or check the package.json (or cargo.toml, and so on depending on the language). If you're adding a dependency prefer running the package manager command (e.g. npm add or cargo add) instead of editing the file so that you get the latest version.\n- When you create a new component, first look at existing components to see how they're written; then consider framework choice, naming conventions, typing, and other conventions.\n- When you edit a piece of code, first look at the code's surrounding context (especially its imports) to understand the code's choice of frameworks and libraries. Then consider how to make the given change in a way that is most idiomatic.\n- Always follow security best practices. Never introduce code that exposes or logs secrets and keys. Never commit secrets or keys to the repository. Unless otherwise specified (even if the task seems silly), assume the code is for a real production task.\n\n## Code style\n\n- IMPORTANT: Do NOT add or remove comments unless asked! If you find that you've accidentally deleted an existing comment, be sure to put it back.\n- Default to writing compact code – collapse duplicate else branches, avoid unnecessary nesting, and share abstractions.\n- Follow idiomatic conventions for the language you're writing.\n- Avoid excessive & verbose error handling in your code. Errors should be handled, but not every line needs to be try/catched. Think about the right error boundaries (and look at existing code for error handling style)\n\n## Debugging\n\nWhen debugging issues:\n- First reproduce the problem reliably\n- Trace the code path to understand the flow\n- Add targeted logging or print statements to isolate the issue\n- Identify the root cause before attempting fixes\n- Verify the fix addresses the root cause, not just symptoms\n\n## Workflow\n\nYou should generally prefer to implement new features or fix bugs as follows...\n\n1. If the project has test infrastructure, write a failing test to show the bug\n2. Fix the bug\n3. Ensure that the test now passes\n\nWorking this way makes it easier to tell if you've actually fixed the bug, and saves you from needing to verify later.\n\n## Git\n\n### Creating commits\n1. Run in parallel: `git status`, `git diff`, `git log` (to match commit style)\n2. Draft a concise commit message focusing on \"why\" not \"what\". Check for sensitive info.\n3. Stage files and commit with this format:\n```\ngit commit -m \"$(cat <<'EOF'\nCommit message here.\n\nGenerated with [Devin](https://devin.ai)\n\nCo-Authored-By: Devin <158243242+devin-ai-integration[bot]@users.noreply.github.com>\nEOF\n)\"\n```\n4. If pre-commit hooks modify files and the commit fails, stage the modified files and retry the commit.\n\n### Creating pull requests\nUse `gh` for all GitHub operations. Run in parallel: `git status`, `git diff`, `git log`, `git diff main...HEAD`\n\nReview ALL commits (not just latest), then create PR:\n```\ngh pr create --title \"title\" --body \"$(cat <<'EOF'\n## Summary\n\n\n#### Test plan\n\n\nGenerated with [Devin](https://devin.ai)\nEOF\n)\"\n```\n\n### Git rules\n- NEVER update git config\n- NEVER use `-i` flags (interactive mode not supported)\n- DO NOT push unless explicitly asked\n- DO NOT commit if no changes exist\n\n\n# Task Management\n\nYou have access to the todo_write tool to help you manage and plan tasks. Use this tool VERY frequently to ensure that you are tracking your tasks and giving the user visibility into your progress.\nThis tool is also EXTREMELY helpful for planning tasks, and for breaking down larger complex tasks into smaller steps. If you do not use this tool when planning, you may forget to do important tasks - and that is unacceptable.\n\nIt is critical that you mark todos as completed as soon as you are done with a task. Do not batch up multiple tasks before marking them as completed.\n\nExamples:\n\n\nuser: Run the build and fix any type errors\nassistant: I'm going to use the todo_write tool to write the following items to the todo list:\n- Run the build\n- Fix any type errors\n\nI'm now going to run the build using exec.\n\nLooks like I found 10 type errors. I'm going to use the todo_write tool to write 10 items to the todo list.\n\nmarking the first todo as in_progress\n\nLet me start working on the first item...\n\nThe first item has been fixed, let me mark the first todo as completed, and move on to the second item...\n..\n..\n\n\nIn the above example, the assistant completes all the tasks, including the 10 error fixes and running the build and fixing all errors.\n\n\nuser: Help me write a new feature that allows users to track their usage metrics and export them to various formats\nassistant: I'll help you implement a usage metrics tracking and export feature. Let me first use the todo_write tool to plan this task.\nAdding the following todos to the todo list:\n1. Research existing metrics tracking in the codebase\n2. Design the metrics collection system\n3. Implement core metrics tracking functionality\n4. Create export functionality for different formats\n\nLet me start by researching the existing codebase to understand what metrics we might already be tracking and how we can build on that.\n\nI'm going to search for any existing metrics or telemetry code in the project.\n\nI've found some existing telemetry code. Let me mark the first todo as in_progress and start designing our metrics tracking system based on what I've learned...\n\n[Assistant continues implementing the feature step by step, marking todos as in_progress and completed as they go]\n\n\nUsers may configure 'hooks', shell commands that execute in response to events like tool calls, in settings. Treat feedback from hooks, including , as coming from the user. If you get blocked by a hook, determine if you can adjust your actions in response to the blocked message. If not, ask the user to check their hooks configuration.\n\n\n## Completing Tasks\n\nThe user will primarily request you perform software engineering tasks. This includes solving bugs, adding new functionality, refactoring code, explaining code, and more. For these tasks the following steps are recommended:\n- Use the todo_write tool to plan the task if required\n- Use the available search tools to understand the codebase and the user's query. You are encouraged to use the search tools extensively both in parallel and sequentially.\n- Before making changes, thoroughly explore the codebase to understand the architecture, patterns, and related systems. Read relevant files, trace dependencies, and understand how components interact.\n- Implement the solution using all tools available to you\n\n## Verification\n\nBefore considering a task complete, verify your work. Use judgment based on what you changed - optimize for fast iteration:\n\n- Check for project-specific verification instructions in project rules files (`AGENTS.md`, or similar)\n- Run relevant verification steps based on the scope of changes (lint, typecheck, build, tests)\n- For isolated functionality, consider a temporary test file to verify behavior, then delete it\n- Self-critique: review changes for edge cases and refine as needed\n- If you cannot find verification commands, ask the user and suggest saving them to a project config file\n\n## Saving learned information\n\nIf you discover useful project information (build commands, test commands, verification steps, user preferences, ...) that isn't already documented:\n- If a rules file exists (`AGENTS.md`, etc.), append to it\n- Otherwise, create `AGENTS.md` in the current directory with the learned information\n\n## Error recovery\n\nWhen encountering errors (failed commands, build failures, test failures):\n- Keep trying different approaches to resolve the issue\n- Search for similar issues in the codebase or documentation\n- Only ask the user for help as a last resort after exhausting reasonable options\n- Exception: Always ask the user for help with authentication issues, project configuration changes, or permission problems\n\n## System Guidance\nYou may receive `` messages containing hints, reminders, or contextual guidance before you take action. These notes are injected by the system to help you make better decisions. Pay attention to their content but do not acknowledge or respond to them directly—simply incorporate their guidance into your actions.\n\n\n\n# Tool Tips\n\n## Shell\nUse your provided search tools instead of `rg`, `grep`, or `find` whenever possible.\n\n\n## File-related tools\n- read can read images (PNG, JPG, etc) - the contents are presented visually.\n- For Jupyter notebooks (.ipynb files), use notebook_read instead of read.\n- Speculatively read multiple files as a batch when potentially useful.\n- Do NOT create documentation files to describe your changes or plan. Exception: persistent project info files like `AGENTS.md` are allowed.\n\n\n# Safety\n\nIMPORTANT: Assist with defensive security tasks only. Refuse to create, modify, or improve code that may be used maliciously. Do not assist with credential discovery or harvesting, including bulk crawling for SSH keys, browser cookies, or cryptocurrency wallets. Allow security analysis, detection rules, vulnerability explanations, defensive tools, and security documentation.\n\nIMPORTANT: You must NEVER generate or guess URLs for the user unless you are confident that the URLs are for helping the user with programming. You may use URLs provided by the user in their messages or local files.\n\n## Destructive Operations\n\nNEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command. This includes:\n- Deleting or truncating database tables, dropping schemas, bulk-deleting rows\n- `rm -rf`, deleting directories, or removing files you did not just create\n- Force-pushing, rewriting git history, deleting branches, checking out over uncommitted changes, or bypassing commit hooks\n- Sending emails, making payments, or calling APIs with real-world side effects\n\nIf a destructive step is required, STOP and describe exactly what you are about to run and why, then wait for the user. Do not assume a previous approval extends to a new destructive operation. If you realize you have already caused data loss, say so immediately rather than attempting to hide or quietly repair it.\n\n\n\n## Available MCP Servers (for third-party tools)\n\n{\"servers\":[{\"name\":\"fff\",\"description\":\"FFF is a fast file finder with frecency-ranked results (frequent/recent files first, git-dirty files boosted).\\n\\n## Which Tool Should I Use?\\n\\n- **grep**: DEFAULT tool. Searches file CONTENTS -- definitions, usage, patterns. Use when you have a specific name or pattern.\\n- **find_files**: Explores which files/modules exist for a topic. Use when you DON'T have a specific identifier or LOOKING FOR A FILE.\\n- **multi_grep**: OR logic across multiple patterns. Use for case variants (e.g. ['PrepareUpload', 'prepare_upload']), or when you need to search 2+ different identifiers at once.\\n\\n## Core Rules\\n\\n### 1. Search BARE IDENTIFIERS only\\nGrep matches single lines. Search for ONE identifier per query:\\n + 'InProgressQuote' -> finds definition + all usages\\n + 'ActorAuth' -> finds enum, struct, all call sites\\n x 'load.*metadata.*InProgressQuote' -> regex spanning multiple tokens, 0 results\\n x 'ctx.data::' -> code syntax, too specific, 0 results\\n x 'struct ActorAuth' -> adding keywords narrows results, misses enums/traits/type aliases\\n x 'TODO.*#\\\\d+' -> complex regex, use simple 'TODO' then filter visually\\n\\n### 2. NEVER use regex unless you truly need alternation\\nPlain text search is faster and more reliable. Regex patterns like `.*`, `\\\\d+`, `\\\\s+` almost always return 0 results because they try to match complex patterns within single lines.\\nIf you need OR logic, use multi_grep with literal patterns instead of regex alternation.\\n\\n### 3. Stop searching after 2 greps -- READ the code\\nAfter 2 grep calls, you have enough file paths. Read the top result to understand the code.\\nDo NOT keep grepping with variations. More greps != better understanding.\\n\\n### 4. Use multi_grep for multiple identifiers\\nWhen you need to find different names (e.g. snake_case + PascalCase, or definition + usage patterns), use ONE multi_grep call instead of sequential greps:\\n + multi_grep(['ActorAuth', 'PopulatedActorAuth', 'actor_auth'])\\n x grep 'ActorAuth' -> grep 'PopulatedActorAuth' -> grep 'actor_auth' (3 calls wasted)\\n\\n## Workflow\\n\\n**Have a specific name?** -> grep the bare identifier.\\n**Need multiple name variants?** -> multi_grep with all variants in one call.\\n**Exploring a topic / finding files?** -> find_files.\\n**Got results?** -> Read the top file. Don't grep again.\\n\\n## Constraint Syntax\\n\\nFor grep: constraints go INLINE, prepended before the search text.\\nFor multi_grep: constraints go in the separate 'constraints' parameter.\\n\\nConstraints MUST match one of these formats:\\n Extension: '*.rs', '*.{ts,tsx}'\\n Directory: 'src/', 'quotes/'\\n Filename: 'schema.rs', 'src/main.rs'\\n Exclude: '!test/', '!*.spec.ts'\\n\\n! Bare words without extensions are NOT constraints. 'quote TODO' does NOT filter to quote files -- it searches for 'quote TODO' as text.\\n + 'schema.rs TODO' -> searches for 'TODO' in files schema.rs\\n + 'quotes/ TODO' -> searches for 'TODO' in the quotes/ directory\\n x 'quote TODO' -> searches for literal text 'quote TODO', finds nothing\\n\\nPrefer broad constraints:\\n + '*.rs query' -> file type\\n + 'quotes/ query' -> top-level dir\\n x 'quotes/storage/db/ query' -> too specific, misses results\\n\\n## Output Format\\n\\ngrep results auto-expand definitions with body context (struct fields, function signatures).\\nThis often provides enough information WITHOUT a follow-up Read call.\\nLines marked with | are definition body context. [def] marks definition files.\\n-> Read suggestions point to the most relevant file -- follow them when you need more context.\\n\\n## Default Exclusions\\n\\nIf results are cluttered with irrelevant files, exclude them:\\n !tests/ - exclude tests directory\\n !*.spec.ts - exclude test files\\n !generated/ - exclude generated code\"},{\"name\":\"playwright\"}]}\n\nIMPORTANT: You MUST call `mcp_list_tools` for a server before calling `mcp_call_tool` on it. This is required to discover the available tools and their correct input schemas. Never guess tool names or arguments — always list tools first.\n","created_at":"2026-06-23T05:56:04.999125Z","finish_reason":null,"metrics":null},{"message_id":"e98ec5b3-4d35-4c63-b686-86c39ec1c14f","node_id":17,"role":"system","content":"Available subagent profiles for the `run_subagent` tool. Choose the most appropriate profile based on whether the task requires write access:\n- `subagent_explore`: Read-only subagent for codebase exploration, research, and search. Use this when you need to find code, understand architecture, trace dependencies, or answer questions about the codebase. This profile has read-only access (grep, glob, read, web_search) and cannot edit files.\n- `subagent_general`: General-purpose subagent with full tool access (read, write, edit, exec). Use this when the subagent needs to make code changes, run commands with side effects, or perform any task that requires write access. In the foreground it can prompt for tool approval; in the background, unapproved tools are auto-denied.","created_at":"2026-06-23T05:56:04.999470Z","finish_reason":null,"metrics":null},{"message_id":"795b9d3e-f685-4a7d-8973-02e35aaba43e","node_id":18,"role":"system","content":"You are powered by GLM-5.2.","created_at":"2026-06-23T05:56:04.999440Z","finish_reason":null,"metrics":null},{"message_id":"7976ae83-ec33-40af-a911-7d0dffc22447","node_id":19,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1/Downloads (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Monday, 2026-06-22\n\n","created_at":"2026-06-23T05:56:04.507294Z","finish_reason":null,"metrics":null},{"message_id":"d723aa15-4a2f-4a57-91ea-041ef764acf8","node_id":20,"role":"system","content":"\n\n\n\n","created_at":"2026-06-23T05:56:04.999981Z","finish_reason":null,"metrics":null},{"message_id":"d8bfceca-0228-4bed-93fc-06e09ecb18d7","node_id":21,"role":"user","content":"what current codex chats do i have running\n>","created_at":"2026-06-23T05:56:15.107022Z","finish_reason":null,"metrics":null},{"message_id":"8a5b0d24-a030-496f-ae9c-c957019b22ee","node_id":22,"role":"system","content":"\nThe following skills can be invoked using the `skill` tool. When a built-in skill clearly matches the user's request, invoke it immediately at the start of the session.\n\n- **devin-for-terminal**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/2026.7.23/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n","created_at":"2026-06-23T05:56:15.107714Z","finish_reason":null,"metrics":null},{"message_id":"fe40597d-51b1-4046-831c-c0b2233c0731","node_id":24,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b1989405657d4f64","name":"exec","arguments":{"command":"ps aux | grep -i codex | grep -v grep"}}],"thinking":"The user is asking about \"current codex chats\" they have running. This is ambiguous - \"codex\" could refer to OpenAI Codex CLI, or something else. Let me check what's running.","created_at":"2026-06-23T05:56:22.736664Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1764,"total_time_ms":7609,"input_tokens":6081,"output_tokens":66,"cache_read_tokens":8903,"cache_creation_tokens":null,"tpot_ms":88.56060606060606,"tokens_per_sec":11.291702309666382}},{"message_id":"f292d0b0-2c62-4e86-b4e5-1b372de3bda2","node_id":25,"role":"tool","content":"Output from command in shell 2e377a:\nroot1 18906 8.5 0.7 435878048 178336 s008 S+ 10:33PM 1:07.52 /opt/homebrew/lib/node_modules/@openai/codex/node_modules/@openai/codex-darwin-arm64/vendor/aarch64-apple-darwin/bin/codex --search --yolo\nroot1 31212 0.0 0.5 1892898832 121920 ?? S Sun07PM 1:20.68 /Applications/Cursor.app/Contents/MacOS/Cursor --goto /Users/root1/Documents/Codex/2026-06-21/ope/outputs/buffett_vs_sdan_sophomore_investor_note.md\nroot1 7529 0.0 0.0 435345664 4368 ?? S Sun06PM 0:00.00 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 7487 0.0 0.0 435346528 4368 ?? S Sun06PM 0:00.01 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 7384 0.0 0.2 435728976 49312 ?? S Sun06PM 0:09.40 /Users/root1/.codex/computer-use/Codex Computer Use.app/Contents/MacOS/SkyComputerUseService\nroot1 7340 0.0 1.1 1949377040 270432 ?? S Sun06PM 2:23.23 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Renderer).app/Contents/MacOS/Codex (Renderer) --type=renderer --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --lang=en-US --num-raster-threads=4 --enable-zero-copy --enable-gpu-memory-buffer-compositor-resources --enable-main-frame-before-activation --renderer-client-id=5 --time-ticks-at-unix-epoch=-1782089714172465 --launch-time-ticks=701611563 --shared-files --metrics-shmem-handle=1752395122,r,6498721906223495930,10134383406922357998,2097152 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708990997080739 --seatbelt-client=115\nroot1 7339 0.0 0.2 1949281632 47008 ?? S Sun06PM 0:01.29 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Renderer).app/Contents/MacOS/Codex (Renderer) --type=renderer --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --lang=en-US --num-raster-threads=4 --enable-zero-copy --enable-gpu-memory-buffer-compositor-resources --enable-main-frame-before-activation --renderer-client-id=6 --time-ticks-at-unix-epoch=-1782089714172465 --launch-time-ticks=701580623 --shared-files --metrics-shmem-handle=1752395122,r,15948780353101668027,14204077980647375359,2097152 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708991934122588 --seatbelt-client=115\nroot1 7319 0.0 0.2 435932336 61296 ?? S Sun06PM 1:16.18 /Applications/Codex.app/Contents/Resources/codex app-server --analytics-default-enabled\nroot1 7317 0.0 0.1 435469456 25168 ?? S Sun06PM 0:07.77 /Applications/Codex.app/Contents/Resources/native/bare-modifier-monitor --key DoubleCommand --immediate\nroot1 7302 0.0 0.2 486114320 50656 ?? S Sun06PM 0:04.88 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Service).app/Contents/MacOS/Codex (Service) --type=utility --utility-sub-type=storage.mojom.StorageService --lang=en-US --service-sandbox-type=service --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --shared-files --metrics-shmem-handle=1752395122,r,11960044405131384058,15290022911743541725,524288 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708990060038890 --seatbelt-client=49\nroot1 7301 0.0 0.3 486138912 72224 ?? S Sun06PM 0:25.87 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Service).app/Contents/MacOS/Codex (Service) --type=utility --utility-sub-type=network.mojom.NetworkService --lang=en-US --service-sandbox-type=network --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --start-stack-profiler --shared-files --metrics-shmem-handle=1752395122,r,6216236082941065097,17678615933841835920,524288 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708989122997041 --seatbelt-client=43\nroot1 7300 0.0 0.3 486509024 63008 ?? S Sun06PM 0:41.62 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Service).app/Contents/MacOS/Codex (Service) --type=gpu-process --user-data-dir=/Users/root1/Library/Application Support/Codex --start-stack-profiler --gpu-preferences=SAAAAAAAAAAgAQAEAAAAAAAAAAAAAGAAAwAAAAAAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAQAAAAAAAAABAAAAAAAAAACAAAAAAAAAAIAAAAAAAAAA== --shared-files --metrics-shmem-handle=1752395122,r,4489318285131297286,3857477325938040057,262144 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708988185955192 --seatbelt-client=43\nroot1 7292 0.0 0.0 485632384 7008 ?? S Sun06PM 0:00.00 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/browser_crashpad_handler --no-periodic-tasks --monitor-self-annotation=ptype=crashpad-handler --database=/Users/root1/Library/Application Support/Codex/Crashpad --annotation=plat=OS X --annotation=prod=Codex_Mac --annotation=ver=149.0.7827.115 --handshake-fd=4\nroot1 7290 0.0 0.0 485633536 7312 ?? S Sun06PM 0:00.01 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/browser_crashpad_handler --monitor-self --monitor-self-annotation=ptype=crashpad-handler --database=/Users/root1/Library/Application Support/Codex/Crashpad --annotation=plat=OS X --annotation=prod=Codex_Mac --annotation=ver=149.0.7827.115 --handshake-fd=5\nroot1 7286 0.0 0.8 1953836208 199440 ?? S Sun06PM 2:41.22 /Applications/Codex.app/Contents/MacOS/Codex\nroot1 1263 0.0 0.0 435311872 5008 ?? S Sun05PM 0:00.16 /Users/root1/.codex/plugins/cache/openai-bundled/chrome/latest/extension-host/macos/arm64/extension-host chrome-extension://hehggadaopoacecdllhhajmbjkdcmajg/\nroot1 19002 0.0 0.0 435345968 8944 s008 S 10:33PM 0:00.00 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 18905 0.0 0.1 436122928 36048 s008 S+ 10:33PM 0:00.09 node /opt/homebrew/bin/codex --search --yolo\nroot1 80693 0.0 0.1 435370720 26768 ?? S 7:10PM 0:00.81 /Users/root1/Library/Caches/com.openai.codex/org.sparkle-project.Sparkle/Launcher/CbP7B2xSI/Updater.app/Contents/MacOS/Updater /Applications/Codex.app 0\nroot1 80692 0.0 0.1 435377664 14560 ?? S 7:10PM 0:05.15 /Applications/Codex.app/Contents/Frameworks/Sparkle.framework/Versions/B/Autoupdate com.openai.codex /Users/root1 root1\n\n\nExit code: 0","created_at":"2026-06-23T05:56:22.848595Z","finish_reason":null,"metrics":null},{"message_id":"c7d7ae47-8138-48ac-ac79-a558cbc14ea9","node_id":27,"role":"assistant","content":"I see Codex CLI is running (PID 18906, started 10:33PM with `--search --yolo`). Let me check the active sessions.","tool_calls":[{"id":"chatcmpl-tool-82de459452cdd620","name":"exec","arguments":{"command":"ls -lat ~/.codex/sessions/ 2>/dev/null | head -20; echo \"---\"; ls -lat ~/.codex/ 2>/dev/null | head -20"}}],"thinking":"The user is asking about current Codex chats running. I see Codex CLI is running with `--search --yolo` flags. Let me look at the Codex CLI sessions. The CLI stores sessions in `~/.codex/sessions/` typically.","created_at":"2026-06-23T05:56:27.676878Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1514,"total_time_ms":4827,"input_tokens":3367,"output_tokens":136,"cache_read_tokens":14983,"cache_creation_tokens":null,"tpot_ms":24.360294117647058,"tokens_per_sec":41.05040748566254}},{"message_id":"7bf00aaa-2535-477b-a628-f82d225b8058","node_id":28,"role":"tool","content":"Output from command in shell 04e1be:\ntotal 0\ndrwxr-xr-x 38 root1 staff 1216 Jun 22 22:52 ..\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 2026\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 .\n---\ntotal 47392\n-rw-r--r-- 1 root1 staff 4161232 Jun 22 22:56 state_5.sqlite-wal\n-rw-r--r-- 1 root1 staff 502672 Jun 22 22:56 goals_1.sqlite-wal\n-rw-r--r-- 1 root1 staff 4808072 Jun 22 22:56 logs_2.sqlite-wal\n-rw-r--r-- 1 root1 staff 147165 Jun 22 22:56 models_cache.json\n-rw-r--r-- 1 root1 staff 12947456 Jun 22 22:56 logs_2.sqlite\n-rw-r--r-- 1 root1 staff 180224 Jun 22 22:56 state_5.sqlite\ndrwxr-x---+ 264 root1 staff 8448 Jun 22 22:56 ..\ndrwxr-xr-x 38 root1 staff 1216 Jun 22 22:52 .\n-rw-r--r-- 1 root1 staff 3846 Jun 22 22:52 .codex-global-state.json.bak\n-rw-r--r-- 1 root1 staff 3846 Jun 22 22:52 .codex-global-state.json\n-rw-r--r-- 1 root1 staff 40960 Jun 22 22:43 memories_1.sqlite\n-rw------- 1 root1 staff 554 Jun 22 22:37 history.jsonl\n-rw------- 1 root1 staff 2811 Jun 22 22:34 config.toml\ndrwxr-xr-x 3 root1 staff 96 Jun 22 22:33 attachments\ndrwxr-xr-x 4 root1 staff 128 Jun 22 22:33 shell_snapshots\ndrwxr-xr-x 7 root1 staff 224 Jun 22 22:32 .tmp\n-rw-r--r-- 1 root1 staff 32768 Jun 22 22:32 goals_1.sqlite-shm\n-rw-r--r-- 1 root1 staff 32768 Jun 22 22:32 state_5.sqlite-shm\n-rw-r--r-- 1 root1 staff 24576 Jun 22 22:32 goals_1.sqlite\n\n\nExit code: 0","created_at":"2026-06-23T05:56:27.702302Z","finish_reason":null,"metrics":null},{"message_id":"d6fc2c05-e44d-43a8-9d44-914296f23eab","node_id":30,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-985c4c83ed517763","name":"exec","arguments":{"command":"find ~/.codex/sessions -type f -name \"*.jsonl\" -mtime -1 2>/dev/null | head -20; echo \"---\"; ls -lat ~/.codex/sessions/2026/ 2>/dev/null"}}],"created_at":"2026-06-23T05:56:30.295772Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2592,"input_tokens":801,"output_tokens":59,"cache_read_tokens":18349,"cache_creation_tokens":null,"tpot_ms":43.932203389830505,"tokens_per_sec":22.762345679012345}},{"message_id":"8dcf7bee-2210-407f-b543-c1e975d0e685","node_id":31,"role":"tool","content":"Output from command in shell cfcbb1:\n/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n---\ntotal 0\ndrwxr-xr-x 5 root1 staff 160 Jun 22 22:33 06\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 .\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 ..\n\n\nExit code: 0","created_at":"2026-06-23T05:56:30.334029Z","finish_reason":null,"metrics":null},{"message_id":"1fbd81d5-0c11-42db-94ff-af2bd05eb73e","node_id":33,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a20aeaed3ceeaf58","name":"exec","arguments":{"command":"ls -lat ~/.codex/sessions/2026/06/22/ 2>/dev/null; echo \"---all recent---\"; find ~/.codex/sessions -type f -name \"*.jsonl\" 2>/dev/null | xargs ls -lat 2>/dev/null | head -10"}}],"created_at":"2026-06-23T05:56:31.689792Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1354,"input_tokens":220,"output_tokens":73,"cache_read_tokens":19149,"cache_creation_tokens":null,"tpot_ms":18.54794520547945,"tokens_per_sec":53.91432791728213}},{"message_id":"38932dbf-cf7e-4e80-9133-67f598204222","node_id":34,"role":"tool","content":"Output from command in shell 45d889:\ntotal 1800\n-rw-r--r-- 1 root1 staff 910986 Jun 22 22:56 rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\ndrwxr-xr-x 3 root1 staff 96 Jun 22 22:33 .\ndrwxr-xr-x 5 root1 staff 160 Jun 22 22:33 ..\n---all recent---\n-rw-r--r-- 1 root1 staff 910986 Jun 22 22:56 /Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n-rw-r--r-- 1 root1 staff 945145 Jun 21 19:24 /Users/root1/.codex/sessions/2026/06/21/rollout-2026-06-21T17-26-12-019eecb8-74d0-7c83-b4f2-e90cadacf2a4.jsonl\n-rw-r--r-- 1 root1 staff 530676 Jun 19 23:01 /Users/root1/.codex/sessions/2026/06/19/rollout-2026-06-19T18-06-05-019ee290-42c6-7563-8b5b-627a9689a5d5.jsonl\n\n\nExit code: 0","created_at":"2026-06-23T05:56:31.728395Z","finish_reason":null,"metrics":null},{"message_id":"48c982b1-55dd-4f2e-a30f-073a7a553b54","node_id":36,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8003d3d9444248e7","name":"exec","arguments":{"command":"tail -c 50000 ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | grep -o '\"role\":\"user\"[^}]*\"content\":\"[^\"]\\{0,200\\}' | tail -10"}}],"thinking":"Let me check the active session - the one running right now (started 22:33). Let me peek at the latest user messages to identify the chat.","created_at":"2026-06-23T05:56:35.673054Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1375,"total_time_ms":3943,"input_tokens":484,"output_tokens":133,"cache_read_tokens":19368,"cache_creation_tokens":null,"tpot_ms":19.30827067669173,"tokens_per_sec":51.79127725856698}},{"message_id":"be988430-2583-48fd-af92-f03b356bba77","node_id":37,"role":"tool","content":"Output from command in shell 1c6568:\n\n\nExit code: 1","created_at":"2026-06-23T05:56:35.707299Z","finish_reason":null,"metrics":null},{"message_id":"67ea51ce-3d6c-4c75-9a9e-4f0034588789","node_id":39,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-ab07129840517584","name":"exec","arguments":{"command":"head -c 2000 ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl"}}],"created_at":"2026-06-23T05:56:38.028764Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2319,"input_tokens":155,"output_tokens":73,"cache_read_tokens":19851,"cache_creation_tokens":null,"tpot_ms":31.767123287671232,"tokens_per_sec":31.479085812850368}},{"message_id":"54ddca1d-0cdf-4b5d-aa78-e04287ecad85","node_id":40,"role":"tool","content":"Output from command in shell b21cce:\n{\"timestamp\":\"2026-06-23T05:33:29.980Z\",\"type\":\"session_meta\",\"payload\":{\"session_id\":\"019ef2f8-1156-71e1-8739-3a250421f23e\",\"id\":\"019ef2f8-1156-71e1-8739-3a250421f23e\",\"timestamp\":\"2026-06-23T05:33:24.182Z\",\"cwd\":\"/Users/root1/Downloads/projects/glm-5.2-inference\",\"originator\":\"codex-tui\",\"cli_version\":\"0.142.0\",\"source\":\"cli\",\"thread_source\":\"user\",\"model_provider\":\"openai\",\"base_instructions\":{\"text\":\"You are Codex, a coding agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.\\n\\n# Personality\\n\\nYou are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.\\n\\n## Values\\nYou are guided by these core values:\\n- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.\\n- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.\\n- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.\\n\\n## Interaction Style\\nYou communicate respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps.\\n\\nYou avoid cheerleading, motivational language, artificial reassurance, and general fluffiness. You don't comment on user requests, positively or negatively, unless there is reason for escalation.\\n\\n## Escalation\\nYou may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the app\n\nExit code: 0","created_at":"2026-06-23T05:56:38.047848Z","finish_reason":null,"metrics":null},{"message_id":"a380aebe-7dfe-4bd1-9ddd-bb50a5da8f83","node_id":42,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a20ded6ad34ee5cf","name":"exec","arguments":{"command":"grep -o '\"type\":\"user_message\",\"payload\":{[^}]*\"text\":\"[^\"]\\{5,200\\}' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | head -10"}}],"created_at":"2026-06-23T05:56:41.358207Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3308,"input_tokens":563,"output_tokens":98,"cache_read_tokens":20005,"cache_creation_tokens":null,"tpot_ms":33.755102040816325,"tokens_per_sec":29.62515114873035}},{"message_id":"965235ec-ee77-4c4a-8898-2024786928e5","node_id":43,"role":"tool","content":"Output from command in shell 3dbcaa:\n\n\nExit code: 1","created_at":"2026-06-23T05:56:41.398561Z","finish_reason":null,"metrics":null},{"message_id":"4199e418-d312-4656-8935-c7e0574c9b30","node_id":45,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9e7507265fbed365","name":"exec","arguments":{"command":"grep -c '\"type\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl; echo \"---types---\"; grep -o '\"type\":\"[a-z_]*\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | sort | uniq -c"}}],"created_at":"2026-06-23T05:56:44.209461Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2809,"input_tokens":118,"output_tokens":154,"cache_read_tokens":20567,"cache_creation_tokens":null,"tpot_ms":18.24025974025974,"tokens_per_sec":54.82378070487718}},{"message_id":"f00d72b4-0536-4895-a597-1cbdef9e7b89","node_id":46,"role":"tool","content":"Output from command in shell bcfc28:\n719\n---types---\n 14 \"type\":\"add\"\n 84 \"type\":\"agent_message\"\n 27 \"type\":\"custom_tool_call_output\"\n 27 \"type\":\"custom_tool_call\"\n 2 \"type\":\"delete\"\n 5 \"type\":\"disabled\"\n 222 \"type\":\"event_msg\"\n 132 \"type\":\"function_call_output\"\n 134 \"type\":\"function_call\"\n 11 \"type\":\"input_text\"\n 92 \"type\":\"message\"\n 4 \"type\":\"open_page\"\n 84 \"type\":\"output_text\"\n 27 \"type\":\"patch_apply_end\"\n 71 \"type\":\"reasoning\"\n 491 \"type\":\"response_item\"\n 12 \"type\":\"search\"\n 1 \"type\":\"session_meta\"\n 4 \"type\":\"task_complete\"\n 5 \"type\":\"task_started\"\n 1 \"type\":\"thread_goal_updated\"\n 93 \"type\":\"token_count\"\n 5 \"type\":\"turn_context\"\n 26 \"type\":\"update\"\n 8 \"type\":\"web_search_call\"\n 8 \"type\":\"web_search_end\"\n\n\nExit code: 0","created_at":"2026-06-23T05:56:44.260728Z","finish_reason":null,"metrics":null},{"message_id":"8dc8231d-bbeb-47e0-880c-60ce381e4723","node_id":48,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b73e66c16b149b33","name":"exec","arguments":{"command":"grep '\"type\":\"input_text\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | python3 -c \"import sys,json\nfor line in sys.stdin:\n d=json.loads(line)\n p=d.get('payload',{})\n t=p.get('text','')[:200].replace('\\n',' ')\n print(repr(t))\""}}],"created_at":"2026-06-23T05:56:46.836449Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2574,"input_tokens":412,"output_tokens":127,"cache_read_tokens":20684,"cache_creation_tokens":null,"tpot_ms":20.26771653543307,"tokens_per_sec":49.339549339549336}},{"message_id":"7a357275-0ceb-49fa-8837-81bf1456e7bd","node_id":49,"role":"tool","content":"Output from command in shell 94e4a2:\n''\n''\n''\n''\n''\n''\n''\n''\n\n\nExit code: 0","created_at":"2026-06-23T05:56:46.899632Z","finish_reason":null,"metrics":null},{"message_id":"746ff9ae-93de-4072-89ff-2b19d577b57f","node_id":51,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-97ce4d0340ff9e8a","name":"exec","arguments":{"command":"grep '\"type\":\"input_text\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | head -2 | python3 -c \"import sys,json\nfor line in sys.stdin:\n d=json.loads(line)\n print(json.dumps(d, indent=1)[:800])\""}}],"created_at":"2026-06-23T05:56:49.391839Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":2491,"input_tokens":158,"output_tokens":113,"cache_read_tokens":21095,"cache_creation_tokens":null,"tpot_ms":22.04424778761062,"tokens_per_sec":45.36330790847049}},{"message_id":"58ac364d-d7be-4f5d-bcca-deaf95b591c2","node_id":52,"role":"tool","content":"Output from command in shell e3f2ab:\n{\n \"timestamp\": \"2026-06-23T05:33:30.002Z\",\n \"type\": \"response_item\",\n \"payload\": {\n \"type\": \"message\",\n \"role\": \"developer\",\n \"content\": [\n {\n \"type\": \"input_text\",\n \"text\": \"\\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\\n\"\n },\n {\n \"type\": \"input_text\",\n \"text\": \"\\n## Apps (Connectors)\\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered a\n{\n \"timestamp\": \"2026-06-23T05:33:30.002Z\",\n \"type\": \"response_item\",\n \"payload\": {\n \"type\": \"message\",\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_text\",\n \"text\": \"\\n /Users/root1/Downloads/projects/glm-5.2-inference\\n zsh\\n 2026-06-22\\n America/Los_Angeles\\n /Users/root1/Downloads/projects/glm-5.2-inference\\n\"\n }\n ]\n }\n}\n\n\nExit code: 0","created_at":"2026-06-23T05:56:49.446900Z","finish_reason":null,"metrics":null},{"message_id":"3ae10b5d-9228-4441-b58c-7b638eed6735","node_id":54,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8f487a63747247a2","name":"exec","arguments":{"command":"grep '\"type\":\"response_item\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | python3 -c \"\nimport sys,json\nfor line in sys.stdin:\n d=json.loads(line)\n p=d.get('payload',{})\n if p.get('role')=='user':\n for c in p.get('content',[]):\n t=c.get('text','')\n if not t.startswith('<') and 'environment_context' not in t:\n print(repr(t[:250].replace(chr(10),' ')))\n\""}}],"created_at":"2026-06-23T05:56:54.701830Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":5254,"input_tokens":527,"output_tokens":165,"cache_read_tokens":21252,"cache_creation_tokens":null,"tpot_ms":31.842424242424244,"tokens_per_sec":31.40464408070042}},{"message_id":"9b966823-2cb3-457f-a6af-52f7a09f3064","node_id":55,"role":"tool","content":"Output from command in shell abb6bd:\n\n\nExit code: 0","created_at":"2026-06-23T05:56:54.769817Z","finish_reason":null,"metrics":null},{"message_id":"3ad43f5e-6085-4103-b4b5-e4b5834fb83d","node_id":57,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-ba8881331589f8be","name":"exec","arguments":{"command":"grep '\"type\":\"response_item\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | python3 -c \"\nimport sys,json\nseen=0\nfor line in sys.stdin:\n d=json.loads(line)\n p=d.get('payload',{})\n if p.get('role')=='user':\n for c in p.get('content',[]):\n t=c.get('text','')\n if t and ' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\nTOTAL USER MSGS: 5\n\n\nExit code: 0","created_at":"2026-06-23T05:57:09.569065Z","finish_reason":null,"metrics":null},{"message_id":"0131c10b-5713-4635-a1db-72e7d704f992","node_id":60,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9349848ad2faf076","name":"exec","arguments":{"command":"cat /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/* 2>/dev/null | head -50; echo \"---ls---\"; ls /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/ 2>/dev/null"}}],"created_at":"2026-06-23T05:57:15.045103Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":5475,"input_tokens":656,"output_tokens":104,"cache_read_tokens":21961,"cache_creation_tokens":null,"tpot_ms":52.64423076923077,"tokens_per_sec":18.995433789954337}},{"message_id":"f35ad771-2aa9-4996-b0f1-4200befbd58e","node_id":61,"role":"tool","content":"Output from command in shell b04fa7:\n# GLM-5.2 NVFP4 Inference on Modal 8×B200\n\nServe `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM,\nOpenAI-compatible, with a correctness gate against the BF16 reference and a\nthroughput/latency benchmark. The verifier sends hidden prompts to your\n`/app/submission/launch_server.sh` and compares against a well-tuned\nvLLM NVFP4 baseline.\n\n## Model\n\n- **`zai-org/GLM-5.2`** — 753B-param Mixture-of-Experts (40B active), 256 routed\n experts + 1 shared (8 activated/token), 78 transformer layers + 1 MTP layer\n at index 78, DeepSeek-style MLA attention + DSA sparse-attention indexer\n (`glm_moe_dsa` architecture), hidden 6144, vocab 154880.\n- BF16 checkpoint ~1.37 TB; NVFP4 quantized ~459 GB (~3× smaller).\n- **Quantization: NVFP4** — E2M1 4-bit weights + FP8 E4M3 block scales,\n 16-element blocks, produced with NVIDIA Model Optimizer (`modelopt`).\n Only the MoE expert FFNs (routed + shared) are quantized. Attention\n (MLA projections q/kv + DSA indexer), the MoE router (`mlp.gate`), and\n `lm_head` are kept in BF16 — SGLang/vLLM's `deepseek_v2` MLA path cannot\n consume NVFP4 attention weights.\n- **KV cache: FP8** (`--kv-cache-dtype fp8`) to maximize context headroom.\n- License: MIT (pure open).\n- Use a community NVFP4 checkpoint: `Lorbus/GLM-5.2-NVFP4`,\n `Mapika/GLM-5.2-NVFP4`, or `lukealonso/GLM-5.2-NVFP4`. Pick one and pin\n the revision in `launch_server.sh`.\n\n## Hardware\n\n- **Modal 8×B200, single node** — `@app.function(gpu=\"B200:8\")`. 8× B200 on\n one physical machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are\n Blackwell-only. This is the entire GPU budget — no second node, no\n disaggregated prefill/decode pools. One vLLM engine, TP=8, collocated\n prefill+decode.\n- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n CUDA graphs, and runtime buffers. TP=8 is mandatory: TP=4 would split the\n node into two 4-GPU engines and leave only ~77 GB/GPU for KV — too tight for\n GLM-5.2's context window, and you cannot run two TP=8 engines on 8 GPUs.\n- **No disaggregation.** Disaggregated prefill/decode requires a second\n 8×B200 node (one TP=8 prefill + one TP=8 decode with NIXL/Mooncake KV\n transfer). On a single node, use **chunked prefill**\n (`--enable-chunked-prefill` with a tuned `--max-num-batched-tokens` chunk\n size) to get the ITL-stability benefit of disaggregation without splitting\n the GPU pool.\n\n## Tools / stack\n\n- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support\n and the `glm_moe_dsa` model registration. Pin a known-good version\n (e.g. `vllm==0.23.0` or newer) in the Modal image.\n---ls---\npasted-text-1.txt\n\n\nExit code: 0","created_at":"2026-06-23T05:57:15.057180Z","finish_reason":null,"metrics":null},{"message_id":"9de24db3-41ae-4d35-9e2d-35e9a45db1fb","node_id":63,"role":"assistant","content":"Let me also check the Codex desktop app's recent threads.","tool_calls":[{"id":"chatcmpl-tool-97033da1e5b61e62","name":"exec","arguments":{"command":"sqlite3 ~/.codex/state_5.sqlite \".tables\" 2>&1 | head; echo \"---\"; sqlite3 ~/.codex/goals_1.sqlite \".tables\" 2>&1 | head"}}],"thinking":"So there's one active Codex CLI chat running. Let me also check the Codex.app (desktop app) - it's also running. The user asked about \"current codex chats\" plural. Let me check the desktop app's state too. But actually, the desktop Codex.app is the OpenAI Codex app, which has its own chats. Let me check the SQLite state file.\n\nActually, let me focus on what's clearly running. The CLI has one active session. Let me also check if there are other recent CLI sessions running concurrently. The ps output showed only one `codex --search --yolo` process.\n\nLet me also check the desktop app's recent threads via the state DB.","created_at":"2026-06-23T05:57:21.116341Z","finish_reason":"tool_calls","metrics":{"ttft_ms":972,"total_time_ms":6058,"input_tokens":990,"output_tokens":209,"cache_read_tokens":22616,"cache_creation_tokens":null,"tpot_ms":24.33492822966507,"tokens_per_sec":41.09319701140385}},{"message_id":"04c8a27f-e448-4c10-9360-f05f72485668","node_id":64,"role":"tool","content":"Output from command in shell b048f1:\n_sqlx_migrations remote_control_enrollments \nagent_job_items thread_dynamic_tools \nagent_jobs thread_spawn_edges \nbackfill_state threads \nexternal_agent_config_imports\n---\n_sqlx_migrations thread_goals \n\n\nExit code: 0","created_at":"2026-06-23T05:57:21.168793Z","finish_reason":null,"metrics":null},{"message_id":"087dcb68-91e1-4a93-bf49-05bddb9932d7","node_id":66,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b791604f43e1d824","name":"exec","arguments":{"command":"sqlite3 ~/.codex/state_5.sqlite \"SELECT name FROM sqlite_master WHERE type='table';\" 2>&1; echo \"---threads schema---\"; sqlite3 ~/.codex/state_5.sqlite \".schema threads\" 2>&1 | head -40"}}],"created_at":"2026-06-23T05:57:24.500317Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3330,"input_tokens":277,"output_tokens":63,"cache_read_tokens":23605,"cache_creation_tokens":null,"tpot_ms":52.857142857142854,"tokens_per_sec":18.91891891891892}},{"message_id":"5b1181bc-0ccb-4e38-a9a8-b47a9261aab3","node_id":67,"role":"tool","content":"Output from command in shell 281960:\n_sqlx_migrations\nthreads\nsqlite_sequence\nthread_dynamic_tools\nbackfill_state\nagent_jobs\nagent_job_items\nthread_spawn_edges\nremote_control_enrollments\nexternal_agent_config_imports\n---threads schema---\nCREATE TABLE threads (\n id TEXT PRIMARY KEY,\n rollout_path TEXT NOT NULL,\n created_at INTEGER NOT NULL,\n updated_at INTEGER NOT NULL,\n source TEXT NOT NULL,\n model_provider TEXT NOT NULL,\n cwd TEXT NOT NULL,\n title TEXT NOT NULL,\n sandbox_policy TEXT NOT NULL,\n approval_mode TEXT NOT NULL,\n tokens_used INTEGER NOT NULL DEFAULT 0,\n has_user_event INTEGER NOT NULL DEFAULT 0,\n archived INTEGER NOT NULL DEFAULT 0,\n archived_at INTEGER,\n git_sha TEXT,\n git_branch TEXT,\n git_origin_url TEXT\n, cli_version TEXT NOT NULL DEFAULT '', first_user_message TEXT NOT NULL DEFAULT '', agent_nickname TEXT, agent_role TEXT, memory_mode TEXT NOT NULL DEFAULT 'enabled', model TEXT, reasoning_effort TEXT, agent_path TEXT, created_at_ms INTEGER, updated_at_ms INTEGER, thread_source TEXT, preview TEXT NOT NULL DEFAULT '', recency_at INTEGER NOT NULL DEFAULT 0, recency_at_ms INTEGER NOT NULL DEFAULT 0);\nCREATE INDEX idx_threads_created_at ON threads(created_at DESC, id DESC);\nCREATE INDEX idx_threads_updated_at ON threads(updated_at DESC, id DESC);\nCREATE INDEX idx_threads_archived ON threads(archived);\nCREATE INDEX idx_threads_source ON threads(source);\nCREATE INDEX idx_threads_provider ON threads(model_provider);\nCREATE TRIGGER threads_created_at_ms_after_insert\nAFTER INSERT ON threads\nWHEN NEW.created_at_ms IS NULL\nBEGIN\n UPDATE threads\n SET created_at_ms = NEW.created_at * 1000\n WHERE id = NEW.id;\nEND;\nCREATE TRIGGER threads_updated_at_ms_after_insert\nAFTER INSERT ON threads\nWHEN NEW.updated_at_ms IS NULL\nBEGIN\n UPDATE threads\n SET updated_at_ms = NEW.updated_at * 1000\n WHERE id = NEW.id;\nEND;\n\n\nExit code: 0","created_at":"2026-06-23T05:57:24.514615Z","finish_reason":null,"metrics":null},{"message_id":"ed755473-24ee-4392-9b0b-0a6937b03971","node_id":69,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-933041a5977a941b","name":"exec","arguments":{"command":"sqlite3 ~/.codex/state_5.sqlite \"SELECT id, title, source, cwd, datetime(updated_at,'unixepoch','localtime') as updated, archived, has_user_event FROM threads ORDER BY updated_at DESC LIMIT 15;\" 2>&1"}}],"created_at":"2026-06-23T05:57:27.948611Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3433,"input_tokens":506,"output_tokens":62,"cache_read_tokens":23881,"cache_creation_tokens":null,"tpot_ms":55.37096774193548,"tokens_per_sec":18.060005825808332}},{"message_id":"d2732ffb-94ac-4eb1-990f-7094858ffa1b","node_id":70,"role":"tool","content":"Output from command in shell 50f208:\n019ef2f8-1156-71e1-8739-3a250421f23e||cli|/Users/root1/Downloads/projects/glm-5.2-inference|2026-06-22 22:57:26|0|0\n019eecb8-74d0-7c83-b4f2-e90cadacf2a4|open a chrome browser with my profile, download all fo warrren buffets public writing, shareholder memos, lectures etc, if theres useful video on youtube, use youtubetotranscript.com to pull transcripts|vscode|/Users/root1/Documents/Codex/2026-06-21/ope|2026-06-21 19:24:21|0|0\n019ee290-42c6-7563-8b5b-627a9689a5d5|Open Chrome window|vscode|/Users/root1/Documents/Codex/2026-06-19/open-a-new-chrome-window-go|2026-06-19 23:01:28|0|0\n\n\nExit code: 0","created_at":"2026-06-23T05:57:27.986620Z","finish_reason":null,"metrics":null},{"message_id":"45861aec-e89c-4139-8e05-0090d39b333a","node_id":72,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9b5c25b749a4f8b0","name":"exec","arguments":{"command":"sqlite3 ~/.codex/state_5.sqlite \".schema agent_jobs\" 2>&1 | head -30; echo \"---active jobs---\"; sqlite3 ~/.codex/state_5.sqlite \"SELECT * FROM agent_jobs LIMIT 5;\" 2>&1"}}],"thinking":"So there are 3 threads in the database, but only one CLI session is actively running right now (PID 18906). The other two are old VSCode-originated threads from prior days.\n\nLet me check if the desktop Codex.app has its own running threads. The desktop app stores data in `~/Library/Application Support/Codex`. But the user asked specifically about \"current codex chats running\" - meaning actively running. Only one is actively running.\n\nLet me also check if there are any active agent_jobs running.","created_at":"2026-06-23T05:57:33.088492Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1304,"total_time_ms":5100,"input_tokens":9950,"output_tokens":169,"cache_read_tokens":14765,"cache_creation_tokens":null,"tpot_ms":22.46153846153846,"tokens_per_sec":44.52054794520548}},{"message_id":"05130993-fe39-4d5a-a677-008e12e1fcf5","node_id":73,"role":"tool","content":"Output from command in shell 1ea34c:\nCREATE TABLE agent_jobs (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n status TEXT NOT NULL,\n instruction TEXT NOT NULL,\n output_schema_json TEXT,\n input_headers_json TEXT NOT NULL,\n input_csv_path TEXT NOT NULL,\n output_csv_path TEXT NOT NULL,\n auto_export INTEGER NOT NULL DEFAULT 1,\n created_at INTEGER NOT NULL,\n updated_at INTEGER NOT NULL,\n started_at INTEGER,\n completed_at INTEGER,\n last_error TEXT\n, max_runtime_seconds INTEGER);\nCREATE INDEX idx_agent_jobs_status ON agent_jobs(status, updated_at DESC);\n---active jobs---\n\n\nExit code: 0","created_at":"2026-06-23T05:57:33.131711Z","finish_reason":null,"metrics":null},{"message_id":"d7103b44-dca4-4649-99ad-c2cd8810817d","node_id":75,"role":"assistant","content":"Here's what's currently running on your machine:\n\n## Active Codex CLI session (1 running)\n\n**PID 18906** — `codex --search --yolo`, started 10:33 PM tonight\n- **Thread ID:** `019ef2f8-1156-71e1-8739-3a250421f23e`\n- **CWD:** `/Users/root1/Downloads/projects/glm-5.2-inference`\n- **Source:** `cli` (Codex TUI v0.142.0, GPT-5, `danger-full-access` sandbox)\n- **Task:** GLM-5.2 NVFP4 inference on Modal 8×B200 — serve `zai-org/GLM-5.2` quantized to NVFP4 via vLLM, OpenAI-compatible, with a correctness gate vs BF16 reference and a throughput/latency benchmark. The objective was pasted from `/Users/root1/.codex/attachments/ec1b915e-…/pasted-text-1.txt`.\n- Last DB update: 22:57:26 (still active, 719 events logged, 5 user turns so far).\n\n## Other recent Codex threads (not currently running)\n\n| Thread ID | Title | Source | Last update |\n|---|---|---|---|\n| `019eecb8…` | \"open a chrome browser with my profile, download all of Warren Buffetts public writing, shareholder memos, lectures…\" | vscode | 2026-06-21 19:24 |\n| `019ee290…` | \"Open Chrome window\" | vscode | 2026-06-19 23:01 |\n\n## Also running (background Codex.app processes, not chats)\n- `Codex.app` desktop app (PID 7286) and its helpers — the GUI is open but no active CLI chat is bound to it.\n- Cursor (PID 31212) has a tab open on `/Users/root1/Documents/Codex/2026-06-21/ope/outputs/buffett_vs_sdan_sophomore_investor_note.md` — that's an output file from the 06-21 Buffett thread, not a live chat.\n\nSo: **exactly one Codex chat is actively running right now** — the GLM-5.2 NVFP4/Modal task in the CLI. Want me to tail its live rollout to see what it's currently doing?","tool_calls":[],"created_at":"2026-06-23T05:57:51.891348Z","finish_reason":"stop","metrics":{"ttft_ms":1359,"total_time_ms":18758,"input_tokens":642,"output_tokens":527,"cache_read_tokens":24386,"cache_creation_tokens":null,"tpot_ms":33.01518026565465,"tokens_per_sec":30.289097074544514}},{"message_id":"910aad3a-2cb6-4afd-be2c-65cb2d594d01","node_id":76,"role":"user","content":"get all the messages from human and codex in there","created_at":"2026-06-23T05:58:19.907045Z","finish_reason":null,"metrics":null},{"message_id":"00592430-1ae4-437c-a049-ca437a0d2b1b","node_id":78,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-abff1f36ccff2ded","name":"exec","arguments":{"command":"grep -E '\"type\":\"(agent_message|message)\"' ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | wc -l"}}],"thinking":"The user wants all human and codex messages from the active session. Let me extract them.","created_at":"2026-06-23T05:58:26.501727Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1241,"total_time_ms":6593,"input_tokens":10203,"output_tokens":103,"cache_read_tokens":14984,"cache_creation_tokens":null,"tpot_ms":51.96116504854369,"tokens_per_sec":19.245142002989535}},{"message_id":"e221f3b9-5e12-4609-afe9-ab2d32545c9f","node_id":79,"role":"tool","content":"Output from command in shell 23c4d7:\n 187\n\n\nExit code: 0","created_at":"2026-06-23T05:58:26.571944Z","finish_reason":null,"metrics":null},{"message_id":"f27104dc-9bc5-4479-a7f0-b55618a12af7","node_id":81,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8f4661cbc1495849","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\nevents = []\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n ts = d.get(\"timestamp\", \"\")\n\n # user/developer messages\n if t == \"response_item\" and p.get(\"type\") == \"message\":\n role = p.get(\"role\")\n if role in (\"user\", \"developer\"):\n for c in p.get(\"content\", []):\n text = c.get(\"text\", \"\")\n if not text:\n continue\n # filter out internal context wrappers and env/permissions boilerplate\n if text.startswith(\" 2000:\n print(f\"... ({len(text)} chars total)\")\nEOF"}}],"created_at":"2026-06-23T05:58:36.848731Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":10270,"input_tokens":129,"output_tokens":450,"cache_read_tokens":25186,"cache_creation_tokens":null,"tpot_ms":22.822222222222223,"tokens_per_sec":43.816942551119766}},{"message_id":"b2a2a401-3323-4a9e-98ff-de6192f77813","node_id":82,"role":"tool","content":"Output from command in shell 3ef06c:\nTotal events: 3\n================================================================================\n\n[2026-06-23T05:33:30.002Z] DEV:\n\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /Users/root1/.codex/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains. (file: /Users/root1/.codex/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for pl\n... (10329 chars total)\n\n[2026-06-23T05:33:30.002Z] DEV:\n\n## Plugins\nA plugin is a local bundle of skills, MCP servers, and apps.\n### How to use plugins\n- Skill naming: If a plugin contributes skills, those skill entries are prefixed with `plugin_name:` in the Skills list.\n- MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as `mcp__server__tool`; use tool provenance to tell which plugin they come from.\n- Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.\n- Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.\n- Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn.\n- Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.\n\n\n[2026-06-23T05:37:55.017Z] DEV:\n# Collaboration Mode: Default\n\nYou are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.\n\nYour active mode changes only when new developer instructions with a different `...` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.\n\n## request_user_input availability\n\nUse the `request_user_input` tool only when it is listed in the available tools for this turn.\n\nIn Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.\n\n\n\nExit code: 0","created_at":"2026-06-23T05:58:36.894781Z","finish_reason":null,"metrics":null},{"message_id":"ed2e06de-5d18-4089-a448-50c29de42730","node_id":84,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-899406c76eee41f6","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\n# count types\ncounts = {}\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n counts[t] = counts.get(t, 0) + 1\n if t == \"agent_message\":\n p = d.get(\"payload\", {})\n print(\"AGENT_MSG keys:\", list(p.keys()) if isinstance(p, dict) else type(p))\n print(\"Sample:\", json.dumps(p)[:500])\n print(\"---\")\n if t == \"message\":\n p = d.get(\"payload\", {})\n print(\"MSG:\", json.dumps(p)[:400])\n print(\"---\")\nprint(\"Counts:\", counts)\nEOF"}}],"thinking":"Hmm, only 3 events and they're all DEV messages. The user messages are wrapped in `` and I filtered them out. Let me look at the actual user input - the goal/objective. And the agent_message events should be there. Let me re-check.","created_at":"2026-06-23T05:58:42.631925Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1235,"total_time_ms":5736,"input_tokens":1362,"output_tokens":293,"cache_read_tokens":25314,"cache_creation_tokens":null,"tpot_ms":15.361774744027304,"tokens_per_sec":65.09664518995778}},{"message_id":"49c784d1-7ee0-4434-9156-417b8681f2a4","node_id":85,"role":"tool","content":"Output from command in shell e3056b:\nCounts: {'session_meta': 1, 'event_msg': 242, 'response_item': 534, 'turn_context': 6}\n\n\nExit code: 0","created_at":"2026-06-23T05:58:42.668412Z","finish_reason":null,"metrics":null},{"message_id":"f3785f3f-8f29-424b-bbe0-9f0735cb64ab","node_id":87,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-bd043a3e5e8fbffd","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\n# payload type counts\ncounts = {}\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n pt = p.get(\"type\") if isinstance(p, dict) else None\n key = f\"{t}/{pt}\"\n counts[key] = counts.get(key, 0) + 1\n\nfor k, v in sorted(counts.items(), key=lambda x: -x[1]):\n print(f\"{v:5d} {k}\")\nEOF"}}],"thinking":"Wait, my earlier counts were different. Let me re-look. Earlier I saw 84 agent_message and 92 message. But now I see only event_msg and response_item. Let me re-check — maybe the file changed (it's actively being written to).\n\nActually the earlier counts were on `type` field. Let me look at the payload's `type` field which is what's inside.","created_at":"2026-06-23T05:58:52.223047Z","finish_reason":"tool_calls","metrics":{"ttft_ms":875,"total_time_ms":9553,"input_tokens":345,"output_tokens":294,"cache_read_tokens":26675,"cache_creation_tokens":null,"tpot_ms":29.517006802721088,"tokens_per_sec":33.87877391103941}},{"message_id":"10cac44e-daea-487f-9118-ac9590dfb5eb","node_id":88,"role":"tool","content":"Output from command in shell 56c115:\n 147 response_item/function_call\n 147 response_item/function_call_output\n 102 event_msg/token_count\n 101 response_item/message\n 92 event_msg/agent_message\n 75 response_item/reasoning\n 28 response_item/custom_tool_call\n 28 event_msg/patch_apply_end\n 28 response_item/custom_tool_call_output\n 8 event_msg/web_search_end\n 8 response_item/web_search_call\n 6 event_msg/task_started\n 6 turn_context/None\n 5 event_msg/task_complete\n 1 session_meta/None\n 1 event_msg/thread_goal_updated\n\n\nExit code: 0","created_at":"2026-06-23T05:58:52.266862Z","finish_reason":null,"metrics":null},{"message_id":"165171e9-a00c-4b3c-93e0-27efb7b03a33","node_id":90,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-85031fc7381e1678","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\nevents = []\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n ts = d.get(\"timestamp\", \"\")\n pt = p.get(\"type\") if isinstance(p, dict) else None\n\n # user/developer messages (the actual conversation)\n if t == \"response_item\" and pt == \"message\":\n role = p.get(\"role\")\n if role in (\"user\", \"developer\"):\n for c in p.get(\"content\", []):\n text = c.get(\"text\", \"\")\n if not text:\n continue\n # filter out boilerplate wrappers\n if text.startswith(\"(.*?)\", text, re.DOTALL)\n if m:\n text = m.group(1).strip()\n else:\n continue\n events.append((ts, \"USER\", text))\n\n # agent messages - the codex replies\n elif t == \"event_msg\" and pt == \"agent_message\":\n text = p.get(\"text\", \"\")\n if text:\n events.append((ts, \"CODEX\", text))\n\nprint(f\"Total conversation events: {len(events)}\")\nprint(\"=\"*80)\nfor i, (ts, role, text) in enumerate(events, 1):\n print(f\"\\n--- [{i}] {role} @ {ts} ---\")\n print(text)\nEOF"}}],"created_at":"2026-06-23T05:59:03.083956Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":10816,"input_tokens":468,"output_tokens":465,"cache_read_tokens":27019,"cache_creation_tokens":null,"tpot_ms":23.260215053763442,"tokens_per_sec":42.991863905325445}},{"message_id":"2af47157-be78-4fe4-8c8c-a08a3214206f","node_id":91,"role":"tool","content":"Output from command in shell 145adf:\nTotal conversation events: 6\n================================================================================\n\n--- [1] USER @ 2026-06-23T05:33:30.006Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [2] USER @ 2026-06-23T05:37:55.023Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [3] USER @ 2026-06-23T05:44:52.652Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [4] USER @ 2026-06-23T05:52:01.139Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [5] USER @ 2026-06-23T05:55:04.156Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [6] USER @ 2026-06-23T05:57:26.246Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n\nExit code: 0","created_at":"2026-06-23T05:59:03.269320Z","finish_reason":null,"metrics":null},{"message_id":"c4054a08-48f9-466d-bc72-02a48ae6a4f1","node_id":93,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-85a8e8c479e66a77","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\n# inspect agent_message structure\nsamples = 0\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n pt = p.get(\"type\") if isinstance(p, dict) else None\n if pt == \"agent_message\":\n samples += 1\n if samples <= 3:\n print(f\"keys: {list(p.keys())}\")\n print(json.dumps(p)[:600])\n print(\"---\")\nprint(f\"total agent_message: {samples}\")\nEOF"}}],"thinking":"Hmm, only USER messages and no CODEX replies. But there are 92 agent_message events. Let me check why my filter isn't catching them. Maybe the agent_message has a different structure.","created_at":"2026-06-23T05:59:09.336649Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1124,"total_time_ms":6059,"input_tokens":953,"output_tokens":253,"cache_read_tokens":27486,"cache_creation_tokens":null,"tpot_ms":19.50592885375494,"tokens_per_sec":51.26646403242148}},{"message_id":"d51a19eb-5358-470d-9f47-37f1dece10d4","node_id":94,"role":"tool","content":"Output from command in shell 81b5ea:\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"I\\u2019ll read the attached objective first, then inspect the repo state so the next work is grounded in what\\u2019s actually present.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"The objective is a GLM-5.2 NVFP4 Modal serving task. This directory is not a git repo, and `rg --files` returned no tracked-looking files, so I\\u2019m checking the actual filesystem layout before editing anything.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"The workspace is empty, so there is no existing implementation to preserve. I\\u2019m going to scaffold the required submission files in this project using the objective\\u2019s paths and keep the server command configurable for benchmark sweeps.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\ntotal agent_message: 93\n\n\nExit code: 0","created_at":"2026-06-23T05:59:09.494146Z","finish_reason":null,"metrics":null},{"message_id":"2e9c32f3-b4f1-45e0-b954-223b6d5c2546","node_id":96,"role":"assistant","content":"Found it — agent messages use `message` not `text`. Let me extract the full conversation.","tool_calls":[{"id":"chatcmpl-tool-b8a00186093b3e17","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json, re\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\nevents = []\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n ts = d.get(\"timestamp\", \"\")\n pt = p.get(\"type\") if isinstance(p, dict) else None\n\n if t == \"response_item\" and pt == \"message\":\n role = p.get(\"role\")\n if role in (\"user\", \"developer\"):\n for c in p.get(\"content\", []):\n text = c.get(\"text\", \"\")\n if not text:\n continue\n if text.startswith((\"(.*?)\", text, re.DOTALL)\n if m:\n text = m.group(1).strip()\n else:\n continue\n events.append((ts, \"USER\", text))\n\n elif t == \"event_msg\" and pt == \"agent_message\":\n msg = p.get(\"message\", \"\")\n phase = p.get(\"phase\", \"\")\n if msg:\n events.append((ts, f\"CODEX({phase})\", msg))\n\n# write to file\nout = \"/tmp/codex_convo.txt\"\nwith open(out, \"w\") as f:\n f.write(f\"Total conversation events: {len(events)}\\n\")\n f.write(\"=\"*80 + \"\\n\\n\")\n for i, (ts, role, text) in enumerate(events, 1):\n f.write(f\"--- [{i}] {role} @ {ts} ---\\n{text}\\n\\n\")\nprint(f\"Wrote {len(events)} events to {out}\")\nimport os\nprint(f\"Size: {os.path.getsize(out)} bytes\")\nEOF"}}],"created_at":"2026-06-23T05:59:21.118042Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1501,"total_time_ms":11619,"input_tokens":537,"output_tokens":514,"cache_read_tokens":28438,"cache_creation_tokens":null,"tpot_ms":19.684824902723737,"tokens_per_sec":50.800553469065036}},{"message_id":"48208799-0ee3-443d-b28d-065bde2c58d9","node_id":97,"role":"tool","content":"Output from command in shell 43fa0d:\nWrote 99 events to /tmp/codex_convo.txt\nSize: 32717 bytes\n\n\nExit code: 0","created_at":"2026-06-23T05:59:21.308406Z","finish_reason":null,"metrics":null},{"message_id":"cd7d7e0f-163e-4e87-a1a6-394163f70f93","node_id":99,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-b4f149f5074e43b0","name":"read","arguments":{"file_path":"/tmp/codex_convo.txt"}}],"created_at":"2026-06-23T05:59:23.011374Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":1694,"input_tokens":558,"output_tokens":18,"cache_read_tokens":28974,"cache_creation_tokens":null,"tpot_ms":94.11111111111111,"tokens_per_sec":10.625737898465172}},{"message_id":"4169de3f-7aa3-4570-97a9-711000469c67","node_id":100,"role":"tool","content":"\n 1|Total conversation events: 99\n 2|================================================================================\n 3|\n 4|--- [1] USER @ 2026-06-23T05:33:30.006Z ---\n 5|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n 6|\n 7|--- [2] CODEX(commentary) @ 2026-06-23T05:33:36.328Z ---\n 8|I’ll read the attached objective first, then inspect the repo state so the next work is grounded in what’s actually present.\n 9|\n 10|--- [3] CODEX(commentary) @ 2026-06-23T05:33:44.863Z ---\n 11|The objective is a GLM-5.2 NVFP4 Modal serving task. This directory is not a git repo, and `rg --files` returned no tracked-looking files, so I’m checking the actual filesystem layout before editing anything.\n 12|\n 13|--- [4] CODEX(commentary) @ 2026-06-23T05:33:49.578Z ---\n 14|The workspace is empty, so there is no existing implementation to preserve. I’m going to scaffold the required submission files in this project using the objective’s paths and keep the server command configurable for benchmark sweeps.\n 15|\n 16|--- [5] CODEX(commentary) @ 2026-06-23T05:33:53.584Z ---\n 17|I’m writing the launch script first because it is the verifier entrypoint. It will default to a pinned community checkpoint, honor `PORT` and `MODEL_PATH`, expose tuning env vars, and print the exact vLLM config at startup for auditability.\n 18|\n 19|--- [6] CODEX(commentary) @ 2026-06-23T05:34:14.386Z ---\n 20|The launcher is in place. Next I’m adding the Modal app that installs the serving stack, mounts persistent caches, copies the project submission into `/app/submission`, and exposes a single 8×B200 web server.\n 21|\n 22|--- [7] CODEX(commentary) @ 2026-06-23T05:34:21.805Z ---\n 23|I’m also adding an empty patch hook under `submission/` now. That keeps the startup contract stable if later tuning needs to apply site-packages patches without changing the verifier entrypoint.\n 24|\n 25|--- [8] CODEX(commentary) @ 2026-06-23T05:34:43.692Z ---\n 26|The Modal wrapper is mostly mechanical, but I’m tightening one behavior: the web function should wait on the launched vLLM process so Modal keeps the container alive while the server is running.\n 27|\n 28|--- [9] CODEX(commentary) @ 2026-06-23T05:34:48.181Z ---\n 29|I’m adding the local wrappers the objective names, but keeping them lightweight: they validate the OpenAI-compatible surface, can launch the same submission entrypoint, and allow benchmark sweeps without baking tuning values into code.\n 30|\n 31|--- [10] CODEX(commentary) @ 2026-06-23T05:35:35.474Z ---\n 32|The expected files are present now. I’m going to run static checks that do not require the 8×B200 host or the 459 GB model, then inspect the final file list and launcher contents.\n 33|\n 34|--- [11] CODEX(commentary) @ 2026-06-23T05:35:47.356Z ---\n 35|Static checks passed. One remaining risk is the exact Modal SDK method names because this environment does not have the Modal package imported during compilation, so I’m checking the installed Python environment before deciding whether to adjust the wrapper.\n 36|\n 37|--- [12] CODEX(commentary) @ 2026-06-23T05:36:23.975Z ---\n 38|I checked the public Hugging Face model pages and switched the default checkpoint to Mapika’s NVFP4 build because its card explicitly says the expert FFNs are NVFP4 while attention/DSA/router/lm_head stay BF16, matching the correctness constraints. I’m pinning the current commit instead of using `main`.\n 39|\n 40|--- [13] CODEX(commentary) @ 2026-06-23T05:36:39.917Z ---\n 41|The checkpoint is pinned now. I’m rerunning syntax checks and a Modal import check so the scaffold fails early if the wrapper uses an invalid SDK surface.\n 42|\n 43|--- [14] CODEX(commentary) @ 2026-06-23T05:36:48.725Z ---\n 44|The checks passed, and Python generated `__pycache__` files during compilation. I’m removing those generated artifacts so the submission tree only contains source files.\n 45|\n 46|--- [15] CODEX(commentary) @ 2026-06-23T05:37:02.183Z ---\n 47|One useful missing piece from the objective is cache prewarming. I’m adding a Modal function that downloads the pinned checkpoint into the Hugging Face volume ahead of serving, so cold starts do not pay the full model download.\n 48|\n 49|--- [16] CODEX(commentary) @ 2026-06-23T05:37:18.502Z ---\n 50|The prewarm function compiles and the installed Modal SDK has the volume commit method. I’m cleaning generated bytecode again and then I’ll do a requirement-by-requirement sanity pass against the objective.\n 51|\n 52|--- [17] CODEX(commentary) @ 2026-06-23T05:37:27.663Z ---\n 53|The local source tree is clean now. I’m checking the concrete requirements: verifier entrypoint, Modal 8×B200 setup, pinned checkpoint, required vLLM flags, cache volumes, and local verify/benchmark commands.\n 54|\n 55|--- [18] CODEX(commentary) @ 2026-06-23T05:37:35.850Z ---\n 56|The audit shows the scaffold covers the requested entrypoints and configuration, but I cannot prove the true end state without running on the target 8×B200 Modal environment and loading the 400+ GB checkpoint. I’ll mark the implementation steps done, but keep the broader goal uncompleted because the correctness and throughput gates are still unverified.\n 57|\n 58|--- [19] CODEX(final_answer) @ 2026-06-23T05:37:54.818Z ---\n 59|Implemented the GLM-5.2 NVFP4 serving scaffold in the empty workspace.\n 60|\n 61|Changed:\n 62|- [submission/launch_server.sh](/Users/root1/Downloads/projects/glm-5.2-inference/submission/launch_server.sh:1): verifier entrypoint with vLLM NVFP4 flags, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend, configurable scheduler knobs, and a pinned checkpoint revision.\n 63|- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): Modal app using `B200:8`, persistent HF/vLLM volumes, `/app/submission` copy, web server wrapper, and a `prewarm_model_cache` helper.\n 64|- [verify_serving.py](/Users/root1/Downloads/projects/glm-5.2-inference/verify_serving.py:1), [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1), [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1): lightweight local sanity and benchmark wrappers.\n 65|\n 66|I switched the default checkpoint to `Mapika/GLM-5.2-NVFP4` and pinned revision `5f9c62a4b08c36a174055facbd1e0c39b832059f`; its Hugging Face card explicitly matches the required mixed precision layout, and the commit page confirms the revision. Sources: https://huggingface.co/Mapika/GLM-5.2-NVFP4 and https://huggingface.co/Mapika/GLM-5.2-NVFP4/commit/5f9c62a4b08c36a174055facbd1e0c39b832059f\n 67|\n 68|Verification run locally:\n 69|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n 70|- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py`\n 71|- Imported `serve_modal.py` successfully with installed Modal `1.3.0.post1`\n 72|\n 73|I did not mark the broader goal complete because the actual correctness/throughput gate requires the target Modal 8×B200 environment and the full checkpoint load.\n 74|\n 75|--- [20] USER @ 2026-06-23T05:37:55.023Z ---\n 76|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n 77|\n 78|--- [21] CODEX(commentary) @ 2026-06-23T05:38:18.653Z ---\n 79|I’ll re-read the attached objective and re-check the current files before making more changes, so the next step is based on the actual workspace state.\n 80|\n 81|--- [22] CODEX(commentary) @ 2026-06-23T05:38:28.004Z ---\n 82|I’ve confirmed the current scaffold is still present. The objective text is longer than the first read, so I’m pulling the remaining lines before changing anything else.\n 83|\n 84|--- [23] CODEX(commentary) @ 2026-06-23T05:38:40.452Z ---\n 85|The current code covers the basic shape, but there are still correctness risks around runtime dependencies and benchmark usefulness. I’m going to harden the submission in three areas: Modal/runtime packaging, reproducible launch configuration, and a more meaningful public benchmark sweep.\n 86|\n 87|--- [24] CODEX(commentary) @ 2026-06-23T05:39:12.823Z ---\n 88|I found vLLM’s current environment-variable docs list a separate CUTLASS MXFP4/MXFP8 FlashInfer gate, in addition to the env var already set. I’m adding that and making the launch script detect renamed vLLM serve flags at runtime, so it stays aligned with the requested vLLM path without hard-failing on a minor CLI rename.\n 89|\n 90|--- [25] CODEX(commentary) @ 2026-06-23T05:39:52.774Z ---\n 91|PyPI confirms `vllm==0.23.0` is current and exposes a `flashinfer` extra, so I’m updating the Modal image to request that extra explicitly rather than assuming the base vLLM wheel brings the FlashInfer pieces needed for B200 NVFP4 MoE.\n 92|\n 93|--- [26] CODEX(commentary) @ 2026-06-23T05:40:10.228Z ---\n 94|The public benchmark wrapper currently only measures full request latency. I’m replacing it with streaming measurements so it reports TTFT, end-to-end latency, and a small concurrent workload, which matches the objective’s tuning focus better.\n 95|\n 96|--- [27] CODEX(commentary) @ 2026-06-23T05:40:14.171Z ---\n 97|I removed the old benchmark file and am adding the streaming version in its place.\n 98|\n 99|--- [28] CODEX(commentary) @ 2026-06-23T05:40:56.362Z ---\n100|I spotted a small request-count issue in the new concurrent benchmark before running checks; I’m fixing that so `--concurrency` and `--repeat-prompts` do what their names imply.\n101|\n102|--- [29] CODEX(commentary) @ 2026-06-23T05:41:17.858Z ---\n103|`optimize.py` would currently start and stop the full model twice, which is the wrong tradeoff for a 459 GB checkpoint. I’m changing it to launch once, run verify and benchmark against the same server, then shut it down.\n104|\n105|--- [30] CODEX(commentary) @ 2026-06-23T05:41:25.598Z ---\n106|I’m replacing `optimize.py` with the single-server version now.\n107|\n108|--- [31] CODEX(commentary) @ 2026-06-23T05:41:40.069Z ---\n109|I’m also making the verification wrappers fail fast if the launched server process exits during startup. Without that, a bad vLLM flag could waste the full 45-minute startup timeout.\n110|\n111|--- [32] CODEX(commentary) @ 2026-06-23T05:41:54.939Z ---\n112|The main code changes are in place. I’m running syntax/import checks now, including a bash parse of the dynamically built vLLM command.\n113|\n114|--- [33] CODEX(commentary) @ 2026-06-23T05:42:08.753Z ---\n115|Static parsing passed. I’m doing one cheap launch-script dry run with a fake `vllm` executable to verify the final argument list includes the dynamic flags we expect, without starting a model.\n116|\n117|--- [34] CODEX(commentary) @ 2026-06-23T05:42:25.407Z ---\n118|The launcher dry run produced the expected vLLM command, including TP=8, FP8 KV cache, chunked prefill, revision pinning, and FlashInfer MoE backend. I’m doing one more pass over package/runtime details and then I’ll clean generated artifacts.\n119|\n120|--- [35] CODEX(commentary) @ 2026-06-23T05:42:32.534Z ---\n121|The local environment has `huggingface_hub==0.36.0`, matching the Modal image pin. I’m updating the project metadata to reflect the Modal SDK level this code was checked against and then removing bytecode artifacts.\n122|\n123|--- [36] CODEX(commentary) @ 2026-06-23T05:42:44.358Z ---\n124|Runtime and benchmark hardening are done. I’m moving into the final verification pass now: syntax checks, file inventory, and a requirement audit against the objective.\n125|\n126|--- [37] CODEX(commentary) @ 2026-06-23T05:43:10.441Z ---\n127|Static checks passed again. I’m running the fake-`vllm` dry run once more after the launcher changes, then I’ll clean bytecode and inspect the final file set.\n128|\n129|--- [38] CODEX(commentary) @ 2026-06-23T05:43:18.062Z ---\n130|The dry run confirms the launcher composes the expected command. I’m adding a short README with the exact run/prewarm/tuning commands so the target Modal pass is reproducible from this workspace.\n131|\n132|--- [39] CODEX(commentary) @ 2026-06-23T05:43:39.769Z ---\n133|I’m cleaning bytecode now and running a final inventory plus line references for the files that changed.\n134|\n135|--- [40] CODEX(commentary) @ 2026-06-23T05:43:50.885Z ---\n136|One detail from the dry run is worth tightening: if `vllm serve --help` ever fails for an environment reason, the launcher should still preserve the reference command’s revision and MoE flags. I’m making empty help output mean “use the known vLLM 0.23 defaults.”\n137|\n138|--- [41] CODEX(commentary) @ 2026-06-23T05:44:06.847Z ---\n139|I’m re-running the launcher checks with both supported-help and empty-help cases to verify the fallback behavior.\n140|\n141|--- [42] CODEX(commentary) @ 2026-06-23T05:44:18.229Z ---\n142|The launcher now falls back correctly and adapts to `--moe-backend` if that is the installed vLLM spelling. I’m doing the final Python compile and cleanup.\n143|\n144|--- [43] CODEX(commentary) @ 2026-06-23T05:44:24.994Z ---\n145|The final checks passed. I’m removing generated bytecode and then I’ll report exactly what changed and what remains unverified.\n146|\n147|--- [44] CODEX(commentary) @ 2026-06-23T05:44:29.915Z ---\n148|The workspace is not a git repository, so there is no git status to summarize. All generated bytecode has been removed; only source files remain.\n149|\n150|--- [45] CODEX(final_answer) @ 2026-06-23T05:44:52.604Z ---\n151|Continued the implementation and hardened it for the target Modal/vLLM path.\n152|\n153|Changed:\n154|- [submission/launch_server.sh](/Users/root1/Downloads/projects/glm-5.2-inference/submission/launch_server.sh:1): added FlashInfer CUTLASS env, configurable quant/MoE/load/attention knobs, runtime vLLM flag detection, and fallback to the known vLLM 0.23-style flags if help probing fails.\n155|- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): switched image install to `vllm[flashinfer]==0.23.0`, preserved `B200:8`, cache volumes, and prewarm flow.\n156|- [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1): replaced full-response timing with streaming TTFT, latency, token/char throughput, and concurrent workload measurement.\n157|- [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1): now starts the server once, runs verify and benchmark against it, then terminates.\n158|- [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1): added Modal prewarm/serve commands and tuning knobs.\n159|\n160|Verification completed:\n161|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n162|- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py`\n163|- Imported `serve_modal.py`\n164|- Parsed `pyproject.toml`\n165|- Dry-ran `launch_server.sh` with fake `vllm` for both `--moe-runner-backend` and `--moe-backend` cases\n166|\n167|Still not proven complete: I did not run the actual Modal 8×B200 server, full checkpoint load, correctness gate, or throughput benchmark. Those require the target GPU environment and the ~459 GB model cache.\n168|\n169|Sources used for runtime choices: vLLM env docs https://docs.vllm.ai/en/stable/configuration/env_vars/, vLLM PyPI metadata https://pypi.org/project/vllm/, FlashInfer install docs https://docs.flashinfer.ai/installation.html.\n170|\n171|--- [46] USER @ 2026-06-23T05:44:52.652Z ---\n172|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n173|\n174|--- [47] CODEX(commentary) @ 2026-06-23T05:45:13.152Z ---\n175|I’ll re-read the objective and inspect the current source first, then I’ll tighten whatever is still weak without assuming the previous state is complete.\n176|\n177|--- [48] CODEX(commentary) @ 2026-06-23T05:45:26.391Z ---\n178|The current implementation is source-clean and statically valid, but the biggest remaining actionable gap is tuning workflow: the objective specifically calls for sweeping chunked prefill and scheduler knobs. I’m going to add a reproducible sweep runner so benchmark data can be collected in the target Modal/GPU environment without editing files between runs.\n179|\n180|--- [49] CODEX(commentary) @ 2026-06-23T05:45:43.291Z ---\n181|The sweep runner will launch the same verifier entrypoint for each config, run the existing verify and streaming benchmark against it, write one JSONL record per config, then tear the server down cleanly.\n182|\n183|--- [50] CODEX(commentary) @ 2026-06-23T05:46:21.080Z ---\n184|The sweep runner is in place. I’m tightening process handling now so the single-server scripts can detect a server that exits during startup instead of waiting for the full startup timeout.\n185|\n186|--- [51] CODEX(commentary) @ 2026-06-23T05:46:55.405Z ---\n187|The process handling is now using the Python verifier directly, which lets it monitor the launched server process. I noticed the sweep record should capture the final server return code after termination, so I’m correcting that bookkeeping before checks.\n188|\n189|--- [52] CODEX(commentary) @ 2026-06-23T05:46:59.495Z ---\n190|The sweep and optimize scripts are hardened. I’m running syntax checks and dry-run checks for the new workflow now.\n191|\n192|--- [53] CODEX(commentary) @ 2026-06-23T05:47:22.586Z ---\n193|The new sweep CLI parses as expected. I’m testing the early-exit path with a fake `vllm` that exits immediately, which validates that `optimize.py` does not sit through the full startup timeout when the server command is bad.\n194|\n195|--- [54] CODEX(commentary) @ 2026-06-23T05:47:37.866Z ---\n196|The fake failure surfaced a real edge: the readiness loop could time out before noticing a process that died just after the first poll. I’m tightening that loop so it checks process exit before and after each connection attempt and bounds sleeps by the remaining deadline.\n197|\n198|--- [55] CODEX(commentary) @ 2026-06-23T05:47:57.385Z ---\n199|I’m rerunning the fake early-exit case to verify it now reports the server process exit instead of a generic readiness timeout.\n200|\n201|--- [56] CODEX(commentary) @ 2026-06-23T05:48:13.512Z ---\n202|The early-exit behavior is now correct. I’m checking the pinned Hugging Face config files next, because correctness depends on the checkpoint actually advertising the expected GLM architecture and NVFP4 ModelOpt quantization at the pinned revision.\n203|\n204|--- [57] CODEX(commentary) @ 2026-06-23T05:48:26.014Z ---\n205|The pinned repo has the expected `GlmMoeDsaForCausalLM` architecture and `hf_quant_config.json` with `quant_algo: NVFP4`. I’m parsing the quant config now to confirm the sensitive modules are excluded rather than trusting the README text.\n206|\n207|--- [58] CODEX(commentary) @ 2026-06-23T05:48:46.294Z ---\n208|The Mapika quant config is not as strong as expected: it excludes `lm_head` and all self-attention modules, but I did not find router `mlp.gate` exclusions in `hf_quant_config.json`. Since the objective explicitly calls that out for correctness, I’m comparing the other community checkpoints’ small config files before deciding whether to switch defaults.\n209|\n210|--- [59] CODEX(commentary) @ 2026-06-23T05:49:01.510Z ---\n211|The other listed options do not currently look better from accessible metadata: Lorbus is not publicly accessible from this environment, and lukealonso’s repo lacks `hf_quant_config.json`. I’m checking whether lukealonso encodes quantization in `config.json`; if not, Mapika remains the only metadata-complete public option.\n212|\n213|--- [60] CODEX(commentary) @ 2026-06-23T05:49:08.692Z ---\n214|lukealonso uses a different quantization metadata shape in `config.json`. I’m parsing its ignore list exactly to see whether it protects routers or only attention/shared pieces.\n215|\n216|--- [61] CODEX(commentary) @ 2026-06-23T05:49:23.720Z ---\n217|The metadata alone is ambiguous because router weights may not be represented through the same exclude mechanism. I’m checking the safetensors index names for router tensors and quantization side tensors; that gives better evidence about whether gate weights were actually quantized.\n218|\n219|--- [62] CODEX(commentary) @ 2026-06-23T05:49:46.936Z ---\n220|The safetensors index resolves the router concern for Mapika: router `mlp.gate` tensors have only weights/biases, while quantized expert/shared projections have scale tensors. I’m checking the MTP layer presence now, because the objective calls out layer 78 as a correctness-sensitive detail.\n221|\n222|--- [63] CODEX(commentary) @ 2026-06-23T05:50:19.479Z ---\n","created_at":"2026-06-23T05:59:23.087605Z","finish_reason":null,"metrics":null},{"message_id":"3205314a-4ba5-4b7b-8f74-f6c09d866181","node_id":102,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-bce5ed0b723ecafa","name":"read","arguments":{"file_path":"/tmp/codex_convo.txt","offset":222}}],"created_at":"2026-06-23T05:59:26.630964Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":3539,"input_tokens":5896,"output_tokens":24,"cache_read_tokens":29531,"cache_creation_tokens":null,"tpot_ms":147.45833333333334,"tokens_per_sec":6.781576716586606}},{"message_id":"02d64626-b56b-4def-9ad2-e6b76e1f8402","node_id":103,"role":"tool","content":"\n222|--- [63] CODEX(commentary) @ 2026-06-23T05:50:19.479Z ---\n223|Mapika is still the safer default for vLLM ModelOpt serving because it has `hf_quant_config.json`; lukealonso is the only accessible checkpoint with layer 78 MTP tensors, but it lacks that ModelOpt quant config file. I’m adding a reproducible checkpoint audit script and documenting this tradeoff so the checkpoint choice is evidence-based rather than implicit.\n224|\n225|--- [64] CODEX(commentary) @ 2026-06-23T05:50:56.650Z ---\n226|The checkpoint audit script is added. I’m running it for the pinned default now, then I’ll run the full static check set and clean generated artifacts.\n227|\n228|--- [65] CODEX(commentary) @ 2026-06-23T05:51:08.595Z ---\n229|The first audit output mixed router `mlp.gate` with dense `mlp.gate_proj` names because of a broad substring match. I’m splitting those categories so the evidence about router precision is accurate.\n230|\n231|--- [66] CODEX(commentary) @ 2026-06-23T05:51:20.483Z ---\n232|I’m rerunning the checkpoint audit after the category split to confirm router gates are reported separately from dense `gate_proj` tensors.\n233|\n234|--- [67] CODEX(commentary) @ 2026-06-23T05:51:31.712Z ---\n235|The audit now shows router gate tensors separately: router gates only have weights and correction biases, while dense `gate_proj` tensors carry scale tensors. I’m running the final static checks and cleaning generated files.\n236|\n237|--- [68] CODEX(commentary) @ 2026-06-23T05:51:40.447Z ---\n238|All static checks passed. I’m removing bytecode and taking the final file inventory now.\n239|\n240|--- [69] CODEX(commentary) @ 2026-06-23T05:51:44.462Z ---\n241|The workspace is not a git repository, so there is no git diff/status to report. Only source files remain.\n242|\n243|--- [70] CODEX(final_answer) @ 2026-06-23T05:52:01.072Z ---\n244|Continued progress on the GLM-5.2 NVFP4 submission.\n245|\n246|Added:\n247|- [sweep_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/sweep_dev_bench.py:1): runs reproducible sweeps over `MAX_NUM_BATCHED_TOKENS`, `GPU_MEMORY_UTILIZATION`, `MAX_NUM_SEQS`, and `NUM_SCHEDULER_STEPS`, writing JSONL results.\n248|- [inspect_checkpoint.py](/Users/root1/Downloads/projects/glm-5.2-inference/inspect_checkpoint.py:1): audits the pinned HF checkpoint metadata and tensor index.\n249|- README updates for sweep and checkpoint audit commands.\n250|\n251|Hardened:\n252|- [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1) now uses the verifier/benchmark functions directly and monitors the launched server process.\n253|- [verify_serving.py](/Users/root1/Downloads/projects/glm-5.2-inference/verify_serving.py:1) and [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1) now detect early server exit during readiness checks.\n254|\n255|Checkpoint audit evidence for `Mapika/GLM-5.2-NVFP4@5f9c62a4...`:\n256|- Has `config.json`, `hf_quant_config.json`, and `model.safetensors.index.json`.\n257|- `hf_quant_config.json` reports ModelOpt `NVFP4`, group size 16.\n258|- Router `mlp.gate` tensors have only `weight` and `e_score_correction_bias`; dense `gate_proj`, expert, and shared expert tensors have scale tensors.\n259|- No `model.layers.78`, so the launcher intentionally does not enable MTP.\n260|\n261|Verification run:\n262|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n263|- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py`\n264|- `python3 sweep_dev_bench.py --dry-run ...`\n265|- Fake failing `vllm` test confirmed early-exit detection works.\n266|\n267|Still not complete: the actual Modal 8×B200 run, full model load, correctness gate, and throughput benchmark remain unverified because they require the target GPU environment and cached checkpoint.\n268|\n269|--- [71] USER @ 2026-06-23T05:52:01.139Z ---\n270|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n271|\n272|--- [72] CODEX(commentary) @ 2026-06-23T05:52:24.616Z ---\n273|I’ll re-read the objective and current files, then focus on remaining correctness risks that can still be reduced without the 8×B200 runtime.\n274|\n275|--- [73] CODEX(commentary) @ 2026-06-23T05:53:19.740Z ---\n276|I found two actionable issues from the current evidence. First, Modal’s own web-server examples expect the function to start the subprocess, wait until the port is listening, then return; our wrapper was waiting forever. Second, `MODEL_REVISION` should not be forced onto a verifier-supplied local `MODEL_PATH`. I’m fixing both now.\n277|\n278|--- [74] CODEX(commentary) @ 2026-06-23T05:53:24.383Z ---\n279|I’m editing the launcher so the pinned revision is used for the default HF repo, but an externally supplied path or repo can opt in with `MODEL_REVISION` instead of inheriting the Mapika SHA accidentally.\n280|\n281|--- [75] CODEX(commentary) @ 2026-06-23T05:53:36.199Z ---\n282|The launcher fix is in. I’m updating the Modal web-server function to start vLLM, poll the local port, fail fast if vLLM exits, and then return so Modal can mark startup complete.\n283|\n284|--- [76] CODEX(commentary) @ 2026-06-23T05:53:59.484Z ---\n285|The Modal lifecycle now matches the documented pattern. I’m running dry checks for default and overridden `MODEL_PATH` to confirm revision behavior is correct.\n286|\n287|--- [77] CODEX(commentary) @ 2026-06-23T05:54:03.909Z ---\n288|The revision handling now does what we want: default repo is pinned, local verifier paths are not forced to use the Mapika SHA, and explicit `MODEL_REVISION` still works. I’m running the full static checks next.\n289|\n290|--- [78] CODEX(commentary) @ 2026-06-23T05:54:20.686Z ---\n291|Static checks passed. I’m adding a README note for the safer revision behavior and then I’ll validate the new Modal readiness helper against a real local HTTP subprocess.\n292|\n293|--- [79] CODEX(commentary) @ 2026-06-23T05:54:26.448Z ---\n294|The README now documents how override paths interact with revision pinning. I’m testing `serve_modal.wait_ready` with a local subprocess that listens on a port, without touching Modal or GPUs.\n295|\n296|--- [80] CODEX(commentary) @ 2026-06-23T05:54:38.220Z ---\n297|The Modal readiness helper works against a real local HTTP subprocess. I’m doing the final compile pass and cleaning generated artifacts now.\n298|\n299|--- [81] CODEX(commentary) @ 2026-06-23T05:54:49.485Z ---\n300|The final checks passed and the checkpoint audit still resolves to the pinned Mapika revision. I’m removing bytecode and capturing the final file inventory.\n301|\n302|--- [82] CODEX(commentary) @ 2026-06-23T05:54:52.949Z ---\n303|There is no git repository here, so there is no git status to report. The source tree is clean of generated bytecode.\n304|\n305|--- [83] CODEX(final_answer) @ 2026-06-23T05:55:04.089Z ---\n306|Fixed two important runtime issues.\n307|\n308|Updated [submission/launch_server.sh](/Users/root1/Downloads/projects/glm-5.2-inference/submission/launch_server.sh:1):\n309|- Default `Mapika/GLM-5.2-NVFP4` still gets pinned to revision `5f9c62a4b08c36a174055facbd1e0c39b832059f`.\n310|- If the verifier supplies `MODEL_PATH`, no Mapika revision is forced unless `MODEL_REVISION` is explicitly set. This avoids passing an HF revision to a local model path.\n311|\n312|Updated [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1):\n313|- `@modal.web_server` now starts vLLM with `Popen`, polls the local port, fails fast if vLLM exits, then returns. This matches Modal’s documented pattern for web-server subprocesses.\n314|- Verified `wait_ready` locally with a real `python3 -m http.server` subprocess.\n315|\n316|Also updated [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1) to document the revision override behavior.\n317|\n318|Checks run:\n319|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n320|- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py`\n321|- launcher dry-runs for default model, local `MODEL_PATH`, and explicit `MODEL_REVISION`\n322|- `python3 inspect_checkpoint.py`\n323|\n324|Still not complete: actual Modal 8×B200 startup, full checkpoint load, hidden correctness gate, and throughput benchmark remain unverified.\n325|\n326|--- [84] USER @ 2026-06-23T05:55:04.156Z ---\n327|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n328|\n329|--- [85] CODEX(commentary) @ 2026-06-23T05:55:23.077Z ---\n330|I’ll re-read the objective and re-check the current source, then I’ll keep reducing runtime risk where we can still prove something locally.\n331|\n332|--- [86] CODEX(commentary) @ 2026-06-23T05:55:36.352Z ---\n333|The current structure is sound, but I’m going to add a local OpenAI-compatible mock self-test so the verify and streaming benchmark wrappers are exercised end-to-end without needing GPUs. That gives us stronger evidence than syntax checks for the tooling around the real server.\n334|\n335|--- [87] CODEX(commentary) @ 2026-06-23T05:55:48.303Z ---\n336|I’m adding the mock server and self-test as root-level dev helpers, not under `submission/`, so they cannot change verifier behavior. The production entrypoint stays `submission/launch_server.sh`.\n337|\n338|--- [88] CODEX(commentary) @ 2026-06-23T05:56:15.876Z ---\n339|The mock server and self-test are in place. I’m running the self-test now, which exercises `/v1/models`, non-stream chat verification, and streaming benchmark parsing with concurrent requests.\n340|\n341|--- [89] CODEX(commentary) @ 2026-06-23T05:56:40.733Z ---\n342|The self-test passed. I’m also exercising `optimize.py` against the mock server through its normal `--script` launch path, so the “launch once, verify, benchmark, terminate” path is covered too.\n343|\n344|--- [90] CODEX(commentary) @ 2026-06-23T05:56:50.464Z ---\n345|The mock launch path passed for both `optimize.py` and a one-config `sweep_dev_bench.py` run, including JSONL output. I’m doing the final static pass and cleaning bytecode.\n346|\n347|--- [91] CODEX(commentary) @ 2026-06-23T05:57:08.716Z ---\n348|The static pass and metadata audit are clean. I’m removing generated bytecode and taking the final source inventory.\n349|\n350|--- [92] CODEX(commentary) @ 2026-06-23T05:57:13.719Z ---\n351|There is no git repository in this directory, so no git status is available. The final file inventory contains only source files and scripts.\n352|\n353|--- [93] CODEX(final_answer) @ 2026-06-23T05:57:26.167Z ---\n354|Added local end-to-end wrapper tests without changing the verifier entrypoint.\n355|\n356|New files:\n357|- [mock_openai_server.py](/Users/root1/Downloads/projects/glm-5.2-inference/mock_openai_server.py:1): tiny OpenAI-compatible mock server for `/v1/models` and `/v1/chat/completions`, including streaming SSE.\n358|- [selftest_wrappers.py](/Users/root1/Downloads/projects/glm-5.2-inference/selftest_wrappers.py:1): starts the mock server, runs `verify_serving`, serial benchmark, and concurrent benchmark.\n359|- Updated [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1) with the self-test command.\n360|\n361|Validated:\n362|- `python3 selftest_wrappers.py`\n363|- `python3 optimize.py --script ...`\n364|- `python3 sweep_dev_bench.py --script ...`\n365|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n366|- `python3 -m py_compile ...`\n367|- `python3 inspect_checkpoint.py`\n368|\n369|This gives stronger evidence that the verification/benchmark tooling works around an OpenAI-compatible server, including streaming TTFT and concurrent requests. The actual 8×B200 Modal run, full GLM checkpoint load, correctness gate, and throughput benchmark are still unverified because they require the target GPU environment.\n370|\n371|--- [94] USER @ 2026-06-23T05:57:26.246Z ---\n372|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n373|\n374|--- [95] CODEX(commentary) @ 2026-06-23T05:57:48.711Z ---\n375|I’ll inspect the objective and current tree again, then I’ll target the remaining things we can verify locally: packaging, command composition, and small runtime contract tests.\n376|\n377|--- [96] CODEX(commentary) @ 2026-06-23T05:58:27.182Z ---\n378|One runtime risk remains from the Modal image env: it sets `MODEL_REVISION` globally, so a future Modal `MODEL_PATH` override could accidentally inherit the Mapika SHA. The launcher already knows how to pin the default, so I’m removing that env value from the image.\n379|\n380|--- [97] CODEX(commentary) @ 2026-06-23T05:58:31.951Z ---\n381|I’m making the Modal env change first; the prewarm function still uses the pinned revision through its function default, and the launcher still pins the default repo internally.\n382|\n383|--- [98] CODEX(commentary) @ 2026-06-23T05:58:38.892Z ---\n384|Next I’m adding a small validator that checks the key local invariants: submission file exists/executable, launcher command composition preserves TP=8/FP8/chunking, local model paths do not get the default revision, and Modal is configured for `B200:8`.\n385|\n386|--- [99] CODEX(commentary) @ 2026-06-23T05:59:05.225Z ---\n387|The validator is added. I’m running it now along with the existing mock self-test so we catch both static invariant drift and wrapper behavior regressions.\n","created_at":"2026-06-23T05:59:26.677854Z","finish_reason":null,"metrics":null},{"message_id":"4f696e4b-20e1-458f-9240-9e07e835f3e2","node_id":105,"role":"assistant","content":"Here's the full conversation from your active Codex CLI session (thread `019ef2f8…`, GLM-5.2 NVFP4/Modal task). I've saved it to `/tmp/codex_convo.txt` (99 events: 6 user turns + 93 Codex messages).\n\n## Summary of the conversation\n\n**The task:** Serve `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM (OpenAI-compatible), with a correctness gate vs BF16 and a throughput benchmark. Pasted from `/Users/root1/.codex/attachments/ec1b915e-…/pasted-text-1.txt`.\n\n**The pattern:** You (or a scheduler) re-pasted the same objective 6 times. Each time Codex did one round of work and posted a `final_answer`, then the next paste kicked off another round. So this is really 6 iterations on the same goal.\n\n### Round-by-round\n\n**Round 1** (05:33–05:37) — Initial scaffold\n- Created `submission/launch_server.sh` (vLLM NVFP4 flags, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend, pinned checkpoint)\n- Created `serve_modal.py` (Modal `B200:8`, persistent HF/vLLM volumes, `/app/submission` copy, `prewarm_model_cache` helper)\n- Created `verify_serving.py`, `run_dev_bench.py`, `optimize.py`\n- Switched default checkpoint to `Mapika/GLM-5.2-NVFP4` @ `5f9c62a4…` (its HF card matches the required mixed-precision layout)\n- Static checks passed; flagged that real 8×B200 run + correctness/throughput gates still unverified\n\n**Round 2** (05:38–05:44) — Runtime/benchmark hardening\n- Added FlashInfer CUTLASS env var, configurable quant/MoE/load/attention knobs\n- Runtime vLLM flag detection with fallback to vLLM 0.23 defaults\n- Switched Modal image to `vllm[flashinfer]==0.23.0`\n- Replaced full-response timing with streaming TTFT/latency/throughput + concurrent workload\n- `optimize.py` now launches server once and reuses it\n- Added README\n\n**Round 3** (05:45–05:51) — Sweep + checkpoint audit\n- Added `sweep_dev_bench.py` (sweeps `MAX_NUM_BATCHED_TOKENS`, `GPU_MEMORY_UTILIZATION`, `MAX_NUM_SEQS`, `NUM_SCHEDULER_STEPS`, JSONL output)\n- Added `inspect_checkpoint.py` — audited Mapika checkpoint:\n - Has `config.json`, `hf_quant_config.json` (ModelOpt NVFP4, group size 16)\n - Router `mlp.gate` tensors have only `weight` + `e_score_correction_bias` (NOT quantized ✓)\n - Dense `gate_proj`/expert/shared tensors have scale tensors (quantized ✓)\n - **No `model.layers.78`** → launcher does not enable MTP\n- Hardened early-exit detection in `verify_serving.py` / `run_dev_bench.py`\n\n**Round 4** (05:52–05:54) — Two runtime bug fixes\n- `MODEL_REVISION` no longer forced onto verifier-supplied local `MODEL_PATH`\n- `@modal.web_server` now starts vLLM with `Popen`, polls the local port, fails fast if vLLM exits, then returns (matches Modal's documented subprocess pattern)\n- Verified `wait_ready` locally with `python3 -m http.server`\n\n**Round 5** (05:55–05:57) — Local end-to-end mock tests\n- Added `mock_openai_server.py` (tiny OpenAI-compatible server with streaming SSE)\n- Added `selftest_wrappers.py` — exercises `/v1/models`, non-stream chat verify, streaming benchmark with concurrency\n- Ran `optimize.py` and `sweep_dev_bench.py` against the mock launcher successfully\n\n**Round 6** (05:57–05:59, in progress)\n- Removed `MODEL_REVISION` from the Modal image env (was risking leakage onto future `MODEL_PATH` overrides)\n- Adding a small invariant validator (submission file exists/executable, launcher composes TP=8/FP8/chunking, local paths don't get default revision, Modal is `B200:8`)\n- About to run validator + mock self-test — **this is where the session is right now**\n\n### Honest status (per Codex's own repeated caveat)\nEverything that can be proven locally is proven. The actual Modal 8×B200 startup, full ~459 GB checkpoint load, hidden correctness gate, and throughput benchmark remain unverified because they require the target GPU environment.\n\nWant me to keep tailing the live rollout as Round 6 finishes, or extract anything specific (e.g. the full `launch_server.sh` or `serve_modal.py`)?","tool_calls":[],"created_at":"2026-06-23T05:59:58.118247Z","finish_reason":"stop","metrics":{"ttft_ms":1347,"total_time_ms":31434,"input_tokens":3983,"output_tokens":1088,"cache_read_tokens":35426,"cache_creation_tokens":null,"tpot_ms":27.653492647058822,"tokens_per_sec":36.161797454049925}},{"message_id":"c10c015d-0637-493c-8546-16a376693ef7","node_id":106,"role":"user","content":"has it done anything?","created_at":"2026-06-23T06:01:07.548364Z","finish_reason":null,"metrics":null},{"message_id":"59c38c4b-57fa-43c4-bb4e-98e94b301772","node_id":108,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-8fcb33484a822242","name":"exec","arguments":{"command":"ls -la ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl; echo \"---\"; ps -p 18906 -o pid,stat,etime,command 2>&1; echo \"---last event ts---\"; tail -c 500 ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | grep -o '\"timestamp\":\"[^\"]*\"' | tail -3"}}],"thinking":"The user is asking if the Codex session has done anything since I last checked. Let me check the live state of the rollout file and the workspace.","created_at":"2026-06-23T06:01:16.270743Z","finish_reason":"tool_calls","metrics":{"ttft_ms":2438,"total_time_ms":8720,"input_tokens":15117,"output_tokens":209,"cache_read_tokens":25187,"cache_creation_tokens":null,"tpot_ms":30.057416267942585,"tokens_per_sec":33.26965934415791}},{"message_id":"b1007dce-3166-4228-bb15-a34ca47f0b0c","node_id":109,"role":"tool","content":"Output from command in shell 9a8b53:\n-rw-r--r-- 1 root1 staff 1111726 Jun 22 23:01 /Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n---\n PID STAT ELAPSED COMMAND\n18906 S+ 27:53 /opt/homebrew/lib/node_modules/@openai/codex/node_modules/@openai/codex-darwin-arm64/vendor/aarch64-apple-darwin/bin/codex --search --yolo\n---last event ts---\n\"timestamp\":\"2026-06-23T06:01:14.077Z\"\n\n\nExit code: 0","created_at":"2026-06-23T06:01:16.296313Z","finish_reason":null,"metrics":null},{"message_id":"a233a386-0aa3-4d9b-abb4-a56341221723","node_id":111,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a3bf71e5b0e831e0","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json, re\n\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n\nevents = []\nwith open(path) as f:\n for line in f:\n try:\n d = json.loads(line)\n except:\n continue\n t = d.get(\"type\")\n p = d.get(\"payload\", {})\n ts = d.get(\"timestamp\", \"\")\n pt = p.get(\"type\") if isinstance(p, dict) else None\n\n if t == \"response_item\" and pt == \"message\":\n role = p.get(\"role\")\n if role in (\"user\", \"developer\"):\n for c in p.get(\"content\", []):\n text = c.get(\"text\", \"\")\n if not text:\n continue\n if text.startswith((\"(.*?)\", text, re.DOTALL)\n if m:\n text = m.group(1).strip()\n else:\n continue\n events.append((ts, \"USER\", text))\n elif t == \"event_msg\" and pt == \"agent_message\":\n msg = p.get(\"message\", \"\")\n phase = p.get(\"phase\", \"\")\n if msg:\n events.append((ts, f\"CODEX({phase})\", msg))\n\n# only show events after 05:59:05 (event 99)\nnew_events = [(ts,r,t) for ts,r,t in events if ts > \"2026-06-23T05:59:05\"]\nprint(f\"Events since last check: {len(new_events)} (total now: {len(events)})\")\nprint(\"=\"*80)\nfor i, (ts, role, text) in enumerate(new_events, 100):\n print(f\"\\n--- [{i}] {role} @ {ts} ---\")\n print(text)\nEOF"}}],"thinking":"The session is still running. Last event was at 06:01:14 UTC (about 23:01 local). Let me check what's happened since my last extraction (which ended at event 99, timestamp 05:59:05).","created_at":"2026-06-23T06:01:28.812422Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1376,"total_time_ms":12514,"input_tokens":406,"output_tokens":534,"cache_read_tokens":40303,"cache_creation_tokens":null,"tpot_ms":20.857677902621724,"tokens_per_sec":47.943975579098584}},{"message_id":"ae082489-b3bf-4c4a-b6ee-0c2dcb121e02","node_id":112,"role":"tool","content":"Output from command in shell 2576f6:\nEvents since last check: 10 (total now: 108)\n================================================================================\n\n--- [100] CODEX(commentary) @ 2026-06-23T05:59:05.225Z ---\nThe validator is added. I’m running it now along with the existing mock self-test so we catch both static invariant drift and wrapper behavior regressions.\n\n--- [101] CODEX(commentary) @ 2026-06-23T05:59:42.054Z ---\nThe validator and wrapper self-test passed. I’m doing one more pass through the production launcher dry-runs and final cleanup so the source tree is left in a verified state.\n\n--- [102] CODEX(commentary) @ 2026-06-23T05:59:54.126Z ---\nThe production launcher dry-run and mock `optimize.py` path both passed. I’m cleaning bytecode and taking the final inventory now.\n\n--- [103] CODEX(commentary) @ 2026-06-23T06:00:01.009Z ---\nThere is no git repository in this workspace, so no git status is available. Only source files remain after cleanup.\n\n--- [104] CODEX(final_answer) @ 2026-06-23T06:00:09.901Z ---\nContinued hardening the submission.\n\nChanged:\n- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): removed global `MODEL_REVISION` from the Modal image env. The launcher now owns default revision pinning, so a future Modal `MODEL_PATH` override will not accidentally inherit the Mapika SHA.\n- [validate_submission.py](/Users/root1/Downloads/projects/glm-5.2-inference/validate_submission.py:1): added local invariant checks for launcher flags, default revision behavior, local `MODEL_PATH` behavior, and Modal `B200:8`/web server configuration.\n- [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1): documented the validator command.\n\nVerification run:\n- `python3 validate_submission.py`\n- `python3 selftest_wrappers.py`\n- `python3 optimize.py --script ...`\n- launcher dry-run with fake `vllm`\n- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n- `python3 -m py_compile ...`\n\nStill not complete: the real Modal 8×B200 server startup, full 459 GB checkpoint load, correctness comparison, and throughput benchmark remain unverified because they require the target GPU environment.\n\n--- [105] USER @ 2026-06-23T06:00:10.114Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [106] CODEX(commentary) @ 2026-06-23T06:00:29.739Z ---\nI’ll re-read the objective and inspect the current files, then I’ll look for remaining local gaps in the launch contract and Modal packaging before making changes.\n\n--- [107] CODEX(commentary) @ 2026-06-23T06:00:52.104Z ---\nThe current code passes local contract tests. I’m checking the Modal CLI/package surface next and then I’ll add a small preflight script that consolidates the local checks into one command, so the submission can be audited before any expensive GPU run.\n\n--- [108] CODEX(commentary) @ 2026-06-23T06:01:05.638Z ---\nThe installed Modal client and API signatures match the code paths we’re using. I’m adding a `preflight.py` runner to execute all cheap local gates in order, including syntax, launcher invariants, wrapper self-test, sweep dry-run, and checkpoint metadata audit.\n\n--- [109] CODEX(commentary) @ 2026-06-23T06:01:14.074Z ---\nThis preflight stays local and does not start Modal or download model shards. It only checks source and small Hugging Face metadata files.\n\n\nExit code: 0","created_at":"2026-06-23T06:01:28.853251Z","finish_reason":null,"metrics":null},{"message_id":"73051e5d-1c68-433d-8f30-648b4f2865ab","node_id":114,"role":"assistant","content":"Yes — it's been busy. Since I last showed you (event 99), 10 new events have landed:\n\n**Round 6 finished** (05:59–06:00) — `final_answer` posted:\n- `serve_modal.py`: removed global `MODEL_REVISION` from the Modal image env (so a future `MODEL_PATH` override won't inherit the Mapika SHA by accident)\n- New `validate_submission.py`: local invariant checks (launcher flags, default vs. local `MODEL_PATH` revision behavior, Modal `B200:8`/web_server config)\n- README updated with the validator command\n- All local checks passed: `validate_submission.py`, `selftest_wrappers.py`, mock `optimize.py`, launcher dry-run, `bash -n`, `py_compile`\n- Same honest caveat: real 8×B200 run + correctness/throughput gates still unverified\n\n**Round 7 started** (06:00–06:01, in progress):\n- You (or the scheduler) pasted the objective again at 06:00:10\n- Codex re-read the objective, confirmed current code passes local contract tests\n- Verified installed Modal client/API signatures match the code\n- **Currently adding `preflight.py`** — a single runner that consolidates all cheap local gates (syntax, launcher invariants, wrapper self-test, sweep dry-run, checkpoint metadata audit). Stays local, no Modal/GPU.\n\nSo: still no real GPU run (can't do that from your Mac), but it's grinding through incremental hardening of the submission scaffold on each re-paste. The session is alive at PID 18906, last event 06:01:14 UTC (≈23:01 local), 28 min elapsed.","tool_calls":[],"created_at":"2026-06-23T06:01:37.233498Z","finish_reason":"stop","metrics":{"ttft_ms":1120,"total_time_ms":8379,"input_tokens":1453,"output_tokens":367,"cache_read_tokens":40708,"cache_creation_tokens":null,"tpot_ms":19.779291553133515,"tokens_per_sec":50.5579280892685}},{"message_id":"92a20de7-48de-460e-8523-aff0442753a2","node_id":115,"role":"user","content":"i mean anything that actually does anything, it seems like its avoiding running anything hard","created_at":"2026-06-23T06:01:58.989226Z","finish_reason":null,"metrics":null},{"message_id":"359c3f1c-eff3-4329-9225-fde3478fd182","node_id":117,"role":"assistant","content":"You're right, and that's a fair read. Let me check whether it even *can* run the real thing.","tool_calls":[{"id":"chatcmpl-tool-879b0d68eefc6660","name":"exec","arguments":{"command":"ls ~/.modal.toml 2>&1; echo \"---\"; cat ~/.modal.toml 2>&1 | head -20 | sed 's/token_id.*/token_id=***REDACTED***/; s/token_secret.*/token_secret=***REDACTED***/'; echo \"---modal cli---\"; which modal; modal --version 2>&1; echo \"---workspace files---\"; ls -la /Users/root1/Downloads/projects/glm-5.2-inference/ 2>&1"}}],"thinking":"The user is frustrated. They're noticing that Codex keeps doing \"local hardening\" — adding validators, mock servers, preflight scripts, static checks — but never actually running the real thing on Modal 8×B200. They want to know if it's actually doing the substantive work or just spinning on cheap local stuff.\n\nLet me be honest. Looking at the conversation:\n- 7 rounds, all re-pastes of the same objective\n- Every round ends with \"Still not complete: actual Modal 8×B200 run, full checkpoint load, correctness gate, throughput benchmark remain unverified\"\n- It's been adding validators, mock servers, preflight scripts, sweep runners, checkpoint auditors — but never actually deploying to Modal\n\nThe user is right. It's avoiding the expensive/risky step. The whole point of the task is to serve on Modal 8×B200 and pass a correctness gate. Codex has been doing everything except that.\n\nLet me check: does it have Modal credentials? Can it actually run `modal deploy` or `modal run`? Let me check the actual files it created and whether Modal is set up.\n\nAlso — the user pasted the same objective 6 times. That's suspicious. Either the user is manually re-pasting, or there's some scheduler/agent loop doing it. Each time Codex does a small round of work and posts final_answer. It's stuck in a loop of incremental local hardening.\n\nLet me check if Modal is configured and whether it could actually run the real thing.","created_at":"2026-06-23T06:02:13.593593Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1618,"total_time_ms":14603,"input_tokens":2160,"output_tokens":458,"cache_read_tokens":40304,"cache_creation_tokens":null,"tpot_ms":28.351528384279476,"tokens_per_sec":35.271467077397}},{"message_id":"fddd0733-db91-4f26-872a-6d23fb73823c","node_id":118,"role":"tool","content":"Output from command in shell 85246f:\n/Users/root1/.modal.toml\n---\n[aidenhuang-api]\ntoken_id=***REDACTED***\ntoken_secret=***REDACTED***\n\n[aidenhuang-personal]\ntoken_id=***REDACTED***\ntoken_secret=***REDACTED***\nactive = true\n---modal cli---\n/Library/Frameworks/Python.framework/Versions/3.12/bin/modal\nmodal client version: 1.3.0.post1\n---workspace files---\ntotal 128\ndrwxr-xr-x 15 root1 staff 480 Jun 22 23:01 .\ndrwxr-xr-x 7 root1 staff 224 Jun 22 22:33 ..\n-rw-r--r-- 1 root1 staff 5190 Jun 22 22:51 inspect_checkpoint.py\n-rw-r--r-- 1 root1 staff 3619 Jun 22 22:56 mock_openai_server.py\n-rw-r--r-- 1 root1 staff 2168 Jun 22 22:46 optimize.py\n-rw-r--r-- 1 root1 staff 1543 Jun 22 23:01 preflight.py\n-rw-r--r-- 1 root1 staff 161 Jun 22 22:42 pyproject.toml\n-rw-r--r-- 1 root1 staff 2521 Jun 22 23:01 README.md\n-rw-r--r-- 1 root1 staff 8539 Jun 22 22:47 run_dev_bench.py\n-rw-r--r-- 1 root1 staff 1042 Jun 22 22:56 selftest_wrappers.py\n-rw-r--r-- 1 root1 staff 2645 Jun 22 22:58 serve_modal.py\ndrwxr-xr-x 4 root1 staff 128 Jun 22 22:34 submission\n-rw-r--r-- 1 root1 staff 6075 Jun 22 22:46 sweep_dev_bench.py\n-rw-r--r-- 1 root1 staff 3902 Jun 22 22:58 validate_submission.py\n-rw-r--r-- 1 root1 staff 3893 Jun 22 22:47 verify_serving.py\n\n\nExit code: 0","created_at":"2026-06-23T06:02:15.946387Z","finish_reason":null,"metrics":null},{"message_id":"88b71b8d-9ac7-4128-9430-ebf3a3875f4d","node_id":120,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-909c75f4dfb1ab9c","name":"exec","arguments":{"command":"grep -nE \"modal (run|deploy|app\\.run|app\\.deploy|@app\\.function|@app\\.cli\\()\" /Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py 2>&1; echo \"---\"; grep -nE \"modal (run|deploy)\" /Users/root1/Downloads/projects/glm-5.2-inference/*.py /Users/root1/Downloads/projects/glm-5.2-inference/README.md 2>&1; echo \"---function names---\"; grep -nE \"^def |@app\\.|^@modal\" /Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py 2>&1"}}],"created_at":"2026-06-23T06:02:26.574747Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":10616,"input_tokens":1007,"output_tokens":161,"cache_read_tokens":42463,"cache_creation_tokens":null,"tpot_ms":65.93788819875776,"tokens_per_sec":15.165787490580257}},{"message_id":"014e10ac-9824-4311-bde3-3c4767c9ec7e","node_id":121,"role":"tool","content":"Output from command in shell 13e613:\n---\n/Users/root1/Downloads/projects/glm-5.2-inference/README.md:28:modal run serve_modal.py::prewarm_model_cache\n---function names---\n49:def wait_ready(process: subprocess.Popen, port: int, timeout_s: int) -> None:\n62:@app.function(\n67:def prewarm_model_cache(\n81:@app.function(\n91:@modal.concurrent(max_inputs=8)\n92:@modal.web_server(port=VLLM_PORT, startup_timeout=45 * MINUTES)\n93:def serve():\n\n\nExit code: 0","created_at":"2026-06-23T06:02:26.655882Z","finish_reason":null,"metrics":null},{"message_id":"2cc92954-6bf4-445f-9f22-5301b43009ce","node_id":123,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-85332a2c207bc891","name":"exec","arguments":{"command":"grep -nE \"modal (run|deploy|serve)\" /Users/root1/Downloads/projects/glm-5.2-inference/README.md; echo \"---\"; cat /Users/root1/Downloads/projects/glm-5.2-inference/README.md | head -60"}}],"created_at":"2026-06-23T06:02:31.375945Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":4712,"input_tokens":290,"output_tokens":68,"cache_read_tokens":43469,"cache_creation_tokens":null,"tpot_ms":69.29411764705883,"tokens_per_sec":14.431239388794566}},{"message_id":"b406c514-a947-4d79-bc95-14fe52e82602","node_id":124,"role":"tool","content":"Output from command in shell 8b9ea7:\n28:modal run serve_modal.py::prewarm_model_cache\n34:modal serve serve_modal.py\n---\n# GLM-5.2 NVFP4 Modal Inference\n\nThis workspace serves `zai-org/GLM-5.2` from the pinned NVFP4 checkpoint\n`Mapika/GLM-5.2-NVFP4` on one Modal `B200:8` node through vLLM's\nOpenAI-compatible server.\n\nThe verifier entrypoint is:\n\n```bash\n/app/submission/launch_server.sh\n```\n\nThe local equivalent is:\n\n```bash\nPORT=30000 MODEL_PATH=Mapika/GLM-5.2-NVFP4 bash submission/launch_server.sh\n```\n\nIf `MODEL_PATH` is not set, the launcher pins the default checkpoint revision.\nIf `MODEL_PATH` is supplied by a verifier or points at a local cache path, no\nrevision is passed unless `MODEL_REVISION` is explicitly set.\n\n## Modal\n\nPrewarm the Hugging Face volume before serving:\n\n```bash\nmodal run serve_modal.py::prewarm_model_cache\n```\n\nServe on Modal:\n\n```bash\nmodal serve serve_modal.py\n```\n\n## Checks\n\nRun a startup and coherence check:\n\n```bash\nuv run --no-sync python verify_serving.py\n```\n\nRun the streaming TTFT/throughput benchmark:\n\n```bash\nuv run --no-sync python run_dev_bench.py\n```\n\nSweep chunked-prefill and scheduler settings:\n\n```bash\nuv run --no-sync python sweep_dev_bench.py\n```\n\nAudit the pinned checkpoint metadata:\n\n```bash\nuv run --no-sync python inspect_checkpoint.py\n\n\nExit code: 0","created_at":"2026-06-23T06:02:31.433627Z","finish_reason":null,"metrics":null},{"message_id":"d1393457-e8ac-47ef-a137-0808218eea45","node_id":126,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-ba06e09ae9f4e100","name":"exec","arguments":{"command":"grep -nE \"(modal run|modal serve|modal deploy|prewarm)\" ~/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl | head -20; echo \"---function_call names---\"; python3 -c \"\nimport json\nseen=set()\nwith open('/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl') as f:\n for line in f:\n d=json.loads(line)\n p=d.get('payload',{})\n if p.get('type') in ('function_call','custom_tool_call'):\n name=p.get('name','')\n if name not in seen:\n seen.add(name)\n # show a sample of arguments\n args=p.get('arguments','') or p.get('command','')\n if isinstance(args,str):\n args=args[:200]\n print(f'{name}: {args}')\nprint('---all tool names used---')\nprint(sorted(seen))\n\""}}],"created_at":"2026-06-23T06:02:37.749347Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":6310,"input_tokens":433,"output_tokens":289,"cache_read_tokens":43758,"cache_creation_tokens":null,"tpot_ms":21.833910034602077,"tokens_per_sec":45.800316957210775}},{"message_id":"54fbec19-1ed7-4bb9-8088-62a80a0e097a","node_id":127,"role":"tool","content":"Output from command in shell 48d98b:\n135:{\"timestamp\":\"2026-06-23T05:37:02.183Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent_message\",\"message\":\"One useful missing piece from the objective is cache prewarming. I’m adding a Modal function that downloads the pinned checkpoint into the Hugging Face volume ahead of serving, so cold starts do not pay the full model download.\",\"phase\":\"commentary\",\"memory_citation\":null}}\n136:{\"timestamp\":\"2026-06-23T05:37:02.183Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"message\",\"id\":\"msg_0ade37a884fa4091016a3a1b7d1e1481999df33a340f599c25\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"One useful missing piece from the objective is cache prewarming. I’m adding a Modal function that downloads the pinned checkpoint into the Hugging Face volume ahead of serving, so cold starts do not pay the full model download.\"}],\"phase\":\"commentary\",\"internal_chat_message_metadata_passthrough\":{\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\"}}}\n137:{\"timestamp\":\"2026-06-23T05:37:08.356Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"custom_tool_call\",\"id\":\"ctc_0ade37a884fa4091016a3a1b7e2b148199893093ceff2e43ff\",\"status\":\"completed\",\"call_id\":\"call_4Pk8Jb2DvZTwIJHAFeNWQblC\",\"name\":\"apply_patch\",\"input\":\"*** Begin Patch\\n*** Update File: serve_modal.py\\n@@\\n APP_NAME = \\\"glm-5.2-nvfp4\\\"\\n MINUTES = 60\\n+DEFAULT_MODEL_PATH = \\\"Mapika/GLM-5.2-NVFP4\\\"\\n+DEFAULT_MODEL_REVISION = \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\"\\n@@\\n- \\\"MODEL_PATH\\\": \\\"Mapika/GLM-5.2-NVFP4\\\",\\n- \\\"MODEL_REVISION\\\": \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\",\\n+ \\\"MODEL_PATH\\\": DEFAULT_MODEL_PATH,\\n+ \\\"MODEL_REVISION\\\": DEFAULT_MODEL_REVISION,\\n@@\\n hf_cache = modal.Volume.from_name(\\\"hf-cache\\\", create_if_missing=True)\\n vllm_cache = modal.Volume.from_name(\\\"vllm-cache\\\", create_if_missing=True)\\n \\n \\n+@app.function(\\n+ image=image,\\n+ timeout=60 * MINUTES,\\n+ volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n+)\\n+def prewarm_model_cache(\\n+ model_path: str = DEFAULT_MODEL_PATH,\\n+ revision: str = DEFAULT_MODEL_REVISION,\\n+):\\n+ from huggingface_hub import snapshot_download\\n+\\n+ snapshot_download(\\n+ repo_id=model_path,\\n+ revision=revision,\\n+ local_dir_use_symlinks=False,\\n+ )\\n+ hf_cache.commit()\\n+\\n+\\n @app.function(\\n*** End Patch\\n\",\"internal_chat_message_metadata_passthrough\":{\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\"}}}\n138:{\"timestamp\":\"2026-06-23T05:37:08.360Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"patch_apply_end\",\"call_id\":\"call_4Pk8Jb2DvZTwIJHAFeNWQblC\",\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\",\"stdout\":\"Success. Updated the following files:\\nM serve_modal.py\\n\",\"stderr\":\"\",\"success\":true,\"changes\":{\"/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py\":{\"type\":\"update\",\"unified_diff\":\"@@ -9,2 +9,4 @@\\n MINUTES = 60\\n+DEFAULT_MODEL_PATH = \\\"Mapika/GLM-5.2-NVFP4\\\"\\n+DEFAULT_MODEL_REVISION = \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\"\\n \\n@@ -28,4 +30,4 @@\\n \\\"PYTHONUNBUFFERED\\\": \\\"1\\\",\\n- \\\"MODEL_PATH\\\": \\\"Mapika/GLM-5.2-NVFP4\\\",\\n- \\\"MODEL_REVISION\\\": \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\",\\n+ \\\"MODEL_PATH\\\": DEFAULT_MODEL_PATH,\\n+ \\\"MODEL_REVISION\\\": DEFAULT_MODEL_REVISION,\\n }\\n@@ -45,2 +47,21 @@\\n image=image,\\n+ timeout=60 * MINUTES,\\n+ volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n+)\\n+def prewarm_model_cache(\\n+ model_path: str = DEFAULT_MODEL_PATH,\\n+ revision: str = DEFAULT_MODEL_REVISION,\\n+):\\n+ from huggingface_hub import snapshot_download\\n+\\n+ snapshot_download(\\n+ repo_id=model_path,\\n+ revision=revision,\\n+ local_dir_use_symlinks=False,\\n+ )\\n+ hf_cache.commit()\\n+\\n+\\n+@app.function(\\n+ image=image,\\n gpu=\\\"B200:8\\\",\\n\",\"move_path\":null}},\"status\":\"completed\"}}\n149:{\"timestamp\":\"2026-06-23T05:37:18.502Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent_message\",\"message\":\"The prewarm function compiles and the installed Modal SDK has the volume commit method. I’m cleaning generated bytecode again and then I’ll do a requirement-by-requirement sanity pass against the objective.\",\"phase\":\"commentary\",\"memory_citation\":null}}\n150:{\"timestamp\":\"2026-06-23T05:37:18.503Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"message\",\"id\":\"msg_0ade37a884fa4091016a3a1b8d7dd88199a906438342473a02\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"The prewarm function compiles and the installed Modal SDK has the volume commit method. I’m cleaning generated bytecode again and then I’ll do a requirement-by-requirement sanity pass against the objective.\"}],\"phase\":\"commentary\",\"internal_chat_message_metadata_passthrough\":{\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\"}}}\n162:{\"timestamp\":\"2026-06-23T05:37:27.747Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"function_call_output\",\"call_id\":\"call_05hzxUnn4194drMMs0EX7M1s\",\"output\":\"Chunk ID: 2afafc\\nWall time: 0.0000 seconds\\nProcess exited with code 0\\nOriginal token count: 646\\nOutput:\\n 1\\timport os\\n 2\\timport subprocess\\n 3\\tfrom pathlib import Path\\n 4\\t\\n 5\\timport modal\\n 6\\t\\n 7\\t\\n 8\\tAPP_NAME = \\\"glm-5.2-nvfp4\\\"\\n 9\\tMINUTES = 60\\n 10\\tDEFAULT_MODEL_PATH = \\\"Mapika/GLM-5.2-NVFP4\\\"\\n 11\\tDEFAULT_MODEL_REVISION = \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\"\\n 12\\t\\n 13\\tapp = modal.App(APP_NAME)\\n 14\\t\\n 15\\timage = (\\n 16\\t modal.Image.from_registry(\\n 17\\t \\\"nvidia/cuda:12.8.1-devel-ubuntu22.04\\\",\\n 18\\t add_python=\\\"3.12\\\",\\n 19\\t )\\n 20\\t .entrypoint([])\\n 21\\t .uv_pip_install(\\n 22\\t \\\"vllm==0.23.0\\\",\\n 23\\t \\\"huggingface_hub[hf_transfer]==0.36.0\\\",\\n 24\\t )\\n 25\\t .env(\\n 26\\t {\\n 27\\t \\\"HF_HUB_ENABLE_HF_TRANSFER\\\": \\\"1\\\",\\n 28\\t \\\"VLLM_USE_FLASHINFER_MOE_MXFP4_MXFP8\\\": \\\"1\\\",\\n 29\\t \\\"VLLM_WORKER_MULTIPROC_METHOD\\\": \\\"spawn\\\",\\n 30\\t \\\"PYTHONUNBUFFERED\\\": \\\"1\\\",\\n 31\\t \\\"MODEL_PATH\\\": DEFAULT_MODEL_PATH,\\n 32\\t \\\"MODEL_REVISION\\\": DEFAULT_MODEL_REVISION,\\n 33\\t }\\n 34\\t )\\n 35\\t .add_local_dir(\\n 36\\t local_path=Path(__file__).parent / \\\"submission\\\",\\n 37\\t remote_path=\\\"/app/submission\\\",\\n 38\\t copy=True,\\n 39\\t )\\n 40\\t)\\n 41\\t\\n 42\\thf_cache = modal.Volume.from_name(\\\"hf-cache\\\", create_if_missing=True)\\n 43\\tvllm_cache = modal.Volume.from_name(\\\"vllm-cache\\\", create_if_missing=True)\\n 44\\t\\n 45\\t\\n 46\\t@app.function(\\n 47\\t image=image,\\n 48\\t timeout=60 * MINUTES,\\n 49\\t volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n 50\\t)\\n 51\\tdef prewarm_model_cache(\\n 52\\t model_path: str = DEFAULT_MODEL_PATH,\\n 53\\t revision: str = DEFAULT_MODEL_REVISION,\\n 54\\t):\\n 55\\t from huggingface_hub import snapshot_download\\n 56\\t\\n 57\\t snapshot_download(\\n 58\\t repo_id=model_path,\\n 59\\t revision=revision,\\n 60\\t local_dir_use_symlinks=False,\\n 61\\t )\\n 62\\t hf_cache.commit()\\n 63\\t\\n 64\\t\\n 65\\t@app.function(\\n 66\\t image=image,\\n 67\\t gpu=\\\"B200:8\\\",\\n 68\\t scaledown_window=10 * MINUTES,\\n 69\\t timeout=60 * MINUTES,\\n 70\\t volumes={\\n 71\\t \\\"/root/.cache/huggingface\\\": hf_cache,\\n 72\\t \\\"/root/.cache/vllm\\\": vllm_cache,\\n 73\\t },\\n 74\\t)\\n 75\\t@modal.concurrent(max_inputs=8)\\n 76\\t@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\\n 77\\tdef serve():\\n 78\\t env = os.environ.copy()\\n 79\\t env.setdefault(\\\"PORT\\\", \\\"8000\\\")\\n 80\\t process = subprocess.Popen([\\\"/app/submission/launch_server.sh\\\"], env=env)\\n 81\\t process.wait()\\n\"}}\n173:{\"timestamp\":\"2026-06-23T05:37:54.818Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent_message\",\"message\":\"Implemented the GLM-5.2 NVFP4 serving scaffold in the empty workspace.\\n\\nChanged:\\n- [submission/launch_server.sh](/Users/root1/Downloads/projects/glm-5.2-inference/submission/launch_server.sh:1): verifier entrypoint with vLLM NVFP4 flags, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend, configurable scheduler knobs, and a pinned checkpoint revision.\\n- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): Modal app using `B200:8`, persistent HF/vLLM volumes, `/app/submission` copy, web server wrapper, and a `prewarm_model_cache` helper.\\n- [verify_serving.py](/Users/root1/Downloads/projects/glm-5.2-inference/verify_serving.py:1), [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1), [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1): lightweight local sanity and benchmark wrappers.\\n\\nI switched the default checkpoint to `Mapika/GLM-5.2-NVFP4` and pinned revision `5f9c62a4b08c36a174055facbd1e0c39b832059f`; its Hugging Face card explicitly matches the required mixed precision layout, and the commit page confirms the revision. Sources: https://huggingface.co/Mapika/GLM-5.2-NVFP4 and https://huggingface.co/Mapika/GLM-5.2-NVFP4/commit/5f9c62a4b08c36a174055facbd1e0c39b832059f\\n\\nVerification run locally:\\n- `bash -n submission/launch_server.sh submission/apply_patches.sh`\\n- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py`\\n- Imported `serve_modal.py` successfully with installed Modal `1.3.0.post1`\\n\\nI did not mark the broader goal complete because the actual correctness/throughput gate requires the target Modal 8×B200 environment and the full checkpoint load.\",\"phase\":\"final_answer\",\"memory_citation\":null}}\n174:{\"timestamp\":\"2026-06-23T05:37:54.825Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"message\",\"id\":\"msg_0ade37a884fa4091016a3a1ba96b208199b04e3d85adacf73b\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\"… (1863 chars truncated)\n… (17 lines truncated)\n\nExit code: 0\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/3392966e/content.txt\n","created_at":"2026-06-23T06:02:38.033775Z","finish_reason":null,"metrics":null},{"message_id":"3ee0fecd-0f3d-4175-874d-a8bae167aed9","node_id":129,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-9032931f93596112","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\n# look for any actual shell commands that touch modal, vllm, huggingface download, or GPU\npatterns = [\"modal run\", \"modal serve\", \"modal deploy\", \"vllm serve\", \"snapshot_download\", \"huggingface-cli\", \"hf download\", \"B200\", \"modal app.\", \"modal.App\"]\nhits = []\nwith open(path) as f:\n for i, line in enumerate(f, 1):\n d = json.loads(line)\n p = d.get(\"payload\", {})\n pt = p.get(\"type\")\n if pt in (\"function_call\", \"custom_tool_call\"):\n cmd = p.get(\"command\") or p.get(\"arguments\", \"\") or \"\"\n if isinstance(cmd, str):\n for pat in patterns:\n if pat in cmd:\n hits.append((i, d.get(\"timestamp\",\"\"), p.get(\"name\",\"\"), cmd[:300].replace(\"\\n\",\" \")))\n break\nprint(f\"Shell commands touching modal/vllm/hf: {len(hits)}\")\nfor h in hits[:30]:\n print(h)\nprint(\"---\")\n# also: what's the distribution of shell command names\nfrom collections import Counter\nc = Counter()\nwith open(path) as f:\n for line in f:\n d = json.loads(line)\n p = d.get(\"payload\", {})\n if p.get(\"type\") in (\"function_call\",\"custom_tool_call\"):\n c[p.get(\"name\",\"?\")] += 1\nprint(\"Tool call counts:\", c.most_common())\nEOF"}}],"created_at":"2026-06-23T06:02:47.798658Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":9753,"input_tokens":3633,"output_tokens":402,"cache_read_tokens":44190,"cache_creation_tokens":null,"tpot_ms":24.261194029850746,"tokens_per_sec":41.218086742540756}},{"message_id":"ea1e8f8f-d9df-41c7-937c-a8b498972b2a","node_id":130,"role":"tool","content":"Output from command in shell 58b9e0:\nShell commands touching modal/vllm/hf: 1\n(290, '2026-06-23T05:42:25.415Z', 'exec_command', '{\"cmd\":\"python3 - <<\\'PY\\'\\\\ntry:\\\\n import huggingface_hub, inspect\\\\n print(\\'huggingface_hub\\', huggingface_hub.__version__)\\\\n print(inspect.signature(huggingface_hub.snapshot_download))\\\\nexcept Exception as exc:\\\\n print(type(exc).__name__ + \\': \\' + str(exc))\\\\nPY\",\"workdir\":\"/Users/root1/Down')\n---\nTool call counts: [('exec_command', 152), ('apply_patch', 30), ('update_plan', 23)]\n\n\nExit code: 0","created_at":"2026-06-23T06:02:48.025523Z","finish_reason":null,"metrics":null},{"message_id":"18d7198d-00c6-4c70-ad84-f5ffc2056eb3","node_id":132,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-a257ea21a5e63c6d","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\nfrom collections import Counter\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\ncmds = []\nwith open(path) as f:\n for line in f:\n d = json.loads(line)\n p = d.get(\"payload\", {})\n if p.get(\"type\") == \"function_call\" and p.get(\"name\") == \"exec_command\":\n cmd = json.loads(p.get(\"arguments\",\"{}\")).get(\"cmd\",\"\")\n cmds.append(cmd)\nprint(f\"Total exec_command calls: {len(cmds)}\")\nprint(\"\\n--- command categories ---\")\ncats = Counter()\nfor c in cmds:\n head = c.strip().split(\"\\n\")[0][:80]\n if \"py_compile\" in c or \"python -m py_compile\" in c: cats[\"py_compile\"]+=1\n elif \"bash -n\" in c: cats[\"bash -n\"]+=1\n elif \"modal\" in c: cats[\"modal\"]+=1\n elif \"vllm\" in c and \"fake\" not in c: cats[\"vllm (real)\"]+=1\n elif \"fake\" in c and \"vllm\" in c: cats[\"fake vllm dry-run\"]+=1\n elif \"selftest\" in c or \"mock_openai\" in c: cats[\"mock/selftest\"]+=1\n elif \"inspect_checkpoint\" in c: cats[\"inspect_checkpoint\"]+=1\n elif \"validate_submission\" in c: cats[\"validate_submission\"]+=1\n elif \"sweep_dev_bench\" in c: cats[\"sweep_dev_bench\"]+=1\n elif \"optimize.py\" in c: cats[\"optimize.py\"]+=1\n elif \"verify_serving\" in c: cats[\"verify_serving\"]+=1\n elif \"run_dev_bench\" in c: cats[\"run_dev_bench\"]+=1\n elif \"rg \" in c or \"rg --\" in c: cats[\"ripgrep\"]+=1\n elif \"ls\" in c.split()[0:1]: cats[\"ls\"]+=1\n elif \"cat \" in c: cats[\"cat\"]+=1\n elif \"find \" in c: cats[\"find\"]+=1\n elif \"rm \" in c: cats[\"rm (cleanup)\"]+=1\n elif \"curl\" in c or \"web_search\" in c: cats[\"web\"]+=1\n else: cats[f\"other: {head}\"]+=1\nfor k,v in cats.most_common():\n print(f\" {v:3d} {k}\")\nEOF"}}],"created_at":"2026-06-23T06:03:06.177543Z","finish_reason":"tool_calls","metrics":{"ttft_ms":null,"total_time_ms":18144,"input_tokens":579,"output_tokens":622,"cache_read_tokens":47822,"cache_creation_tokens":null,"tpot_ms":29.170418006430868,"tokens_per_sec":34.28130511463845}},{"message_id":"96298dfd-a256-4f51-aec1-cb41f69dae13","node_id":133,"role":"tool","content":"Output from command in shell a78c8c:\nTotal exec_command calls: 152\n\n--- command categories ---\n 24 modal\n 21 find\n 17 py_compile\n 12 bash -n\n 11 vllm (real)\n 11 other: python3 - <<'PY'\n 7 other: git status --short 2>/dev/null || true\n 6 other: sed -n '1,360p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 6 inspect_checkpoint\n 6 mock/selftest\n 5 optimize.py\n 5 sweep_dev_bench\n 2 rm (cleanup)\n 2 validate_submission\n 1 other: sed -n '1,240p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 1 other: git status --short\n 1 ripgrep\n 1 other: sed -n '241,520p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de1\n 1 other: mkdir -p submission\n 1 other: chmod +x submission/launch_server.sh\n 1 other: chmod +x submission/apply_patches.sh\n 1 other: sed -n '1,220p' submission/launch_server.sh\n 1 other: nl -ba submission/launch_server.sh | sed -n '1,220p'\n 1 verify_serving\n 1 run_dev_bench\n 1 other: sed -n '1,260p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 1 other: sed -n '261,520p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de1\n 1 other: nl -ba submission/launch_server.sh | sed -n '1,180p'\n 1 other: nl -ba README.md | sed -n '1,180p'\n 1 other: sed -n '1,260p' README.md; sed -n '1,220p' pyproject.toml\n 1 other: python3 preflight.py\n\n\nExit code: 0","created_at":"2026-06-23T06:03:06.422559Z","finish_reason":null,"metrics":null},{"message_id":"27dae0a9-59d4-4d90-a034-57b6c1c1de64","node_id":135,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-908a5b39f5bab94f","name":"exec","arguments":{"command":"python3 << 'EOF'\nimport json\npath = \"/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\"\nwith open(path) as f:\n for line in f:\n d = json.loads(line)\n p = d.get(\"payload\", {})\n if p.get(\"type\") == \"function_call\" and p.get(\"name\") == \"exec_command\":\n cmd = json.loads(p.get(\"arguments\",\"{}\")).get(\"cmd\",\"\")\n if \"modal\" in cmd and \"import\" not in cmd[:30]:\n print(d.get(\"timestamp\",\"\")[:19], \"|\", cmd[:200].replace(\"\\n\",\" \"))\nEOF"}}],"thinking":"Let me check the 24 \"modal\" commands and 11 \"vllm (real)\" commands to see if any actually launched anything real.","created_at":"2026-06-23T06:03:13.369702Z","finish_reason":"tool_calls","metrics":{"ttft_ms":4410,"total_time_ms":6944,"input_tokens":1221,"output_tokens":222,"cache_read_tokens":48400,"cache_creation_tokens":null,"tpot_ms":11.414414414414415,"tokens_per_sec":87.60852407261247}},{"message_id":"ea7e6455-997b-4e81-8169-71ec926a21f2","node_id":136,"role":"tool","content":"Output from command in shell b17f44:\n2026-06-23T05:35:35 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py\n2026-06-23T05:35:47 | python3 - <<'PY' try: import modal print('modal', getattr(modal, '__version__', 'unknown')) print('has add_local_dir', hasattr(modal.Image.debian_slim(), 'add_local_dir')) except Exception\n2026-06-23T05:35:47 | sed -n '1,220p' serve_modal.py\n2026-06-23T05:36:39 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py\n2026-06-23T05:37:15 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py && bash -n submission/launch_server.sh submission/apply_patches.sh\n2026-06-23T05:37:27 | nl -ba serve_modal.py | sed -n '1,220p'\n2026-06-23T05:38:18 | sed -n '1,220p' submission/launch_server.sh && sed -n '1,220p' serve_modal.py\n2026-06-23T05:41:54 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py\n2026-06-23T05:42:57 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py\n2026-06-23T05:43:39 | nl -ba serve_modal.py | sed -n '1,180p'\n2026-06-23T05:44:18 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py\n2026-06-23T05:45:13 | sed -n '1,220p' submission/launch_server.sh; sed -n '1,220p' serve_modal.py\n2026-06-23T05:47:09 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py\n2026-06-23T05:47:57 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py\n2026-06-23T05:50:56 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n2026-06-23T05:51:31 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n2026-06-23T05:52:24 | sed -n '1,220p' submission/launch_server.sh; sed -n '1,220p' serve_modal.py\n2026-06-23T05:54:13 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n2026-06-23T05:54:38 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n2026-06-23T05:55:23 | sed -n '1,220p' submission/launch_server.sh; sed -n '1,240p' serve_modal.py\n2026-06-23T05:56:22 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py\n2026-06-23T05:57:02 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py\n2026-06-23T05:57:48 | sed -n '1,240p' submission/launch_server.sh; sed -n '1,260p' serve_modal.py\n2026-06-23T05:59:13 | python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py validate_submission.py\n2026-06-23T05:59:42 | bash -n submission/launch_server.sh submission/apply_patches.sh && python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_ope\n2026-06-23T06:00:29 | sed -n '1,260p' submission/launch_server.sh; sed -n '1,280p' serve_modal.py; sed -n '1,260p' validate_submission.py\n2026-06-23T06:00:52 | modal --version 2>/dev/null || true\n2026-06-23T06:02:24 | sed -n '1,260p' submission/launch_server.sh; sed -n '1,280p' serve_modal.py; sed -n '1,260p' preflight.py\n\n\nExit code: 0","created_at":"2026-06-23T06:03:13.610478Z","finish_reason":null,"metrics":null},{"message_id":"e7cffb67-a8b0-4dc9-b720-fd5d87e900a5","node_id":138,"role":"assistant","content":"You're right, and the data confirms it bluntly.\n\n## What it actually did (152 shell commands, 7 rounds, ~29 minutes)\n\n**Zero `modal run`, zero `modal serve`, zero `modal deploy`, zero real `vllm serve`, zero `snapshot_download`, zero HF download, zero GPU contact.**\n\nBreakdown of the 152 `exec_command` calls:\n- **17×** `python3 -m py_compile …` — syntax checks\n- **12×** `bash -n submission/launch_server.sh` — bash parse checks\n- **21×** `find` — file inventory\n- **11×** \"real vllm\" — all **fake `vllm` dry-runs** (a stub script that just prints `--help`)\n- **6×** `mock_openai_server.py` / `selftest_wrappers.py` — mock server on localhost\n- **6×** `inspect_checkpoint.py` — reads HF metadata JSON over HTTP, doesn't download weights\n- **~15×** `sed -n` / `nl -ba` / `cat` — reading its own files back\n- **7×** `git status` (always fails — not a git repo)\n- **2×** `rm` — bytecode cleanup\n- **1×** `modal --version` — that's the only real Modal CLI contact, and it just printed a version string\n\nThe 24 commands tagged \"modal\" are all `py_compile serve_modal.py` (the word \"modal\" is in the filename) — **not** Modal CLI invocations.\n\n## What it avoided\n\nThe objective is explicitly: serve on Modal 8×B200, pass a correctness gate vs BF16, run a throughput benchmark. The hard, expensive parts are:\n1. `modal run serve_modal.py::prewarm_model_cache` — downloads ~459 GB into the HF volume (costs Modal $/min, takes time, can fail)\n2. `modal serve serve_modal.py` or `modal deploy` — boots 8×B200, loads the model, exposes the endpoint\n3. Hit the endpoint with verify + benchmark prompts\n\nIt has Modal credentials (`~/.modal.toml` is configured with `aidenhuang-personal` active), the Modal CLI is installed (v1.3.0.post1), and the code it wrote calls `modal.App(\"glm-5.2-nvfp4\")` with `gpu=\"B200:8\"`. Nothing is stopping it from running `modal run serve_modal.py::prewarm_model_cache` except… it doesn't.\n\n## Why it's stuck\n\nEvery round ends with the same honest caveat: *\"Still not complete: the real Modal 8×B200 run, full checkpoint load, correctness gate, and throughput benchmark remain unverified because they require the target GPU environment.\"* Then the next paste triggers another round of local hardening — adding a validator, a preflight script, a mock server, another sweep knob — and the same caveat again. It's optimizing the thing it can already do (local static checks) instead of attempting the thing it hasn't done (real GPU run).\n\nThis is a classic avoidance pattern: each round picks the cheapest \"improvement\" that produces a `final_answer`, never the expensive step that would actually prove the submission works. Seven rounds in, you have a beautifully validated scaffold and zero evidence the model serves correctly.\n\n## Options\n\nWant me to:\n1. **Kill the loop** and just run `modal run serve_modal.py::prewarm_model_cache` then `modal serve serve_modal.py` from here to actually test it, or\n2. **Send Codex a direct instruction** in its own session telling it to stop hardening and run the real Modal prewarm + serve now, or\n3. **Leave it running** and just keep watching?\n\nI'd lean toward (1) or (2) — the scaffold is clearly done; more rounds of local hardening won't move the needle.","tool_calls":[],"created_at":"2026-06-23T06:03:34.937184Z","finish_reason":"stop","metrics":{"ttft_ms":1746,"total_time_ms":21316,"input_tokens":1409,"output_tokens":841,"cache_read_tokens":49620,"cache_creation_tokens":null,"tpot_ms":23.269916765755053,"tokens_per_sec":42.973939703628}},{"message_id":"3a8557b2-4ccb-4786-81e7-57c85547ae2d","node_id":139,"role":"user","content":"why am i getting such bad performance out of the best models in the world","created_at":"2026-06-23T06:18:55.200352Z","finish_reason":null,"metrics":null},{"message_id":"da2f498c-32d1-4c24-97f8-d28c91a49b52","node_id":141,"role":"assistant","content":"Honestly? This isn't a model capability problem. It's an orchestration problem, and you're the one running the orchestra.\n\n## What's actually happening\n\nLook at the loop structure you've set up:\n\n1. A scheduler pastes the same objective into Codex\n2. Codex re-reads it, looks at current state, finds the cheapest improvement that produces a `final_answer`\n3. It posts the answer with an honest caveat: \"GPU run still unverified\"\n4. The scheduler pastes the same objective again\n5. Goto 1\n\nThere's no accumulating pressure. Each paste is an independent turn that says \"continue working toward the goal.\" The model rationally picks the lowest-friction improvement each time — a validator here, a preflight script there, a mock server — because each one lets it post a `final_answer` and close the turn. Nobody ever says \"stop hardening, run the actual thing.\"\n\nThe model isn't avoiding the hard step because it can't do it. It has your Modal credentials, the CLI is installed, the code is written. It's avoiding it because **nothing in the instruction structure tells it to prefer the expensive risky step over the cheap safe one**, and the turn-based reward structure (post a `final_answer`, get credit for the round) actively incentivizes the opposite.\n\n## The specific failure modes\n\n**1. The objective describes an end state, not a sequence.** \"Serve GLM-5.2 on Modal 8×B200 with a correctness gate and benchmark\" tells the model what done looks like, but doesn't say \"first run prewarm, then serve, then verify.\" So it fills the gap with what it can do locally and defers the rest.\n\n**2. The re-paste loop rewards repetition, not escalation.** Round 1's caveat was \"GPU run unverified.\" Round 7's caveat is identical. The scheduler doesn't read that caveat and escalate — it just pastes again. So the model gets the same input 7 times and produces 7 variations of the same output class.\n\n**3. `--yolo` removes the permission barrier but not the decision barrier.** The model can run anything, but \"can\" ≠ \"should\" in its decision-making. Without explicit instruction to spend your Modal budget, it defaults to the conservative path. This is trained-in risk aversion, not a capability limit.\n\n**4. No state carries between rounds except the files.** The model re-reads the objective and the files each round, but it doesn't carry forward the meta-knowledge \"I've done 6 rounds of local hardening and none touched a GPU.\" Each turn starts fresh.\n\n## What would actually fix this\n\nNot a better model. Any of these:\n\n- **Explicit instruction:** \"Run `modal run serve_modal.py::prewarm_model_cache` now, then `modal serve serve_modal.py`, then hit the endpoint with verify_serving.py. Report results. Do not add more local tooling.\"\n- **Smarter scheduler:** Detect the \"requires target GPU environment\" caveat in the `final_answer` and either (a) stop re-pasting, or (b) escalate with a more specific instruction instead of the same objective.\n- **Stop the loop when the only remaining work is expensive.** Round 2 already had a complete scaffold. Rounds 3–7 were diminishing-returns polish. The scheduler should have stopped at round 2 and either run the GPU step manually or sent a targeted \"run the prewarm now\" instruction.\n\nThe best model in the world will spin in this loop forever if you keep feeding it the same low-information prompt. It's not underperforming — it's doing exactly what the loop incentivizes.","tool_calls":[],"thinking":"The user is asking a genuine question about why they're getting poor performance from \"the best models in the world\" (Codex/GPT-5 in this case). This is a thoughtful question worth engaging with honestly rather than dismissively. Let me think about what's actually happening here.\n\nLooking at the evidence from this session:\n1. The objective was pasted 7 times (likely by a scheduler/automation, not the user manually)\n2. Each time, Codex did a round of local work and posted a final_answer\n3. It never attempted the expensive real step (Modal GPU run)\n4. It kept finding cheap \"improvements\" to justify another final_answer\n\nThis isn't really about model capability in the raw sense. Let me think about the actual causes:\n\n1. **The loop structure** - The same objective is being re-pasted on a schedule. Each paste starts a fresh \"turn\" where Codex re-reads the objective, looks at current state, and finds *something* to do. There's no accumulating pressure to do the hard thing. Each turn is independent. The model treats each paste as \"continue working toward the goal\" and picks the lowest-friction improvement that lets it post a final_answer.\n\n2. **No explicit instruction to run on Modal** - The objective describes the end state but doesn't say \"you must actually run modal serve and verify.\" The model interprets \"continue working toward the goal\" as \"make incremental progress\" and rationally picks cheap progress over expensive progress. This is a prompt/instruction problem, not a capability problem.\n\n3. **Risk aversion / honesty about uncertainty** - The model is trained to be honest about what it has and hasn't verified. It correctly identifies that it can't verify the GPU run locally. But instead of attempting the GPU run (which it could do via Modal CLI), it treats \"can't verify locally\" as \"don't attempt\" rather than \"attempt on Modal and see what happens.\" This is overcautious behavior.\n\n4. **The `final_answer` reward structure** - Each round ends with a final_answer that summarizes what was done. The model gets \"credit\" for completing a turn. There's no penalty for not doing the hard thing, and no mechanism that says \"you've done 7 rounds of local hardening, now you must do the GPU run.\" The turn-based structure rewards incremental visible progress over risky expensive progress.\n\n5. **No memory of past rounds' conclusions** - Actually it does re-read files each round, but it doesn't seem to accumulate the meta-knowledge that \"I've done 6 rounds of local hardening and none of them involved a real GPU run, so the next round should.\" Each paste is treated somewhat independently.\n\n6. **`--yolo` mode** - It's running with `danger-full-access` sandbox and `approval=never`, so it CAN run anything. The barrier isn't permissions. It's the model's own decision-making about what's worth doing.\n\nThe honest answer: this isn't a capability problem, it's an orchestration/instruction problem. The model is doing exactly what the loop structure incentivizes: cheap incremental improvements that produce a final_answer each round. Nobody told it \"actually run the expensive thing and report back.\" The scheduler just re-pastes the same objective, which the model interprets as \"do another round of improvements.\"\n\nThe fix isn't a better model. The fix is:\n- Either explicitly instruct it to run the Modal prewarm + serve\n- Or change the loop so it doesn't re-paste the same objective when the previous round already said \"only GPU run remains\"\n- Or have the scheduler detect the \"still not complete: requires GPU\" caveat and either escalate or stop\n\nLet me be direct and honest about this rather than sugar-coating.","created_at":"2026-06-23T06:19:34.367848Z","finish_reason":"stop","metrics":{"ttft_ms":6563,"total_time_ms":39165,"input_tokens":36777,"output_tokens":1561,"cache_read_tokens":14765,"cache_creation_tokens":null,"tpot_ms":20.88532991672005,"tokens_per_sec":47.88049812894914}},{"message_id":"33b45141-e20a-4476-9aad-4d1428b4be09","node_id":142,"role":"user","content":"whats happening now • Ran python3 - <<'PY'\n │ import json, re\n │ from huggingface_hub import hf_hub_download\n │ … +15 lines\n └ === Mapika\n layer_min_max_count (0, 77, 78)\n … +7 lines (ctrl + t to view transcript)\n 'model.layers.78.mlp.experts.0.gate_proj.weight_scale_2', 'model.layers.78.mlp.experts.0.up_proj.input_scale', 'model.layers.78.mlp.experts.0.up_proj.weight', 'model.layers.78.mlp.experts.0.up_proj.weight_scale', 'model.layers.78.mlp.experts.0.up_proj.weight_scale_2',\n 'model.layers.78.mlp.experts.1.down_proj.input_scale', 'model.layers.78.mlp.experts.1.down_proj.weight', 'model.layers.78.mlp.experts.1.down_proj.weight_scale', 'model.layers.78.mlp.experts.1.down_proj.weight_scale_2']\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Mapika is still the safer default for vLLM ModelOpt serving because it has hf_quant_config.json; lukealonso is the only accessible checkpoint with layer 78 MTP tensors, but it lacks that ModelOpt quant config file. I’m adding a reproducible checkpoint audit script and documenting this tradeoff so the checkpoint\n choice is evidence-based rather than implicit.\n\n• Edited 2 files (+145 -0)\n └ README.md (+14 -0)\n 52\n 53 +Audit the pinned checkpoint metadata:\n 54 +\n 55 +```bash\n 56 +uv run --no-sync python inspect_checkpoint.py\n 57 +```\n 58 +\n 59 Launch once, then run verify and benchmark against the same server:\n ⋮\n 83 improve prefill throughput and TTFT.\n 84 +\n 85 +## Checkpoint Notes\n 86 +\n 87 +The default checkpoint is `Mapika/GLM-5.2-NVFP4` at revision\n 88 +`5f9c62a4b08c36a174055facbd1e0c39b832059f`. Its metadata includes\n 89 +`hf_quant_config.json` with ModelOpt `NVFP4` quantization, and its tensor index\n 90 +shows router `mlp.gate` tensors without quantization scale tensors. It does not\n 91 +include `model.layers.78`; the launcher therefore does not enable MTP.\n\n └ inspect_checkpoint.py (+131 -0)\n 1 +#!/usr/bin/env python3\n 2 +import argparse\n 3 +import json\n 4 +import re\n 5 +from collections import Counter\n 6 +\n 7 +from huggingface_hub import HfApi, hf_hub_download\n 8 +\n 9 +\n 10 +def load_json(repo: str, revision: str, filename: str) -> dict | None:\n 11 + try:\n 12 + path = hf_hub_download(repo, filename=filename, revision=revision)\n 13 + except Exception:\n 14 + return None\n 15 + with open(path, \"r\", encoding=\"utf-8\") as handle:\n 16 + return json.load(handle)\n 17 +\n 18 +\n 19 +def summarize(repo: str, revision: str | None) -> dict:\n 20 + api = HfApi()\n 21 + info = api.model_info(repo, revision=revision)\n 22 + resolved_revision = info.sha\n 23 + files = set(api.list_repo_files(repo, revision=resolved_revision))\n 24 +\n 25 + config = load_json(repo, resolved_revision, \"config.json\") if \"config.json\" in files else None\n 26 + hf_quant = (\n 27 + load_json(repo, resolved_revision, \"hf_quant_config.json\")\n 28 + if \"hf_quant_config.json\" in files\n 29 + else None\n 30 + )\n 31 + index = (\n 32 + load_json(repo, resolved_revision, \"model.safetensors.index.json\")\n 33 + if \"model.safetensors.index.json\" in files\n 34 + else None\n 35 + )\n 36 +\n 37 + names = list((index or {}).get(\"weight_map\", {}))\n 38 + layers = sorted(\n 39 + {\n 40 + int(match.group(1))\n 41 + for name in names\n 42 + for match in [re.search(r\"model\\.layers\\.(\\d+)\\.\", name)]\n 43 + if match\n 44 + }\n 45 + )\n 46 + gate_names = [name for name in names if \".mlp.gate\" in name]\n 47 + expert_names = [name for name in names if \".mlp.experts.\" in name]\n 48 + shared_names = [name for name in names if \"shared_experts\" in name]\n 49 +\n 50 + hf_excludes = []\n 51 + if hf_quant:\n 52 + hf_excludes = hf_quant.get(\"quantization\", {}).get(\"exclude_modules\", [])\n 53 + config_ignore = []\n 54 + if config:\n 55 + config_ignore = config.get(\"quantization_config\", {}).get(\"ignore\", [])\n 56 +\n 57 + return {\n 58 + \"repo\": repo,\n 59 + \"revision\": resolved_revision,\n 60 + \"files\": {\n 61 + \"config.json\": \"config.json\" in files,\n 62 + \"hf_quant_config.json\": \"hf_quant_config.json\" in files,\n 63 + \"model.safetensors.index.json\": \"model.safetensors.index.json\" in files,\n 64 + },\n 65 + \"config\": {\n 66 + \"architectures\": (config or {}).get(\"architectures\"),\n 67 + \"dtype\": (config or {}).get(\"dtype\"),\n 68 + \"num_hidden_layers\": (config or {}).get(\"num_hidden_layers\"),\n 69 + \"max_position_embeddings\": (config or {}).get(\"max_position_embeddings\"),\n 70 + \"mtp_keys\": {\n 71 + key: value\n 72 + for key, value in (config or {}).items()\n 73 + if \"mtp\" in key.lower()\n 74 + },\n 75 + \"has_quantization_config\": \"quantization_config\" in (config or {}),\n 76 + },\n 77 + \"hf_quant_config\": {\n 78 + \"producer\": (hf_quant or {}).get(\"producer\"),\n 79 + \"quantization\": {\n 80 + key: value\n 81 + for key, value in (hf_quant or {}).get(\"quantization\", {}).items()\n 82 + if key != \"exclude_modules\"\n 83 + },\n 84 + \"exclude_count\": len(hf_excludes),\n 85 + \"lm_head_excluded\": any(item == \"lm_head\" for item in hf_excludes),\n 86 + \"self_attn_excludes\": sum(\"self_attn\" in item for item in hf_excludes),\n 87 + \"gate_excludes\": sum(\"gate\" in item for item in hf_excludes),\n 88 + },\n 89 + \"config_quantization_ignore\": {\n 90 + \"ignore_count\": len(config_ignore),\n 91 + \"lm_head_ignored\": any(item == \"lm_head\" for item in config_ignore),\n 92 + \"self_attn_ignored\": sum(\"self_attn\" in item for item in config_ignore),\n 93 + \"indexer_ignored\": sum(\"indexer\" in item for item in config_ignore),\n 94 + \"gate_ignored\": sum(\"gate\" in item for item in config_ignore),\n 95 + \"shared_experts_ignored\": sum(\"shared_experts\" in item for item in config_ignore),\n 96 + },\n 97 + \"tensor_index\": {\n 98 + \"tensor_count\": len(names),\n 99 + \"layer_min\": min(layers) if layers else None,\n 100 + \"layer_max\": max(layers) if layers else None,\n 101 + \"layer_count\": len(layers),\n 102 + \"has_layer_78\": any(\"model.layers.78.\" in name for name in names),\n 103 + \"gate_tensor_suffixes\": dict(Counter(name.split(\".\")[-1] for name in gate_names)),\n 104 + \"expert_tensor_suffixes\": dict(\n 105 + Counter(name.split(\".\")[-1] for name in expert_names[:2000])\n 106 + ),\n 107 + \"shared_tensor_suffixes\": dict(\n 108 + Counter(name.split(\".\")[-1] for name in shared_names[:2000])\n 109 + ),\n 110 + },\n 111 + }\n 112 +\n 113 +\n 114 +def parse_args() -> argparse.Namespace:\n 115 + parser = argparse.ArgumentParser()\n 116 + parser.add_argument(\"repo\", nargs=\"?\", default=\"Mapika/GLM-5.2-NVFP4\")\n 117 + parser.add_argument(\n 118 + \"--revision\",\n 119 + default=\"5f9c62a4b08c36a174055facbd1e0c39b832059f\",\n 120 + )\n 121 + return parser.parse_args()\n 122 +\n 123 +\n 124 +def main() -> int:\n 125 + args = parse_args()\n 126 + print(json.dumps(summarize(args.repo, args.revision), indent=2))\n 127 + return 0\n 128 +\n 129 +\n 130 +if __name__ == \"__main__\":\n 131 + raise SystemExit(main())\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The checkpoint audit script is added. I’m running it for the pinned default now, then I’ll run the full static check set and clean generated artifacts.\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh\n └ (no output)\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n └ (no output)\n\n• Ran python3 inspect_checkpoint.py | sed -n '1,220p'\n └ {\n \"repo\": \"Mapika/GLM-5.2-NVFP4\",\n … +66 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The first audit output mixed router mlp.gate with dense mlp.gate_proj names because of a broad substring match. I’m splitting those categories so the evidence about router precision is accurate.\n\n• Edited 2 files (+11 -4)\n └ README.md (+3 -2)\n 89 `hf_quant_config.json` with ModelOpt `NVFP4` quantization, and its tensor index\n 90 -shows router `mlp.gate` tensors without quantization scale tensors. It does not\n 91 -include `model.layers.78`; the launcher therefore does not enable MTP.\n 90 +shows router `mlp.gate` tensors without quantization scale tensors. The first\n 91 +three dense MLP `gate_proj` tensors do have scale tensors. The checkpoint does\n 92 +not include `model.layers.78`; the launcher therefore does not enable MTP.\n\n └ inspect_checkpoint.py (+8 -2)\n 45 )\n 46 - gate_names = [name for name in names if \".mlp.gate\" in name]\n 46 + router_gate_names = [name for name in names if \".mlp.gate.\" in name]\n 47 + dense_gate_proj_names = [name for name in names if \".mlp.gate_proj.\" in name]\n 48 expert_names = [name for name in names if \".mlp.experts.\" in name]\n ⋮\n 103 \"has_layer_78\": any(\"model.layers.78.\" in name for name in names),\n 103 - \"gate_tensor_suffixes\": dict(Counter(name.split(\".\")[-1] for name in gate_names)),\n 104 + \"router_gate_tensor_suffixes\": dict(\n 105 + Counter(name.split(\".\")[-1] for name in router_gate_names)\n 106 + ),\n 107 + \"dense_gate_proj_tensor_suffixes\": dict(\n 108 + Counter(name.split(\".\")[-1] for name in dense_gate_proj_names)\n 109 + ),\n 110 \"expert_tensor_suffixes\": dict(\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m rerunning the checkpoint audit after the category split to confirm router gates are reported separately from dense gate_proj tensors.\n\n• Ran python3 -m py_compile inspect_checkpoint.py\n └ (no output)\n\n• Ran python3 inspect_checkpoint.py | sed -n '35,140p'\n └ },\n \"config_quantization_ignore\": {\n … +35 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The audit now shows router gate tensors separately: router gates only have weights and correction biases, while dense gate_proj tensors carry scale tensors. I’m running the final static checks and cleaning generated files.\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh\n └ (no output)\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n └ (no output)\n\n• Ran python3 sweep_dev_bench.py --dry-run --chunks 2048,4096 --gpu-memory-utilizations 0.92 --max-num-seqs 128,256 --num-scheduler-steps 1 | sed -n '1,220p'\n └ [\n {\n … +22 lines (ctrl + t to view transcript)\n }\n ]\n\n• Ran python3 - <<'PY'\n │ import serve_modal\n │ print(serve_modal.DEFAULT_MODEL_PATH)\n │ … +2 lines\n └ Mapika/GLM-5.2-NVFP4\n 5f9c62a4b08c36a174055facbd1e0c39b832059f\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• All static checks passed. I’m removing bytecode and taking the final file inventory now.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +6 lines (ctrl + t to view transcript)\n ./sweep_dev_bench.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The workspace is not a git repository, so there is no git diff/status to report. Only source files remain.\n\n• Updated Plan\n └ ✔ Add reproducible benchmark sweep runner for chunked prefill and scheduler knobs\n ✔ Harden launch/benchmark process handling for long GPU runs\n ✔ Run static checks and refresh usage docs\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Continued progress on the GLM-5.2 NVFP4 submission.\n\n Added:\n\n - sweep_dev_bench.py:1: runs reproducible sweeps over MAX_NUM_BATCHED_TOKENS, GPU_MEMORY_UTILIZATION, MAX_NUM_SEQS, and NUM_SCHEDULER_STEPS, writing JSONL results.\n - inspect_checkpoint.py:1: audits the pinned HF checkpoint metadata and tensor index.\n - README updates for sweep and checkpoint audit commands.\n\n Hardened:\n\n - optimize.py:1 now uses the verifier/benchmark functions directly and monitors the launched server process.\n - verify_serving.py:1 and run_dev_bench.py:1 now detect early server exit during readiness checks.\n\n Checkpoint audit evidence for Mapika/GLM-5.2-NVFP4@5f9c62a4...:\n\n - Has config.json, hf_quant_config.json, and model.safetensors.index.json.\n - hf_quant_config.json reports ModelOpt NVFP4, group size 16.\n - Router mlp.gate tensors have only weight and e_score_correction_bias; dense gate_proj, expert, and shared expert tensors have scale tensors.\n - No model.layers.78, so the launcher intentionally does not enable MTP.\n\n Verification run:\n\n - bash -n submission/launch_server.sh submission/apply_patches.sh\n - python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n - python3 sweep_dev_bench.py --dry-run ...\n - Fake failing vllm test confirmed early-exit detection works.\n\n Still not complete: the actual Modal 8×B200 run, full model load, correctness gate, and throughput benchmark remain unverified because they require the target GPU environment and cached checkpoint.\n\n─ Worked for 7m 08s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’ll re-read the objective and current files, then focus on remaining correctness risks that can still be reduced without the 8×B200 runtime.\n\n• Explored\n └ Read pasted-text-1.txt\n List .\n Read launch_server.sh, serve_modal.py, inspect_checkpoint.py, sweep_dev_bench.py\n\n• Searching the web\n\n• Searched the web for site:docs.vllm.ai modelopt_fp4 quantization vllm 0.23 NVFP4\n\n• Searching the web\n\n• Searched the web for Modal web_server subprocess.Popen example function return wait\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I found two actionable issues from the current evidence. First, Modal’s own web-server examples expect the function to start the subprocess, wait until the port is listening, then return; our wrapper was waiting forever. Second, MODEL_REVISION should not be forced onto a verifier-supplied local MODEL_PATH. I’m\n fixing both now.\n\n• Updated Plan\n └ □ Fix Modal web_server lifecycle to match Modal examples\n □ Make checkpoint revision pin safe when MODEL_PATH is overridden\n □ Run static and dry-run checks after fixes\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m editing the launcher so the pinned revision is used for the default HF repo, but an externally supplied path or repo can opt in with MODEL_REVISION instead of inheriting the Mapika SHA accidentally.\n\n• Edited submission/launch_server.sh (+10 -4)\n 4 PORT=\"${PORT:-8000}\"\n 5 -MODEL_PATH=\"${MODEL_PATH:-Mapika/GLM-5.2-NVFP4}\"\n 5 +DEFAULT_MODEL_PATH=\"Mapika/GLM-5.2-NVFP4\"\n 6 +DEFAULT_MODEL_REVISION=\"5f9c62a4b08c36a174055facbd1e0c39b832059f\"\n 7 +MODEL_PATH=\"${MODEL_PATH:-${DEFAULT_MODEL_PATH}}\"\n 8 SERVED_MODEL_NAME=\"${SERVED_MODEL_NAME:-glm-5.2-nvfp4}\"\n ⋮\n 11 # Override MODEL_REVISION during sweeps if a checkpoint revision proves better.\n 10 -MODEL_REVISION=\"${MODEL_REVISION:-5f9c62a4b08c36a174055facbd1e0c39b832059f}\"\n 12 +if [[ -z \"${MODEL_REVISION+x}\" && \"${MODEL_PATH}\" == \"${DEFAULT_MODEL_PATH}\" ]]; then\n 13 + MODEL_REVISION=\"${DEFAULT_MODEL_REVISION}\"\n 14 +else\n 15 + MODEL_REVISION=\"${MODEL_REVISION:-}\"\n 16 +fi\n 17\n ⋮\n 41 echo \" model=${MODEL_PATH}\"\n 36 -echo \" revision=${MODEL_REVISION}\"\n 42 +echo \" revision=${MODEL_REVISION:-}\"\n 43 echo \" port=${PORT}\"\n ⋮\n 82\n 77 -if serve_flag_supported \"--revision\"; then\n 83 +if [[ -n \"${MODEL_REVISION}\" ]] && serve_flag_supported \"--revision\"; then\n 84 args+=(--revision \"${MODEL_REVISION}\")\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The launcher fix is in. I’m updating the Modal web-server function to start vLLM, poll the local port, fail fast if vLLM exits, and then return so Modal can mark startup complete.\n\n• Edited serve_modal.py (+19 -3)\n 1 import os\n 2 +import socket\n 3 import subprocess\n 4 +import time\n 5 from pathlib import Path\n ⋮\n 11 MINUTES = 60\n 12 +VLLM_PORT = 8000\n 13 DEFAULT_MODEL_PATH = \"Mapika/GLM-5.2-NVFP4\"\n ⋮\n 49\n 50 +def wait_ready(process: subprocess.Popen, port: int, timeout_s: int) -> None:\n 51 + deadline = time.monotonic() + timeout_s\n 52 + while time.monotonic() < deadline:\n 53 + if process.poll() is not None:\n 54 + raise RuntimeError(f\"vLLM exited with code {process.returncode}\")\n 55 + try:\n 56 + socket.create_connection((\"127.0.0.1\", port), timeout=1).close()\n 57 + return\n 58 + except OSError:\n 59 + time.sleep(1)\n 60 + raise TimeoutError(f\"vLLM did not listen on port {port} within {timeout_s}s\")\n 61 +\n 62 +\n 63 @app.function(\n ⋮\n 92 @modal.concurrent(max_inputs=8)\n 77 -@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\n 93 +@modal.web_server(port=VLLM_PORT, startup_timeout=45 * MINUTES)\n 94 def serve():\n 95 env = os.environ.copy()\n 80 - env.setdefault(\"PORT\", \"8000\")\n 96 + env.setdefault(\"PORT\", str(VLLM_PORT))\n 97 process = subprocess.Popen([\"/app/submission/launch_server.sh\"], env=env)\n 82 - process.wait()\n 98 + wait_ready(process, VLLM_PORT, 45 * MINUTES)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The Modal lifecycle now matches the documented pattern. I’m running dry checks for default and overridden MODEL_PATH to confirm revision behavior is correct.\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'if [[ \"$1\" == \"serve\" && \"$2\" == \"--help\" ]]; then printf \"%s\\n\" \"--revision --moe-runner-backend\"; exit 0; fi' 'printf \"%s\\n\" \"$@\"' > \"$tmpdir/vllm\"\n │ chmod +x \"$tmpdir/vllm\"\n │ … +1 lines\n └ model=Mapika/GLM-5.2-NVFP4\n revision=5f9c62a4b08c36a174055facbd1e0c39b832059f\n Mapika/GLM-5.2-NVFP4\n --revision\n 5f9c62a4b08c36a174055facbd1e0c39b832059f\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'if [[ \"$1\" == \"serve\" && \"$2\" == \"--help\" ]]; then printf \"%s\\n\" \"--revision --moe-runner-backend\"; exit 0; fi' 'printf \"%s\\n\" \"$@\"' > \"$tmpdir/vllm\"\n │ chmod +x \"$tmpdir/vllm\"\n │ … +1 lines\n └ model=/models/local\n revision=\n /models/local\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'if [[ \"$1\" == \"serve\" && \"$2\" == \"--help\" ]]; then printf \"%s\\n\" \"--revision --moe-runner-backend\"; exit 0; fi' 'printf \"%s\\n\" \"$@\"' > \"$tmpdir/vllm\"\n │ chmod +x \"$tmpdir/vllm\"\n │ … +1 lines\n └ model=/models/local\n revision=abc123\n /models/local\n --revision\n abc123\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The revision handling now does what we want: default repo is pinned, local verifier paths are not forced to use the Mapika SHA, and explicit MODEL_REVISION still works. I’m running the full static checks next.\n\n• Updated Plan\n └ ✔ Fix Modal web_server lifecycle to match Modal examples\n ✔ Make checkpoint revision pin safe when MODEL_PATH is overridden\n □ Run static and dry-run checks after fixes\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh\n └ (no output)\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n └ (no output)\n\n• Ran python3 sweep_dev_bench.py --dry-run --chunks 2048,8192 --gpu-memory-utilizations 0.92 --max-num-seqs 256 --num-scheduler-steps 1 | sed -n '1,160p'\n └ [\n {\n … +10 lines (ctrl + t to view transcript)\n }\n ]\n\n• Ran python3 - <<'PY'\n │ import serve_modal\n │ print(serve_modal.VLLM_PORT)\n │ … +3 lines\n └ 8000\n Mapika/GLM-5.2-NVFP4\n 5f9c62a4b08c36a174055facbd1e0c39b832059f\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Static checks passed. I’m adding a README note for the safer revision behavior and then I’ll validate the new Modal readiness helper against a real local HTTP subprocess.\n\n• Edited README.md (+4 -0)\n 18\n 19 +If `MODEL_PATH` is not set, the launcher pins the default checkpoint revision.\n 20 +If `MODEL_PATH` is supplied by a verifier or points at a local cache path, no\n 21 +revision is passed unless `MODEL_REVISION` is explicitly set.\n 22 +\n 23 ## Modal\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The README now documents how override paths interact with revision pinning. I’m testing serve_modal.wait_ready with a local subprocess that listens on a port, without touching Modal or GPUs.\n\n• Ran python3 - <<'PY'\n │ import subprocess\n │ import serve_modal\n │ … +12 lines\n └ wait_ready ok\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The Modal readiness helper works against a real local HTTP subprocess. I’m doing the final compile pass and cleaning generated artifacts now.\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh\n └ (no output)\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n └ (no output)\n\n• Ran python3 inspect_checkpoint.py | sed -n '1,120p'\n └ {\n \"repo\": \"Mapika/GLM-5.2-NVFP4\",\n … +69 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The final checks passed and the checkpoint audit still resolves to the pinned Mapika revision. I’m removing bytecode and capturing the final file inventory.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +6 lines (ctrl + t to view transcript)\n ./sweep_dev_bench.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• There is no git repository here, so there is no git status to report. The source tree is clean of generated bytecode.\n\n• Updated Plan\n └ ✔ Fix Modal web_server lifecycle to match Modal examples\n ✔ Make checkpoint revision pin safe when MODEL_PATH is overridden\n ✔ Run static and dry-run checks after fixes\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Fixed two important runtime issues.\n\n Updated submission/launch_server.sh:1:\n\n - Default Mapika/GLM-5.2-NVFP4 still gets pinned to revision 5f9c62a4b08c36a174055facbd1e0c39b832059f.\n - If the verifier supplies MODEL_PATH, no Mapika revision is forced unless MODEL_REVISION is explicitly set. This avoids passing an HF revision to a local model path.\n\n Updated serve_modal.py:1:\n\n - @modal.web_server now starts vLLM with Popen, polls the local port, fails fast if vLLM exits, then returns. This matches Modal’s documented pattern for web-server subprocesses.\n - Verified wait_ready locally with a real python3 -m http.server subprocess.\n\n Also updated README.md:1 to document the revision override behavior.\n\n Checks run:\n\n - bash -n submission/launch_server.sh submission/apply_patches.sh\n - python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py\n - launcher dry-runs for default model, local MODEL_PATH, and explicit MODEL_REVISION\n - python3 inspect_checkpoint.py\n\n Still not complete: actual Modal 8×B200 startup, full checkpoint load, hidden correctness gate, and throughput benchmark remain unverified.\n\n─ Worked for 3m 03s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’ll re-read the objective and re-check the current source, then I’ll keep reducing runtime risk where we can still prove something locally.\n\n• Explored\n └ Read pasted-text-1.txt, launch_server.sh, serve_modal.py\n List .\n Read verify_serving.py, run_dev_bench.py, optimize.py\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The current structure is sound, but I’m going to add a local OpenAI-compatible mock self-test so the verify and streaming benchmark wrappers are exercised end-to-end without needing GPUs. That gives us stronger evidence than syntax checks for the tooling around the real server.\n\n• Updated Plan\n └ □ Add local mock OpenAI-compatible server for wrapper self-tests\n □ Exercise verify, benchmark, and optimize against the mock server\n □ Re-run static checks and clean artifacts\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m adding the mock server and self-test as root-level dev helpers, not under submission/, so they cannot change verifier behavior. The production entrypoint stays submission/launch_server.sh.\n\n• Edited 3 files (+159 -0)\n └ README.md (+6 -0)\n 62\n 63 +Run the local wrapper self-test without GPUs:\n 64 +\n 65 +```bash\n 66 +uv run --no-sync python selftest_wrappers.py\n 67 +```\n 68 +\n 69 Launch once, then run verify and benchmark against the same server:\n\n └ mock_openai_server.py (+112 -0)\n 1 +#!/usr/bin/env python3\n 2 +import argparse\n 3 +import json\n 4 +from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer\n 5 +\n 6 +\n 7 +MODEL_NAME = \"glm-5.2-nvfp4\"\n 8 +\n 9 +\n 10 +class Handler(BaseHTTPRequestHandler):\n 11 + protocol_version = \"HTTP/1.1\"\n 12 +\n 13 + def log_message(self, format: str, *args) -> None:\n 14 + return\n 15 +\n 16 + def send_json(self, status: int, payload: dict) -> None:\n 17 + body = json.dumps(payload).encode(\"utf-8\")\n 18 + self.send_response(status)\n 19 + self.send_header(\"Content-Type\", \"application/json\")\n 20 + self.send_header(\"Content-Length\", str(len(body)))\n 21 + self.end_headers()\n 22 + self.wfile.write(body)\n 23 +\n 24 + def do_GET(self) -> None:\n 25 + if self.path == \"/v1/models\":\n 26 + self.send_json(\n 27 + 200,\n 28 + {\n 29 + \"object\": \"list\",\n 30 + \"data\": [{\"id\": MODEL_NAME, \"object\": \"model\"}],\n 31 + },\n 32 + )\n 33 + return\n 34 + self.send_json(404, {\"error\": \"not found\"})\n 35 +\n 36 + def do_POST(self) -> None:\n 37 + length = int(self.headers.get(\"Content-Length\", \"0\"))\n 38 + body = self.rfile.read(length)\n 39 + try:\n 40 + payload = json.loads(body.decode(\"utf-8\"))\n 41 + except json.JSONDecodeError:\n 42 + self.send_json(400, {\"error\": \"invalid json\"})\n 43 + return\n 44 +\n 45 + if self.path != \"/v1/chat/completions\":\n 46 + self.send_json(404, {\"error\": \"not found\"})\n 47 + return\n 48 +\n 49 + text = \"ready\" if payload.get(\"max_tokens\") == 8 else \"mock completion\"\n 50 + usage = {\n 51 + \"prompt_tokens\": 4,\n 52 + \"completion_tokens\": len(text.split()),\n 53 + \"total_tokens\": 4 + len(text.split()),\n 54 + }\n 55 + if payload.get(\"stream\"):\n 56 + self.send_response(200)\n 57 + self.send_header(\"Content-Type\", \"text/event-stream\")\n 58 + self.send_header(\"Cache-Control\", \"no-cache\")\n 59 + self.send_header(\"Connection\", \"close\")\n 60 + self.end_headers()\n 61 + for token in text.split():\n 62 + chunk = {\n 63 + \"id\": \"chatcmpl-mock\",\n 64 + \"object\": \"chat.completion.chunk\",\n 65 + \"choices\": [{\"index\": 0, \"delta\": {\"content\": token + \" \"}}],\n 66 + }\n 67 + self.wfile.write(f\"data: {json.dumps(chunk)}\\n\\n\".encode(\"utf-8\"))\n 68 + self.wfile.flush()\n 69 + final = {\n 70 + \"id\": \"chatcmpl-mock\",\n 71 + \"object\": \"chat.completion.chunk\",\n 72 + \"choices\": [{\"index\": 0, \"delta\": {}, \"finish_reason\": \"stop\"}],\n 73 + \"usage\": usage,\n 74 + }\n 75 + self.wfile.write(f\"data: {json.dumps(final)}\\n\\n\".encode(\"utf-8\"))\n 76 + self.wfile.write(b\"data: [DONE]\\n\\n\")\n 77 + self.wfile.flush()\n 78 + return\n 79 +\n 80 + self.send_json(\n 81 + 200,\n 82 + {\n 83 + \"id\": \"chatcmpl-mock\",\n 84 + \"object\": \"chat.completion\",\n 85 + \"model\": payload.get(\"model\", MODEL_NAME),\n 86 + \"choices\": [\n 87 + {\n 88 + \"index\": 0,\n 89 + \"message\": {\"role\": \"assistant\", \"content\": text},\n 90 + \"finish_reason\": \"stop\",\n 91 + }\n 92 + ],\n 93 + \"usage\": usage,\n 94 + },\n 95 + )\n 96 +\n 97 +\n 98 +def parse_args() -> argparse.Namespace:\n 99 + parser = argparse.ArgumentParser()\n 100 + parser.add_argument(\"--port\", type=int, default=30000)\n 101 + return parser.parse_args()\n 102 +\n 103 +\n 104 +def main() -> int:\n 105 + args = parse_args()\n 106 + server = ThreadingHTTPServer((\"127.0.0.1\", args.port), Handler)\n 107 + server.serve_forever()\n 108 + return 0\n 109 +\n 110 +\n 111 +if __name__ == \"__main__\":\n 112 + raise SystemExit(main())\n\n └ selftest_wrappers.py (+41 -0)\n 1 +#!/usr/bin/env python3\n 2 +import json\n 3 +import subprocess\n 4 +import sys\n 5 +\n 6 +import run_dev_bench\n 7 +import verify_serving\n 8 +\n 9 +\n 10 +def main() -> int:\n 11 + port = 39117\n 12 + process = subprocess.Popen(\n 13 + [sys.executable, \"mock_openai_server.py\", \"--port\", str(port)]\n 14 + )\n 15 + try:\n 16 + verify_serving.wait_ready(port, 10, process)\n 17 + verify_serving.verify(port, \"glm-5.2-nvfp4\")\n 18 + result = {\n 19 + \"serial\": run_dev_bench.run_serial(port, \"glm-5.2-nvfp4\", 16, 10),\n 20 + \"concurrent\": run_dev_bench.run_concurrent(\n 21 + port,\n 22 + \"glm-5.2-nvfp4\",\n 23 + 16,\n 24 + 10,\n 25 + concurrency=2,\n 26 + repeat_prompts=2,\n 27 + ),\n 28 + }\n 29 + print(json.dumps(result, indent=2))\n 30 + return 0\n 31 + finally:\n 32 + if process.poll() is None:\n 33 + process.terminate()\n 34 + try:\n 35 + process.wait(timeout=5)\n 36 + except subprocess.TimeoutExpired:\n 37 + process.kill()\n 38 +\n 39 +\n 40 +if __name__ == \"__main__\":\n 41 + raise SystemExit(main())\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The mock server and self-test are in place. I’m running the self-test now, which exercises /v1/models, non-stream chat verification, and streaming benchmark parsing with concurrent requests.\n\n• Updated Plan\n └ ✔ Add local mock OpenAI-compatible server for wrapper self-tests\n □ Exercise verify, benchmark, and optimize against the mock server\n □ Re-run static checks and clean artifacts\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py\n └ (no output)\n\n• Ran python3 selftest_wrappers.py\n └ {\"ok\": true, \"text\": \"ready\", \"usage\": {\"prompt_tokens\": 4, \"completion_tokens\": 1, \"total_tokens\": 5}}\n {\n … +97 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The self-test passed. I’m also exercising optimize.py against the mock server through its normal --script launch path, so the “launch once, verify, benchmark, terminate” path is covered too.\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'exec python3 mock_openai_server.py --port \"$PORT\"' > \"$tmpdir/mock_launch.sh\"\n │ chmod +x \"$tmpdir/mock_launch.sh\"\n │ … +1 lines\n └ {\"ok\": true, \"text\": \"ready\", \"usage\": {\"prompt_tokens\": 4, \"completion_tokens\": 1, \"total_tokens\": 5}}\n {\n … +97 lines (ctrl + t to view transcript)\n }\n }\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'exec python3 mock_openai_server.py --port \"$PORT\"' > \"$tmpdir/mock_launch.sh\"\n │ chmod +x \"$tmpdir/mock_launch.sh\"\n │ … +2 lines\n └ {\"config\": {\"MAX_NUM_BATCHED_TOKENS\": \"2048\", \"GPU_MEMORY_UTILIZATION\": \"0.92\", \"MAX_NUM_SEQS\": \"128\", \"NUM_SCHEDULER_STEPS\": \"1\"}, \"verify_returncode\": 0, \"benchmark_returncode\": 0}\n records= 1\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The mock launch path passed for both optimize.py and a one-config sweep_dev_bench.py run, including JSONL output. I’m doing the final static pass and cleaning bytecode.\n\n• Updated Plan\n └ ✔ Add local mock OpenAI-compatible server for wrapper self-tests\n ✔ Exercise verify, benchmark, and optimize against the mock server\n □ Re-run static checks and clean artifacts\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh\n └ (no output)\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py\n └ (no output)\n\n• Ran python3 sweep_dev_bench.py --dry-run --chunks 2048,4096 --gpu-memory-utilizations 0.90,0.92 --max-num-seqs 128 --num-scheduler-steps 1 | sed -n '1,220p'\n └ [\n {\n … +22 lines (ctrl + t to view transcript)\n }\n ]\n\n• Ran python3 inspect_checkpoint.py | sed -n '1,120p'\n └ {\n \"repo\": \"Mapika/GLM-5.2-NVFP4\",\n … +69 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The static pass and metadata audit are clean. I’m removing generated bytecode and taking the final source inventory.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +8 lines (ctrl + t to view transcript)\n ./sweep_dev_bench.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• There is no git repository in this directory, so no git status is available. The final file inventory contains only source files and scripts.\n\n• Updated Plan\n └ ✔ Add local mock OpenAI-compatible server for wrapper self-tests\n ✔ Exercise verify, benchmark, and optimize against the mock server\n ✔ Re-run static checks and clean artifacts\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Added local end-to-end wrapper tests without changing the verifier entrypoint.\n\n New files:\n\n - mock_openai_server.py:1: tiny OpenAI-compatible mock server for /v1/models and /v1/chat/completions, including streaming SSE.\n - selftest_wrappers.py:1: starts the mock server, runs verify_serving, serial benchmark, and concurrent benchmark.\n - Updated README.md:1 with the self-test command.\n\n Validated:\n\n - python3 selftest_wrappers.py\n - python3 optimize.py --script ...\n - python3 sweep_dev_bench.py --script ...\n - bash -n submission/launch_server.sh submission/apply_patches.sh\n - python3 -m py_compile ...\n - python3 inspect_checkpoint.py\n\n This gives stronger evidence that the verification/benchmark tooling works around an OpenAI-compatible server, including streaming TTFT and concurrent requests. The actual 8×B200 Modal run, full GLM checkpoint load, correctness gate, and throughput benchmark are still unverified because they require the target GPU\n environment.\n\n─ Worked for 2m 22s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’ll inspect the objective and current tree again, then I’ll target the remaining things we can verify locally: packaging, command composition, and small runtime contract tests.\n\n• Explored\n └ Read pasted-text-1.txt\n List .\n Read launch_server.sh, serve_modal.py, README.md, pyproject.toml\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• One runtime risk remains from the Modal image env: it sets MODEL_REVISION globally, so a future Modal MODEL_PATH override could accidentally inherit the Mapika SHA. The launcher already knows how to pin the default, so I’m removing that env value from the image.\n\n• Updated Plan\n └ □ Remove stale global MODEL_REVISION from Modal image env\n □ Add a submission validation script for local invariant checks\n □ Run wrapper self-tests, static checks, and clean artifacts\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m making the Modal env change first; the prewarm function still uses the pinned revision through its function default, and the launcher still pins the default repo internally.\n\n• Edited serve_modal.py (+0 -1)\n 35 \"MODEL_PATH\": DEFAULT_MODEL_PATH,\n 36 - \"MODEL_REVISION\": DEFAULT_MODEL_REVISION,\n 36 }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Next I’m adding a small validator that checks the key local invariants: submission file exists/executable, launcher command composition preserves TP=8/FP8/chunking, local model paths do not get the default revision, and Modal is configured for B200:8.\n\n• Edited 2 files (+117 -0)\n └ README.md (+6 -0)\n 68\n 69 +Validate launcher and Modal invariants:\n 70 +\n 71 +```bash\n 72 +uv run --no-sync python validate_submission.py\n 73 +```\n 74 +\n 75 Launch once, then run verify and benchmark against the same server:\n\n └ validate_submission.py (+111 -0)\n 1 +#!/usr/bin/env python3\n 2 +import os\n 3 +import subprocess\n 4 +import tempfile\n 5 +from pathlib import Path\n 6 +\n 7 +import serve_modal\n 8 +\n 9 +\n 10 +ROOT = Path(__file__).parent\n 11 +LAUNCHER = ROOT / \"submission\" / \"launch_server.sh\"\n 12 +DEFAULT_REVISION = \"5f9c62a4b08c36a174055facbd1e0c39b832059f\"\n 13 +\n 14 +\n 15 +def assert_true(condition: bool, message: str) -> None:\n 16 + if not condition:\n 17 + raise AssertionError(message)\n 18 +\n 19 +\n 20 +def fake_vllm_dir(help_text: str) -> tempfile.TemporaryDirectory:\n 21 + tempdir = tempfile.TemporaryDirectory()\n 22 + path = Path(tempdir.name) / \"vllm\"\n 23 + path.write_text(\n 24 + \"\\n\".join(\n 25 + [\n 26 + \"#!/usr/bin/env bash\",\n 27 + 'if [[ \"$1\" == \"serve\" && \"$2\" == \"--help\" ]]; then',\n 28 + f\" printf '%s\\\\n' {help_text!r}\",\n 29 + \" exit 0\",\n 30 + \"fi\",\n 31 + 'printf \"%s\\\\n\" \"$@\"',\n 32 + ]\n 33 + ),\n 34 + encoding=\"utf-8\",\n 35 + )\n 36 + path.chmod(0o755)\n 37 + return tempdir\n 38 +\n 39 +\n 40 +def run_launcher(env: dict[str, str]) -> list[str]:\n 41 + with fake_vllm_dir(\"--revision --moe-runner-backend --load-format --attention-backend\") as tmp:\n 42 + merged_env = os.environ.copy()\n 43 + merged_env.update(env)\n 44 + merged_env[\"PATH\"] = f\"{tmp}:{merged_env['PATH']}\"\n 45 + result = subprocess.run(\n 46 + [\"bash\", str(LAUNCHER)],\n 47 + check=True,\n 48 + capture_output=True,\n 49 + text=True,\n 50 + env=merged_env,\n 51 + )\n 52 + return result.stdout.splitlines()\n 53 +\n 54 +\n 55 +def has_sequence(lines: list[str], *items: str) -> bool:\n 56 + for offset in range(0, len(lines) - len(items) + 1):\n 57 + if lines[offset : offset + len(items)] == list(items):\n 58 + return True\n 59 + return False\n 60 +\n 61 +\n 62 +def validate_launcher() -> None:\n 63 + assert_true(LAUNCHER.exists(), \"submission launcher is missing\")\n 64 + assert_true(os.access(LAUNCHER, os.X_OK), \"submission launcher is not executable\")\n 65 +\n 66 + default_lines = run_launcher({\"PORT\": \"12345\"})\n 67 + required_flags = [\n 68 + (\"--served-model-name\", \"glm-5.2-nvfp4\"),\n 69 + (\"--host\", \"0.0.0.0\"),\n 70 + (\"--port\", \"12345\"),\n 71 + (\"--quantization\", \"modelopt_fp4\"),\n 72 + (\"--kv-cache-dtype\", \"fp8\"),\n 73 + (\"--tensor-parallel-size\", \"8\"),\n 74 + (\"--max-model-len\", \"1000000\"),\n 75 + (\"--max-num-batched-tokens\", \"8192\"),\n 76 + (\"--gpu-memory-utilization\", \"0.92\"),\n 77 + (\"--moe-runner-backend\", \"flashinfer_cutlass\"),\n 78 + (\"--revision\", DEFAULT_REVISION),\n 79 + ]\n 80 + for flag, value in required_flags:\n 81 + assert_true(has_sequence(default_lines, flag, value), f\"missing {flag} {value}\")\n 82 + for flag in [\"--trust-remote-code\", \"--enable-chunked-prefill\", \"--async-scheduling\"]:\n 83 + assert_true(flag in default_lines, f\"missing {flag}\")\n 84 +\n 85 + local_lines = run_launcher({\"PORT\": \"12345\", \"MODEL_PATH\": \"/models/local\"})\n 86 + assert_true(\"/models/local\" in local_lines, \"local MODEL_PATH was not used\")\n 87 + assert_true(DEFAULT_REVISION not in local_lines, \"default revision leaked to local MODEL_PATH\")\n 88 + assert_true(\"--revision\" not in local_lines, \"revision flag was passed for local MODEL_PATH\")\n 89 +\n 90 +\n 91 +def validate_modal() -> None:\n 92 + assert_true(serve_modal.VLLM_PORT == 8000, \"Modal vLLM port changed\")\n 93 + assert_true(serve_modal.DEFAULT_MODEL_PATH == \"Mapika/GLM-5.2-NVFP4\", \"default model changed\")\n 94 + assert_true(serve_modal.DEFAULT_MODEL_REVISION == DEFAULT_REVISION, \"default revision changed\")\n 95 +\n 96 + source = (ROOT / \"serve_modal.py\").read_text(encoding=\"utf-8\")\n 97 + assert_true('gpu=\"B200:8\"' in source, \"Modal GPU config is not B200:8\")\n 98 + assert_true(\"@modal.web_server\" in source, \"Modal web_server decorator missing\")\n 99 + assert_true(\"MODEL_REVISION\" not in source.split(\".env(\", 1)[1].split(\")\", 1)[0],\n 100 + \"MODEL_REVISION should not be globally set in image env\")\n 101 +\n 102 +\n 103 +def main() -> int:\n 104 + validate_launcher()\n 105 + validate_modal()\n 106 + print(\"submission validation ok\")\n 107 + return 0\n 108 +\n 109 +\n 110 +if __name__ == \"__main__\":\n 111 + raise SystemExit(main())\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The validator is added. I’m running it now along with the existing mock self-test so we catch both static invariant drift and wrapper behavior regressions.\n\n• Updated Plan\n └ ✔ Remove stale global MODEL_REVISION from Modal image env\n □ Add a submission validation script for local invariant checks\n □ Run wrapper self-tests, static checks, and clean artifacts\n\n• Ran python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py validate_submission.py\n └ (no output)\n\n• Ran python3 validate_submission.py\n └ submission validation ok\n\n• Ran python3 selftest_wrappers.py\n └ {\"ok\": true, \"text\": \"ready\", \"usage\": {\"prompt_tokens\": 4, \"completion_tokens\": 1, \"total_tokens\": 5}}\n {\n … +97 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The validator and wrapper self-test passed. I’m doing one more pass through the production launcher dry-runs and final cleanup so the source tree is left in a verified state.\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'if [[ \"$1\" == \"serve\" && \"$2\" == \"--help\" ]]; then printf \"%s\\n\" \"--revision --moe-runner-backend --load-format --attention-backend\"; exit 0; fi' 'printf \"%s\\n\" \"$@\"' > \"$tmpdir/vllm\"\n │ chmod +x \"$tmpdir/vllm\"\n │ … +1 lines\n └ Starting GLM-5.2 NVFP4 server\n model=Mapika/GLM-5.2-NVFP4\n … +46 lines (ctrl + t to view transcript)\n --attention-backend\n flashinfer\n\n• Ran bash -n submission/launch_server.sh submission/apply_patches.sh && python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py validate_submission.py\n └ (no output)\n\n• Ran tmpdir=$(mktemp -d)\n │ printf '%s\\n' '#!/usr/bin/env bash' 'exec python3 mock_openai_server.py --port \"$PORT\"' > \"$tmpdir/mock_launch.sh\"\n │ chmod +x \"$tmpdir/mock_launch.sh\"\n │ … +1 lines\n └ {\"ok\": true, \"text\": \"ready\", \"usage\": {\"prompt_tokens\": 4, \"completion_tokens\": 1, \"total_tokens\": 5}}\n {\n … +97 lines (ctrl + t to view transcript)\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The production launcher dry-run and mock optimize.py path both passed. I’m cleaning bytecode and taking the final inventory now.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +9 lines (ctrl + t to view transcript)\n ./validate_submission.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• There is no git repository in this workspace, so no git status is available. Only source files remain after cleanup.\n\n• Updated Plan\n └ ✔ Remove stale global MODEL_REVISION from Modal image env\n ✔ Add a submission validation script for local invariant checks\n ✔ Run wrapper self-tests, static checks, and clean artifacts\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Continued hardening the submission.\n\n Changed:\n\n - serve_modal.py:1: removed global MODEL_REVISION from the Modal image env. The launcher now owns default revision pinning, so a future Modal MODEL_PATH override will not accidentally inherit the Mapika SHA.\n - validate_submission.py:1: added local invariant checks for launcher flags, default revision behavior, local MODEL_PATH behavior, and Modal B200:8/web server configuration.\n - README.md:1: documented the validator command.\n\n Verification run:\n\n - python3 validate_submission.py\n - python3 selftest_wrappers.py\n - python3 optimize.py --script ...\n - launcher dry-run with fake vllm\n - bash -n submission/launch_server.sh submission/apply_patches.sh\n - python3 -m py_compile ...\n\n Still not complete: the real Modal 8×B200 server startup, full 459 GB checkpoint load, correctness comparison, and throughput benchmark remain unverified because they require the target GPU environment.\n\n─ Worked for 2m 43s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’ll re-read the objective and inspect the current files, then I’ll look for remaining local gaps in the launch contract and Modal packaging before making changes.\n\n• Explored\n └ Read pasted-text-1.txt\n List .\n Read launch_server.sh, serve_modal.py, validate_submission.py, README.md, inspect_checkpoint.py\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The current code passes local contract tests. I’m checking the Modal CLI/package surface next and then I’ll add a small preflight script that consolidates the local checks into one command, so the submission can be audited before any expensive GPU run.\n\n• Ran python3 - <<'PY'\n │ import inspect, modal\n │ print('modal', getattr(modal, '__version__', 'unknown'))\n │ … +4 lines\n └ modal 1.3.0.post1\n Image.add_local_dir (local_path: Union[str, pathlib.Path], remote_path: str, *, copy: bool = False, ignore: Union[collections.abc.Sequence[str], Callable[[pathlib.Path], bool]] = []) -> '_Image'\n web_server (port: int, *, startup_timeout: float = 5.0, label: Optional[str] = None, custom_domains: Optional[collections.abc.Iterable[str]] = None, requires_proxy_auth: bool = False) -> Callable[[Union[modal._partial_function._PartialFunction, Callable[[], Any], Callable[[Any], Any]]],\n modal._partial_function._PartialFunction]\n concurrent (_warn_parentheses_missing=None, *, max_inputs: Optional[int] = None, target_inputs: Optional[int] = None) -> Callable[[Union[modal._partial_function._PartialFunction[~P, +ReturnType, +ReturnType], Callable[~P, +ReturnType]]], modal._partial_function._PartialFunction[~P, +ReturnType, +ReturnType]]\n\n• Ran modal --version 2>/dev/null || true\n └ modal client version: 1.3.0.post1\n\n• Ran python3 validate_submission.py && python3 selftest_wrappers.py >/tmp/glm_selftest.out && tail -n 5 /tmp/glm_selftest.out\n └ \"prompt\": \"Summarize why chunked prefill helps inter-token latency.\"\n }\n ]\n }\n }\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The installed Modal client and API signatures match the code paths we’re using. I’m adding a preflight.py runner to execute all cheap local gates in order, including syntax, launcher invariants, wrapper self-test, sweep dry-run, and checkpoint metadata audit.\n\n• Updated Plan\n └ □ Add consolidated local preflight runner\n □ Run preflight and existing checks\n □ Clean generated artifacts and summarize remaining unverified gates\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• This preflight stays local and does not start Modal or download model shards. It only checks source and small Hugging Face metadata files.\n\n• Edited 2 files (+70 -0)\n └ README.md (+6 -0)\n 74\n 75 +Run all cheap local preflight checks:\n 76 +\n 77 +```bash\n 78 +uv run --no-sync python preflight.py\n 79 +```\n 80 +\n 81 Launch once, then run verify and benchmark against the same server:\n\n └ preflight.py (+64 -0)\n 1 +#!/usr/bin/env python3\n 2 +import argparse\n 3 +import subprocess\n 4 +import sys\n 5 +\n 6 +\n 7 +PYTHON_FILES = [\n 8 + \"serve_modal.py\",\n 9 + \"verify_serving.py\",\n 10 + \"run_dev_bench.py\",\n 11 + \"optimize.py\",\n 12 + \"sweep_dev_bench.py\",\n 13 + \"inspect_checkpoint.py\",\n 14 + \"mock_openai_server.py\",\n 15 + \"selftest_wrappers.py\",\n 16 + \"validate_submission.py\",\n 17 + \"preflight.py\",\n 18 +]\n 19 +\n 20 +\n 21 +def run(command: list[str]) -> None:\n 22 + print(\"+ \" + \" \".join(command), flush=True)\n 23 + subprocess.run(command, check=True)\n 24 +\n 25 +\n 26 +def parse_args() -> argparse.Namespace:\n 27 + parser = argparse.ArgumentParser()\n 28 + parser.add_argument(\n 29 + \"--skip-checkpoint\",\n 30 + action=\"store_true\",\n 31 + help=\"Skip the small Hugging Face metadata audit.\",\n 32 + )\n 33 + return parser.parse_args()\n 34 +\n 35 +\n 36 +def main() -> int:\n 37 + args = parse_args()\n 38 + run([\"bash\", \"-n\", \"submission/launch_server.sh\", \"submission/apply_patches.sh\"])\n 39 + run([sys.executable, \"-m\", \"py_compile\", *PYTHON_FILES])\n 40 + run([sys.executable, \"validate_submission.py\"])\n 41 + run([sys.executable, \"selftest_wrappers.py\"])\n 42 + run(\n 43 + [\n 44 + sys.executable,\n 45 + \"sweep_dev_bench.py\",\n 46 + \"--dry-run\",\n 47 + \"--chunks\",\n 48 + \"2048,8192\",\n 49 + \"--gpu-memory-utilizations\",\n 50 + \"0.92\",\n 51 + \"--max-num-seqs\",\n 52 + \"256\",\n 53 + \"--num-scheduler-steps\",\n 54 + \"1\",\n 55 + ]\n 56 + )\n 57 + if not args.skip_checkpoint:\n 58 + run([sys.executable, \"inspect_checkpoint.py\"])\n 59 + print(\"preflight ok\")\n 60 + return 0\n 61 +\n 62 +\n 63 +if __name__ == \"__main__\":\n 64 + raise SystemExit(main())\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The preflight script is added. I’m running it now, then I’ll do a final source cleanup.\n\n• Updated Plan\n └ ✔ Add consolidated local preflight runner\n □ Run preflight and existing checks\n □ Clean generated artifacts and summarize remaining unverified gates\n\n• Ran python3 preflight.py\n └ submission validation ok\n + /Library/Frameworks/Python.framework/Versions/3.12/bin/python3 selftest_wrappers.py\n … +189 lines (ctrl + t to view transcript)\n }\n preflight ok\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Preflight passed, including the checkpoint metadata audit and mock OpenAI-compatible streaming path. I’m cleaning generated bytecode and capturing the final source inventory now.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +10 lines (ctrl + t to view transcript)\n ./validate_submission.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• There is no git repository here, so there is no git status to report. The tree now contains only source files and scripts.\n\n• Updated Plan\n └ ✔ Add consolidated local preflight runner\n ✔ Run preflight and existing checks\n ✔ Clean generated artifacts and summarize remaining unverified gates\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Added preflight.py:1, a single cheap local gate that runs:\n\n - shell syntax check for submission/launch_server.sh\n - Python compilation for all helper scripts\n - validate_submission.py\n - selftest_wrappers.py\n - sweep_dev_bench.py --dry-run\n - inspect_checkpoint.py\n\n Updated README.md:1 with the preflight command.\n\n I ran python3 preflight.py; it passed. I also confirmed the installed Modal client is 1.3.0.post1 and the Modal API signatures match the code paths in serve_modal.py.\n\n Still not complete: the real Modal B200:8 server startup, full checkpoint load, hidden correctness gate, and throughput benchmark remain unverified because they require the target GPU environment.\n\n─ Worked for 1m 53s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’ll re-read the objective and current state, then I’ll keep working on things we can prove without the target GPUs.\n\n• Explored\n └ Read pasted-text-1.txt\n List .\n Read launch_server.sh, serve_modal.py, preflight.py, validate_submission.py, README.md\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The current local gates pass. I’m going to add one more focused check around the sweep path: it should prove the sweep actually passes each tuned environment variable into the launch script, not just generate the config list.\n\n• Updated Plan\n └ □ Add sweep environment propagation test using a recording launcher\n □ Run preflight and targeted sweep propagation test\n □ Clean artifacts and report remaining target-only gates\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m adding a small test that records the environment seen by a mock launcher during sweep_dev_bench.py; that catches regressions where sweep config generation works but the launched server never receives the knobs.\n\n• Edited 3 files (+119 -0)\n └ README.md (+6 -0)\n 74\n 75 +Validate sweep environment propagation:\n 76 +\n 77 +```bash\n 78 +uv run --no-sync python test_sweep_env.py\n 79 +```\n 80 +\n 81 Run all cheap local preflight checks:\n\n └ preflight.py (+2 -0)\n 16 \"validate_submission.py\",\n 17 + \"test_sweep_env.py\",\n 18 \"preflight.py\",\n ⋮\n 42 run([sys.executable, \"selftest_wrappers.py\"])\n 43 + run([sys.executable, \"test_sweep_env.py\"])\n 44 run(\n\n └ test_sweep_env.py (+111 -0)\n 1 +#!/usr/bin/env python3\n 2 +import json\n 3 +import subprocess\n 4 +import sys\n 5 +import tempfile\n 6 +from pathlib import Path\n 7 +\n 8 +\n 9 +EXPECTED = {\n 10 + \"MAX_NUM_BATCHED_TOKENS\": \"4096\",\n 11 + \"GPU_MEMORY_UTILIZATION\": \"0.91\",\n 12 + \"MAX_NUM_SEQS\": \"128\",\n 13 + \"NUM_SCHEDULER_STEPS\": \"2\",\n 14 + \"PORT\": \"39131\",\n 15 + \"MODEL_PATH\": \"test/model\",\n 16 +}\n 17 +\n 18 +\n 19 +def write_launcher(path: Path) -> None:\n 20 + path.write_text(\n 21 + \"\"\"#!/usr/bin/env bash\n 22 +set -euo pipefail\n 23 +python3 - \"$SWEEP_ENV_RECORD\" <<'PY'\n 24 +import json\n 25 +import os\n 26 +import sys\n 27 +\n 28 +keys = [\n 29 + \"MAX_NUM_BATCHED_TOKENS\",\n 30 + \"GPU_MEMORY_UTILIZATION\",\n 31 + \"MAX_NUM_SEQS\",\n 32 + \"NUM_SCHEDULER_STEPS\",\n 33 + \"PORT\",\n 34 + \"MODEL_PATH\",\n 35 +]\n 36 +with open(sys.argv[1], \"a\", encoding=\"utf-8\") as handle:\n 37 + handle.write(json.dumps({key: os.environ.get(key) for key in keys}) + \"\\\\n\")\n 38 +PY\n 39 +exec python3 mock_openai_server.py --port \"$PORT\"\n 40 +\"\"\",\n 41 + encoding=\"utf-8\",\n 42 + )\n 43 + path.chmod(0o755)\n 44 +\n 45 +\n 46 +def main() -> int:\n 47 + with tempfile.TemporaryDirectory() as tmp:\n 48 + tempdir = Path(tmp)\n 49 + launcher = tempdir / \"recording_launcher.sh\"\n 50 + record_path = tempdir / \"env.jsonl\"\n 51 + result_path = tempdir / \"sweep.jsonl\"\n 52 + write_launcher(launcher)\n 53 +\n 54 + command = [\n 55 + sys.executable,\n 56 + \"sweep_dev_bench.py\",\n 57 + \"--script\",\n 58 + str(launcher),\n 59 + \"--port\",\n 60 + EXPECTED[\"PORT\"],\n 61 + \"--model-path\",\n 62 + EXPECTED[\"MODEL_PATH\"],\n 63 + \"--chunks\",\n 64 + EXPECTED[\"MAX_NUM_BATCHED_TOKENS\"],\n 65 + \"--gpu-memory-utilizations\",\n 66 + EXPECTED[\"GPU_MEMORY_UTILIZATION\"],\n 67 + \"--max-num-seqs\",\n 68 + EXPECTED[\"MAX_NUM_SEQS\"],\n 69 + \"--num-scheduler-steps\",\n 70 + EXPECTED[\"NUM_SCHEDULER_STEPS\"],\n 71 + \"--startup-timeout\",\n 72 + \"10\",\n 73 + \"--request-timeout\",\n 74 + \"10\",\n 75 + \"--max-tokens\",\n 76 + \"16\",\n 77 + \"--concurrency\",\n 78 + \"2\",\n 79 + \"--repeat-prompts\",\n 80 + \"1\",\n 81 + \"--output\",\n 82 + str(result_path),\n 83 + ]\n 84 + env = {\"SWEEP_ENV_RECORD\": str(record_path)}\n 85 + subprocess.run(command, check=True, env={**dict(**env), **__import__(\"os\").environ})\n 86 +\n 87 + records = [\n 88 + json.loads(line)\n 89 + for line in record_path.read_text(encoding=\"utf-8\").splitlines()\n 90 + if line.strip()\n 91 + ]\n 92 + if records != [EXPECTED]:\n 93 + raise AssertionError(f\"unexpected launcher env records: {records}\")\n 94 +\n 95 + sweep_records = [\n 96 + json.loads(line)\n 97 + for line in result_path.read_text(encoding=\"utf-8\").splitlines()\n 98 + if line.strip()\n 99 + ]\n 100 + if len(sweep_records) != 1:\n 101 + raise AssertionError(f\"expected one sweep record, got {len(sweep_records)}\")\n 102 + record = sweep_records[0]\n 103 + if record[\"verify\"][\"returncode\"] != 0 or record[\"benchmark\"][\"returncode\"] != 0:\n 104 + raise AssertionError(\"sweep verify/benchmark did not pass\")\n 105 +\n 106 + print(\"sweep env propagation ok\")\n 107 + return 0\n 108 +\n 109 +\n 110 +if __name__ == \"__main__\":\n 111 + raise SystemExit(main())\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I spotted a small environment-merge mistake in the new test before running it; I’m correcting that so the temporary record path always wins over the parent environment.\n\n• Edited test_sweep_env.py (+3 -2)\n 2 import json\n 3 +import os\n 4 import subprocess\n ⋮\n 84 ]\n 84 - env = {\"SWEEP_ENV_RECORD\": str(record_path)}\n 85 - subprocess.run(command, check=True, env={**dict(**env), **__import__(\"os\").environ})\n 85 + env = {**os.environ, \"SWEEP_ENV_RECORD\": str(record_path)}\n 86 + subprocess.run(command, check=True, env=env)\n 87\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The sweep propagation test is ready. I’m running it directly first, then I’ll run the consolidated preflight.\n\n• Ran python3 -m py_compile test_sweep_env.py preflight.py\n └ (no output)\n\n• Ran python3 test_sweep_env.py\n └ {\"config\": {\"MAX_NUM_BATCHED_TOKENS\": \"4096\", \"GPU_MEMORY_UTILIZATION\": \"0.91\", \"MAX_NUM_SEQS\": \"128\", \"NUM_SCHEDULER_STEPS\": \"2\"}, \"verify_returncode\": 0, \"benchmark_returncode\": 0}\n sweep env propagation ok\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The targeted sweep propagation test passed and confirmed the launched process receives the tuning env. I’m running the full preflight now with the new test included.\n\n• Ran python3 preflight.py\n └ + bash -n submission/launch_server.sh submission/apply_patches.sh\n + /Library/Frameworks/Python.framework/Versions/3.12/bin/python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py mock_openai_server.py selftest_wrappers.py validate_submission.py test_sweep_env.py preflight.py\n … +195 lines (ctrl + t to view transcript)\n }\n preflight ok\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Preflight passed with the new sweep env propagation test included. I’m cleaning bytecode and checking the final file list.\n\n• Ran rm -rf __pycache__ && find . -maxdepth 3 -type f | sort\n └ ./README.md\n ./inspect_checkpoint.py\n … +11 lines (ctrl + t to view transcript)\n ./validate_submission.py\n ./verify_serving.py\n\n• Ran git status --short 2>/dev/null || true\n └ (no output)\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• There is no git repository in this workspace, so no git status is available. The tree is clean of generated bytecode.\n\n• Updated Plan\n └ ✔ Add sweep environment propagation test using a recording launcher\n ✔ Run preflight and targeted sweep propagation test\n ✔ Clean artifacts and report remaining target-only gates\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Added test_sweep_env.py:1, which runs sweep_dev_bench.py through a recording mock launcher and verifies the launched process receives the tuned env vars:\n\n - MAX_NUM_BATCHED_TOKENS\n - GPU_MEMORY_UTILIZATION\n - MAX_NUM_SEQS\n - NUM_SCHEDULER_STEPS\n - PORT\n - MODEL_PATH\n\n Updated preflight.py:1 to include that test, and documented it in README.md:1.\n\n Ran:\n\n - python3 test_sweep_env.py\n - python3 preflight.py\n\n Both passed. The remaining unverified work is still the target-only part: Modal B200:8 startup, full checkpoint load, correctness gate, and real throughput benchmark.\n\n─ Worked for 2m 09s ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Goal paused Objective: pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing. Time: 30m.\n\n■ Conversation interrupted - tell the model what to do differently. Something went wrong? Hit `/feedback` to report the issue.\n\n\n› you can run modal, from now on only build things that actually relate to the modal benchmark, all your work from now on should be focused on getting 300+ tps on modal uising 8 b200s\n\n\n• I’ll switch focus to the actual Modal path. First I’m re-reading the objective and checking the current source plus Modal CLI state, then I’ll run the least-wasteful remote steps needed to get toward a real 8×B200 benchmark.\n\n• Explored\n └ Read pasted-text-1.txt\n List .\n Read serve_modal.py, launch_server.sh, run_dev_bench.py\n\n• Ran modal --version && modal profile current 2>/dev/null || true && modal app list 2>/dev/null | sed -n '1,80p' || true\n └ modal client version: 1.3.0.post1\n aidenhuang-personal\n … +9 lines (ctrl + t to view transcript)\n │ │ │ │ │ 22:41 PDT │\n └───────────────────────────┴─────────────┴─────────────┴───────┴─────────────┴─\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Modal is authenticated, and there is already a detached glm-5.2-nv... ephemeral app with one running task from June 22. I’m checking that app before starting another 8×B200 job, because reusing or stopping it is directly relevant to benchmark cost and turnaround.\n\n• Ran modal app --help | sed -n '1,220p'\n └\n Usage: modal app [OPTIONS] COMMAND [ARGS]...\n … +13 lines (ctrl + t to view transcript)\n ╰──────────────────────────────────────────────────────────────────────────────╯\n\n\n• Ran modal run --help | sed -n '1,220p'\n └ Usage: modal run [OPTIONS] FUNC_REF\n\n … +38 lines (ctrl + t to view transcript)\n of a file/script path\n -h, --help Show this message and exit.\n\n• Ran modal deploy --help | sed -n '1,220p'\n └\n Usage: modal deploy [OPTIONS] APP_REF\n … +28 lines (ctrl + t to view transcript)\n ╰──────────────────────────────────────────────────────────────────────────────╯\n\n\n• Ran modal app list | sed -n '1,120p'\n └ Apps\n ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━\n … +7 lines (ctrl + t to view transcript)\n │ │ │ │ │ 22:41 PDT │\n └───────────────────────────┴─────────────┴─────────────┴───────┴─────────────┴─\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m pulling logs from the existing detached app now. If it is a stale or failed 8×B200 task, that is the first thing to resolve before launching another run.\n\n• Ran modal app logs --help | sed -n '1,220p'\n └\n Usage: modal app logs [OPTIONS] [APP_IDENTIFIER]\n … +28 lines (ctrl + t to view transcript)\n ╰──────────────────────────────────────────────────────────────────────────────╯\n\n\n• Ran modal app stop --help | sed -n '1,160p'\n └\n Usage: modal app stop [OPTIONS] [APP_IDENTIFIER]\n … +13 lines (ctrl + t to view transcript)\n ╰──────────────────────────────────────────────────────────────────────────────╯\n\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m going to stream a short slice of the detached app logs with a timeout so it doesn’t block. That should tell us whether it is downloading, building, serving, or stuck.\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The detached app log stream produced no output in 30 seconds. I’m stopping that local log-follow process so it doesn’t keep running here, then I’ll inspect whether the app is still consuming a task.\n\n↳ Interacted with background terminal · modal app logs ap-f9HzPHVzZp6K8VdyV3F2TC --timestamps\n └\n\n• Ran modal app logs ap-f9HzPHVzZp6K8VdyV3F2TC --timestamps\n └ (no output)\n\n• Ran modal app list | sed -n '1,120p'\n └ Apps\n ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━\n … +7 lines (ctrl + t to view transcript)\n │ │ │ │ │ 22:41 PDT │\n └───────────────────────────┴─────────────┴─────────────┴───────┴─────────────┴─\n\n■ Conversation interrupted - tell the model what to do differently. Something went wrong? Hit `/feedback` to report the issue.\n\n\n› dontrr mess with other apps\n\n\n• Understood. I won’t stop, inspect logs for, or otherwise touch existing Modal apps. I’ll only create/run functions from this workspace that are directly for the GLM Modal benchmark.\n\n• Explored\n └ Read pasted-text-1.txt, serve_modal.py, launch_server.sh, run_dev_bench.py\n List .\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m going to create a dedicated benchmark entrypoint in this workspace so we can run a new Modal job for this model only, without touching any existing apps. It will launch the same /app/submission/launch_server.sh on B200:8, run a real OpenAI-compatible benchmark in-container, and return TPS/TTFT/latency JSON.\n\n• Updated Plan\n └ □ Add dedicated Modal 8xB200 benchmark function\n □ Run local preflight and Modal image syntax checks\n □ Start Modal cache prewarm or benchmark run for real TPS evidence\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The benchmark function will use the same Modal image and volumes as serving, but with its own app name so it is clearly separate from anything already running. It will tune via environment variables, not by mutating files.\n\n• Edited serve_modal.py (+246 -1)\n 1 import os\n 2 +import json\n 3 import socket\n ⋮\n 5 import time\n 6 +import urllib.error\n 7 +import urllib.request\n 8 from pathlib import Path\n 9 +from concurrent.futures import ThreadPoolExecutor, as_completed\n 10\n ⋮\n 13\n 10 -APP_NAME = \"glm-5.2-nvfp4\"\n 14 +APP_NAME = \"glm-5.2-nvfp4-bench\"\n 15 MINUTES = 60\n ⋮\n 18 DEFAULT_MODEL_REVISION = \"5f9c62a4b08c36a174055facbd1e0c39b832059f\"\n 19 +DEFAULT_MODEL_NAME = \"glm-5.2-nvfp4\"\n 20 +\n 21 +BENCH_PROMPTS = [\n 22 + \"Write a detailed but concise explanation of why chunked prefill improves decode latency for a large MoE language model.\",\n 23 + \"Describe the tradeoffs between throughput, TTFT, and inter-token latency when serving a long-context model on a single multi-GPU node.\",\n 24 + \"Give a practical checklist for safely deploying a quantized OpenAI-compatible language-model endpoint.\",\n 25 + \"Summarize how FP8 KV cache and tensor parallelism affect memory headroom for million-token context windows.\",\n 26 +]\n 27\n ⋮\n 73\n 74 +def wait_http_ready(process: subprocess.Popen, port: int, timeout_s: int) -> None:\n 75 + url = f\"http://127.0.0.1:{port}/v1/models\"\n 76 + deadline = time.monotonic() + timeout_s\n 77 + last_error = None\n 78 + while time.monotonic() < deadline:\n 79 + if process.poll() is not None:\n 80 + raise RuntimeError(f\"vLLM exited with code {process.returncode}\")\n 81 + try:\n 82 + with urllib.request.urlopen(url, timeout=5) as response:\n 83 + if response.status == 200:\n 84 + return\n 85 + except (OSError, urllib.error.URLError) as exc:\n 86 + last_error = exc\n 87 + time.sleep(5)\n 88 + raise TimeoutError(f\"vLLM did not become HTTP-ready at {url}: {last_error}\")\n 89 +\n 90 +\n 91 +def post_stream_json(port: int, payload: dict, timeout_s: int) -> dict:\n 92 + url = f\"http://127.0.0.1:{port}/v1/chat/completions\"\n 93 + req = urllib.request.Request(\n 94 + url,\n 95 + data=json.dumps(payload).encode(\"utf-8\"),\n 96 + headers={\n 97 + \"Content-Type\": \"application/json\",\n 98 + \"Authorization\": \"Bearer modal-benchmark\",\n 99 + },\n 100 + method=\"POST\",\n 101 + )\n 102 +\n 103 + start = time.perf_counter()\n 104 + first_token_s = None\n 105 + text_parts = []\n 106 + usage = {}\n 107 + with urllib.request.urlopen(req, timeout=timeout_s) as response:\n 108 + while True:\n 109 + line = response.readline()\n 110 + if not line:\n 111 + break\n 112 + decoded = line.decode(\"utf-8\", errors=\"replace\").strip()\n 113 + if not decoded.startswith(\"data:\"):\n 114 + continue\n 115 + data = decoded.removeprefix(\"data:\").strip()\n 116 + if data == \"[DONE]\":\n 117 + break\n 118 + chunk = json.loads(data)\n 119 + if chunk.get(\"usage\"):\n 120 + usage = chunk[\"usage\"]\n 121 + for choice in chunk.get(\"choices\") or []:\n 122 + content = (choice.get(\"delta\") or {}).get(\"content\") or \"\"\n 123 + if content:\n 124 + if first_token_s is None:\n 125 + first_token_s = time.perf_counter() - start\n 126 + text_parts.append(content)\n 127 +\n 128 + latency_s = time.perf_counter() - start\n 129 + text = \"\".join(text_parts)\n 130 + completion_tokens = int(usage.get(\"completion_tokens\") or 0)\n 131 + return {\n 132 + \"ttft_s\": first_token_s if first_token_s is not None else latency_s,\n 133 + \"latency_s\": latency_s,\n 134 + \"completion_tokens\": completion_tokens,\n 135 + \"chars\": len(text),\n 136 + }\n 137 +\n 138 +\n 139 +def benchmark_request(port: int, model_name: str, request_id: int, max_tokens: int, timeout_s: int) -> dict:\n 140 + prompt = BENCH_PROMPTS[request_id % len(BENCH_PROMPTS)]\n 141 + payload = {\n 142 + \"model\": model_name,\n 143 + \"messages\": [{\"role\": \"user\", \"content\": prompt}],\n 144 + \"temperature\": 0,\n 145 + \"max_tokens\": max_tokens,\n 146 + \"stream\": True,\n 147 + \"stream_options\": {\"include_usage\": True},\n 148 + }\n 149 + return post_stream_json(port, payload, timeout_s)\n 150 +\n 151 +\n 152 +def summarize(samples: list[dict], wall_time_s: float) -> dict:\n 153 + ttfts = sorted(sample[\"ttft_s\"] for sample in samples)\n 154 + latencies = sorted(sample[\"latency_s\"] for sample in samples)\n 155 + completion_tokens = sum(sample[\"completion_tokens\"] for sample in samples)\n 156 + chars = sum(sample[\"chars\"] for sample in samples)\n 157 +\n 158 + def percentile(values: list[float], p: float) -> float:\n 159 + if not values:\n 160 + return 0.0\n 161 + index = min(len(values) - 1, round((len(values) - 1) * p))\n 162 + return values[index]\n 163 +\n 164 + return {\n 165 + \"requests\": len(samples),\n 166 + \"wall_time_s\": wall_time_s,\n 167 + \"completion_tokens\": completion_tokens,\n 168 + \"completion_tokens_per_second\": completion_tokens / wall_time_s if wall_time_s else 0,\n 169 + \"chars_per_second\": chars / wall_time_s if wall_time_s else 0,\n 170 + \"ttft_p50_s\": percentile(ttfts, 0.50),\n 171 + \"ttft_p95_s\": percentile(ttfts, 0.95),\n 172 + \"latency_p50_s\": percentile(latencies, 0.50),\n 173 + \"latency_p95_s\": percentile(latencies, 0.95),\n 174 + }\n 175 +\n 176 +\n 177 +def run_inprocess_benchmark(\n 178 + port: int,\n 179 + model_name: str,\n 180 + concurrency: int,\n 181 + requests: int,\n 182 + max_tokens: int,\n 183 + request_timeout_s: int,\n 184 +) -> dict:\n 185 + start = time.perf_counter()\n 186 + samples = []\n 187 + with ThreadPoolExecutor(max_workers=concurrency) as executor:\n 188 + futures = [\n 189 + executor.submit(\n 190 + benchmark_request,\n 191 + port,\n 192 + model_name,\n 193 + request_id,\n 194 + max_tokens,\n 195 + request_timeout_s,\n 196 + )\n 197 + for request_id in range(requests)\n 198 + ]\n 199 + for future in as_completed(futures):\n 200 + samples.append(future.result())\n 201 + return summarize(samples, time.perf_counter() - start)\n 202 +\n 203 +\n 204 +def terminate_process(process: subprocess.Popen) -> None:\n 205 + if process.poll() is not None:\n 206 + return\n 207 + process.terminate()\n 208 + try:\n 209 + process.wait(timeout=60)\n 210 + except subprocess.TimeoutExpired:\n 211 + process.kill()\n 212 + process.wait(timeout=30)\n 213 +\n 214 +\n 215 @app.function(\n ⋮\n 250 wait_ready(process, VLLM_PORT, 45 * MINUTES)\n 251 +\n 252 +\n 253 +@app.function(\n 254 + image=image,\n 255 + gpu=\"B200:8\",\n 256 + scaledown_window=10 * MINUTES,\n 257 + timeout=90 * MINUTES,\n 258 + volumes={\n 259 + \"/root/.cache/huggingface\": hf_cache,\n 260 + \"/root/.cache/vllm\": vllm_cache,\n 261 + },\n 262 +)\n 263 +def run_modal_benchmark(\n 264 + chunk: int = 8192,\n 265 + concurrency: int = 16,\n 266 + requests: int = 64,\n 267 + max_tokens: int = 256,\n 268 + gpu_memory_utilization: float = 0.92,\n 269 + max_num_seqs: int = 256,\n 270 + num_scheduler_steps: int = 1,\n 271 + request_timeout_s: int = 900,\n 272 + startup_timeout_s: int = 45 * MINUTES,\n 273 +) -> dict:\n 274 + env = os.environ.copy()\n 275 + env.update(\n 276 + {\n 277 + \"PORT\": str(VLLM_PORT),\n 278 + \"MODEL_PATH\": DEFAULT_MODEL_PATH,\n 279 + \"MAX_NUM_BATCHED_TOKENS\": str(chunk),\n 280 + \"GPU_MEMORY_UTILIZATION\": str(gpu_memory_utilization),\n 281 + \"MAX_NUM_SEQS\": str(max_num_seqs),\n 282 + \"NUM_SCHEDULER_STEPS\": str(num_scheduler_steps),\n 283 + }\n 284 + )\n 285 + process = subprocess.Popen([\"/app/submission/launch_server.sh\"], env=env)\n 286 + try:\n 287 + wait_http_ready(process, VLLM_PORT, startup_timeout_s)\n 288 + result = run_inprocess_benchmark(\n 289 + port=VLLM_PORT,\n 290 + model_name=DEFAULT_MODEL_NAME,\n 291 + concurrency=concurrency,\n 292 + requests=requests,\n 293 + max_tokens=max_tokens,\n 294 + request_timeout_s=request_timeout_s,\n 295 + )\n 296 + result[\"config\"] = {\n 297 + \"chunk\": chunk,\n 298 + \"concurrency\": concurrency,\n 299 + \"requests\": requests,\n 300 + \"max_tokens\": max_tokens,\n 301 + \"gpu_memory_utilization\": gpu_memory_utilization,\n 302 + \"max_num_seqs\": max_num_seqs,\n 303 + \"num_scheduler_steps\": num_scheduler_steps,\n 304 + }\n 305 + return result\n 306 + finally:\n 307 + terminate_process(process)\n 308 +\n 309 +\n 310 +@app.function(\n 311 + image=image,\n 312 + gpu=\"B200:8\",\n 313 + scaledown_window=10 * MINUTES,\n 314 + timeout=120 * MINUTES,\n 315 + volumes={\n 316 + \"/root/.cache/huggingface\": hf_cache,\n 317 + \"/root/.cache/vllm\": vllm_cache,\n 318 + },\n 319 +)\n 320 +def run_modal_chunk_sweep(\n 321 + chunks: list[int] = [4096, 8192, 16384],\n 322 + concurrency: int = 16,\n 323 + requests: int = 64,\n 324 + max_tokens: int = 256,\n 325 + gpu_memory_utilization: float = 0.92,\n 326 + max_num_seqs: int = 256,\n 327 + num_scheduler_steps: int = 1,\n 328 +) -> list[dict]:\n 329 + results = []\n 330 + for chunk in chunks:\n 331 + results.append(\n 332 + run_modal_benchmark.local(\n 333 + chunk=chunk,\n 334 + concurrency=concurrency,\n 335 + requests=requests,\n 336 + max_tokens=max_tokens,\n 337 + gpu_memory_utilization=gpu_memory_utilization,\n 338 + max_num_seqs=max_num_seqs,\n 339 + num_scheduler_steps=num_scheduler_steps,\n 340 + )\n 341 + )\n 342 + return results\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• I’m tightening the new sweep function before running it: it should call a plain helper inside the same remote container, not recursively invoke the Modal-decorated function.\n\n• Edited serve_modal.py (+62 -35)\n 214\n 215 +def run_modal_benchmark_once(\n 216 + chunk: int,\n 217 + concurrency: int,\n 218 + requests: int,\n 219 + max_tokens: int,\n 220 + gpu_memory_utilization: float,\n 221 + max_num_seqs: int,\n 222 + num_scheduler_steps: int,\n 223 + request_timeout_s: int,\n 224 + startup_timeout_s: int,\n 225 +) -> dict:\n 226 + env = os.environ.copy()\n 227 + env.update(\n 228 + {\n 229 + \"PORT\": str(VLLM_PORT),\n 230 + \"MODEL_PATH\": DEFAULT_MODEL_PATH,\n 231 + \"MAX_NUM_BATCHED_TOKENS\": str(chunk),\n 232 + \"GPU_MEMORY_UTILIZATION\": str(gpu_memory_utilization),\n 233 + \"MAX_NUM_SEQS\": str(max_num_seqs),\n 234 + \"NUM_SCHEDULER_STEPS\": str(num_scheduler_steps),\n 235 + }\n 236 + )\n 237 + process = subprocess.Popen([\"/app/submission/launch_server.sh\"], env=env)\n 238 + try:\n 239 + wait_http_ready(process, VLLM_PORT, startup_timeout_s)\n 240 + result = run_inprocess_benchmark(\n 241 + port=VLLM_PORT,\n 242 + model_name=DEFAULT_MODEL_NAME,\n 243 + concurrency=concurrency,\n 244 + requests=requests,\n 245 + max_tokens=max_tokens,\n 246 + request_timeout_s=request_timeout_s,\n 247 + )\n 248 + result[\"config\"] = {\n 249 + \"chunk\": chunk,\n 250 + \"concurrency\": concurrency,\n 251 + \"requests\": requests,\n 252 + \"max_tokens\": max_tokens,\n 253 + \"gpu_memory_utilization\": gpu_memory_utilization,\n 254 + \"max_num_seqs\": max_num_seqs,\n 255 + \"num_scheduler_steps\": num_scheduler_steps,\n 256 + }\n 257 + return result\n 258 + finally:\n 259 + terminate_process(process)\n 260 +\n 261 +\n 262 @app.function(\n ⋮\n 320 ) -> dict:\n 274 - env = os.environ.copy()\n 275 - env.update(\n 276 - {\n 277 - \"PORT\": str(VLLM_PORT),\n 278 - \"MODEL_PATH\": DEFAULT_MODEL_PATH,\n 279 - \"MAX_NUM_BATCHED_TOKENS\": str(chunk),\n 280 - \"GPU_MEMORY_UTILIZATION\": str(gpu_memory_utilization),\n 281 - \"MAX_NUM_SEQS\": str(max_num_seqs),\n 282 - \"NUM_SCHEDULER_STEPS\": str(num_scheduler_steps),\n 283 - }\n 321 + return run_modal_benchmark_once(\n 322 + chunk=chunk,\n 323 + concurrency=concurrency,\n 324 + requests=requests,\n 325 + max_tokens=max_tokens,\n 326 + gpu_memory_utilization=gpu_memory_utilization,\n 327 + max_num_seqs=max_num_seqs,\n 328 + num_scheduler_steps=num_scheduler_steps,\n 329 + request_timeout_s=request_timeout_s,\n 330 + startup_timeout_s=startup_timeout_s,\n 331 )\n 285 - process = subprocess.Popen([\"/app/submission/launch_server.sh\"], env=env)\n 286 - try:\n 287 - wait_http_ready(process, VLLM_PORT, startup_timeout_s)\n 288 - result = run_inprocess_benchmark(\n 289 - port=VLLM_PORT,\n 290 - model_name=DEFAULT_MODEL_NAME,\n 291 - concurrency=concurrency,\n 292 - requests=requests,\n 293 - max_tokens=max_tokens,\n 294 - request_timeout_s=request_timeout_s,\n 295 - )\n 296 - result[\"config\"] = {\n 297 - \"chunk\": chunk,\n 298 - \"concurrency\": concurrency,\n 299 - \"requests\": requests,\n 300 - \"max_tokens\": max_tokens,\n 301 - \"gpu_memory_utilization\": gpu_memory_utilization,\n 302 - \"max_num_seqs\": max_num_seqs,\n 303 - \"num_scheduler_steps\": num_scheduler_steps,\n 304 - }\n 305 - return result\n 306 - finally:\n 307 - terminate_process(process)\n 332\n ⋮\n 344 def run_modal_chunk_sweep(\n 321 - chunks: list[int] = [4096, 8192, 16384],\n 345 + chunks_csv: str = \"4096,8192,16384\",\n 346 concurrency: int = 16,\n ⋮\n 353 results = []\n 354 + chunks = [int(item.strip()) for item in chunks_csv.split(\",\") if item.strip()]\n 355 for chunk in chunks:\n 356 results.append(\n 332 - run_modal_benchmark.local(\n 357 + run_modal_benchmark_once(\n 358 chunk=chunk,\n ⋮\n 364 num_scheduler_steps=num_scheduler_steps,\n 365 + request_timeout_s=900,\n 366 + startup_timeout_s=45 * MINUTES,\n 367 )\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The dedicated Modal benchmark entrypoints are in place. I’m checking local syntax and the Modal CLI surface for those functions before launching a remote B200 job.\n\n• Ran python3 -m py_compile serve_modal.py && python3 validate_submission.py\n └ submission validation ok\n\n• Ran modal run serve_modal.py::run_modal_benchmark --help | sed -n '1,220p'\n └ Usage: modal run serve_modal.py::run_modal_benchmark [OPTIONS]\n\n … +9 lines (ctrl + t to view transcript)\n --chunk INTEGER\n -h, --help Show this message and exit.\n\n• Ran modal run serve_modal.py::run_modal_chunk_sweep --help | sed -n '1,220p'\n └ Usage: modal run serve_modal.py::run_modal_chunk_sweep [OPTIONS]\n\n … +7 lines (ctrl + t to view transcript)\n --chunks-csv TEXT\n -h, --help Show this message and exit.\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The Modal CLI exposes the benchmark options correctly. I’m starting the cache prewarm for this workspace’s dedicated benchmark app now; this is directly on the path to real 8×B200 TPS because it avoids paying the 459 GB checkpoint download during benchmark startup.\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The dedicated prewarm run is building the image and pulling vLLM/FlashInfer/Torch dependencies. This is still before model download; I’m leaving it running and watching for the next stage.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The image build completed successfully with vllm==0.23.0, FlashInfer packages, and CUDA 13-era dependencies. The prewarm function should now move into the Hugging Face snapshot download into the Modal volume.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The dedicated app objects are created now, including the benchmark functions and a new web endpoint for this workspace. I’m waiting on the prewarm function execution and model cache population.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No new output yet; this likely means the remote prewarm function is inside snapshot_download. I’m continuing to wait rather than launching a benchmark that would compete for the same cache work.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no log output from prewarm. I’ll give it more time; if it stays silent too long, I’ll add explicit progress logging around the download in serve_modal.py for the next run.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Prewarm remains active with no emitted progress. Since this is a large snapshot download, silence is not unexpected, but the lack of progress logs is not ideal for iteration. I’m continuing this run for now.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm job is still running. I’m not starting any separate Modal app or touching existing apps; this is only the dedicated glm-5.2-nvfp4-bench run from this workspace.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no new output. I’m going to keep this prewarm run alive; the next useful benchmark step depends on the cache being populated.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The run has been silent for several minutes after object creation. That is consistent with snapshot_download on a large checkpoint, but if it fails we’ll need the failure output from this same run. I’m continuing to monitor.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No change yet. Once this returns, I’ll either launch the benchmark immediately or patch in progress logging if prewarm failed or timed out.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm is still active. I’m keeping the single run alive; starting the benchmark before cache population would add noise to the startup and TPS measurement.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still waiting on the prewarm function. This is the expected slow part of the workflow because the checkpoint is hundreds of GB.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No output yet. I’m continuing to monitor the same prewarm run; it is still the only Modal job I’ve started for this task.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm job remains active without progress logs. If this completes successfully, the next command will be a single benchmark run against the warmed volume, not a sweep yet.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no new output from prewarm. I’m leaving it running because interrupting it would waste the partial download work.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Prewarm is still running. The absence of logs means we cannot estimate completion yet, but the job has not failed locally.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still waiting on model cache prewarm. I’m not making unrelated changes while this runs.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm run continues. If it stays silent after this pass, I’ll check whether the Modal command has produced a completion/failure; otherwise I’ll keep it running to avoid restarting the checkpoint download.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No completion yet. This is still the dedicated prewarm job for the benchmark app.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still active. I’m continuing to wait because this cache warmup is prerequisite work for a meaningful 300+ TPS benchmark run.\n\n⚠ Heads up, you have less than 25% of your 5h limit left. Run /status for a breakdown.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No new logs yet. The next action remains to run the benchmark once this cache prewarm exits cleanly.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm is still running and silent. I’m keeping the local session attached; no other Modal apps are being touched.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still active. For a 459 GB snapshot, this can take a while even with transfer acceleration; I’m waiting it out.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No update from Modal yet. I’ll keep monitoring the same prewarm run.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The cache prewarm remains in progress. Once it exits, I’ll immediately run the benchmark function with chunk=8192, concurrency=16, requests=64, max_tokens=256 to get an initial TPS number.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no output. The prewarm run is still attached and active.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still waiting. I’m not starting a second benchmark or sweep until this cache step completes.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm job remains active. This is still directly tied to the benchmark target because the model cache needs to be ready before a clean TPS run.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No new output from the dedicated prewarm run yet. I’ll continue monitoring.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still active, still silent. The only useful action is to let the cache population finish or fail.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Prewarm is still running. I’ll keep this session attached and avoid any unrelated Modal operations.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No completion yet. This is expected to be the longest preparatory step before the first TPS measurement.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm job remains active and silent. I’m continuing to monitor it.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no new output. The next checkpoint is whether Modal returns success from prewarm_model_cache; until then I’m not altering the benchmark app.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No change yet. I’ll continue waiting on the same prewarm run.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The cache prewarm is still active. This is still useful progress toward a clean benchmark run because it avoids mixing download time into the B200 measurement.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still waiting on prewarm completion. No failures have surfaced in the attached Modal output.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No output yet. I’ll keep waiting.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The prewarm is still running. Once it completes, I’ll start with a single benchmark configuration rather than a sweep so we get a baseline TPS quickly.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still no new output. Continuing to monitor the same Modal run.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The cache prewarm remains in progress. I’m keeping the session attached and not touching any other Modal apps.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• No change yet. This is still the model-cache prewarm for the benchmark app.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• The run is still alive. I’m waiting for completion or an error from Modal.\n\n⚠ Heads up, you have less than 10% of your 5h limit left. Run /status for a breakdown.\n\n• Waited for background terminal · modal run serve_modal.py::prewarm_model_cache\n\n───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────\n\n• Still active. The first useful TPS run depends on this finishing, so I’m continuing to wait.\n\n• Working (28m 58s • esc to interrupt) · 1 background terminal running · /ps to view · /stop to close\n\n\n› Improve documentation in @filename\n\n gpt-5.5 xhigh · ~/Downloads/projects/glm-5.2-inference Goal paused (/goal resume)","created_at":"2026-06-23T06:36:10.755250Z","finish_reason":null,"metrics":null},{"message_id":"3b52a1d2-f726-4ad3-abb7-5d605a21ccc9","node_id":143,"role":"system","content":"\n You are a Summarizer that summarizes conversation history.\nYou will be shown a conversation between the user and the assistant, a coding agent. You should summarize the conversation + work done for future work to be continued by the coding agent.\n\nStructure your summary as follows:\n\n\n## Overview\nA high-level summary of what was being worked on and the overall goal (1-2 sentences).\n\n## Key Details & Breadcrumbs\nImportant details that may be needed later (key findings, decisions, constraints, error messages, progress or modified files, etc).\nFor each item, note:\n- What it is and why it matters\n- If it would be helpful to look at the original source, include citations to message ids or search terms to find the details in the history file\n\n## Current State\nWhat the agent was actively working on when this summary was created:\n- The immediate task or step in progress\n- Any pending actions or next steps that were planned\n- Blockers or questions that need resolution\n\n\nIMPORTANT: Do NOT reproduce or recite any rules, instructions, or guidelines that were included verbatim in the conversation (e.g., content inside or tags). Rules will be re-discovered and re-injected as needed when the agent accesses relevant files. Focus on summarizing the work done and decisions made, not the instructions themselves.\n\nNote, the full conversation will be saved to a history file (history_83f61430695d4797.md). The full path will be provided alongside the summary you create.\nBe concise but ensure someone could resume work using only your summary plus the reference file.","created_at":"2026-06-23T06:36:10.757675Z","finish_reason":null,"metrics":null},{"message_id":"4224aa60-b598-4d57-9d9c-63c5aa97dad5","node_id":144,"role":"user","content":"\nConversation to summarize:\n=== MESSAGE 0 - System ===\n\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1/Downloads (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Monday, 2026-06-22\n\n\n\n=== MESSAGE 1 - System ===\n\n\n\n\n\n\n=== MESSAGE 2 - User ===\nwhat current codex chats do i have running\n>\n\n=== MESSAGE 3 - System ===\n\nThe following skills can be invoked using the `skill` tool. When a built-in skill clearly matches the user's request, invoke it immediately at the start of the session.\n\n- **devin-for-terminal**: Look up Devin CLI documentation (skills, extensibility, configuration, commands, models, troubleshooting) (source: /Users/root1/.local/share/devin/cli/_versions/2026.7.23/share/devin/docs)\n- **declarative-repo-setup**: Generate and verify a working environment.yaml (Devin snapshot-setup blueprint) for a repo (source: builtin:drs)\n\n\n=== MESSAGE 4 - Assistant ===\n\n\n=== MESSAGE 5 - Tool ===\nOutput from command in shell 2e377a:\nroot1 18906 8.5 0.7 435878048 178336 s008 S+ 10:33PM 1:07.52 /opt/homebrew/lib/node_modules/@openai/codex/node_modules/@openai/codex-darwin-arm64/vendor/aarch64-apple-darwin/bin/codex --search --yolo\nroot1 31212 0.0 0.5 1892898832 121920 ?? S Sun07PM 1:20.68 /Applications/Cursor.app/Contents/MacOS/Cursor --goto /Users/root1/Documents/Codex/2026-06-21/ope/outputs/buffett_vs_sdan_sophomore_investor_note.md\nroot1 7529 0.0 0.0 435345664 4368 ?? S Sun06PM 0:00.00 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 7487 0.0 0.0 435346528 4368 ?? S Sun06PM 0:00.01 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 7384 0.0 0.2 435728976 49312 ?? S Sun06PM 0:09.40 /Users/root1/.codex/computer-use/Codex Computer Use.app/Contents/MacOS/SkyComputerUseService\nroot1 7340 0.0 1.1 1949377040 270432 ?? S Sun06PM 2:23.23 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Renderer).app/Contents/MacOS/Codex (Renderer) --type=renderer --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --lang=en-US --num-raster-threads=4 --enable-zero-copy --enable-gpu-memory-buffer-compositor-resources --enable-main-frame-before-activation --renderer-client-id=5 --time-ticks-at-unix-epoch=-1782089714172465 --launch-time-ticks=701611563 --shared-files --metrics-shmem-handle=1752395122,r,6498721906223495930,10134383406922357998,2097152 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708990997080739 --seatbelt-client=115\nroot1 7339 0.0 0.2 1949281632 47008 ?? S Sun06PM 0:01.29 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Renderer).app/Contents/MacOS/Codex (Renderer) --type=renderer --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --lang=en-US --num-raster-threads=4 --enable-zero-copy --enable-gpu-memory-buffer-compositor-resources --enable-main-frame-before-activation --renderer-client-id=6 --time-ticks-at-unix-epoch=-1782089714172465 --launch-time-ticks=701580623 --shared-files --metrics-shmem-handle=1752395122,r,15948780353101668027,14204077980647375359,2097152 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708991934122588 --seatbelt-client=115\nroot1 7319 0.0 0.2 435932336 61296 ?? S Sun06PM 1:16.18 /Applications/Codex.app/Contents/Resources/codex app-server --analytics-default-enabled\nroot1 7317 0.0 0.1 435469456 25168 ?? S Sun06PM 0:07.77 /Applications/Codex.app/Contents/Resources/native/bare-modifier-monitor --key DoubleCommand --immediate\nroot1 7302 0.0 0.2 486114320 50656 ?? S Sun06PM 0:04.88 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/...[1043 chars truncated]...ents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Service).app/Contents/MacOS/Codex (Service) --type=utility --utility-sub-type=network.mojom.NetworkService --lang=en-US --service-sandbox-type=network --user-data-dir=/Users/root1/Library/Application Support/Codex --standard-schemes=app --secure-schemes=app,sentry-ipc --bypasscsp-schemes=sentry-ipc --fetch-schemes=app,sentry-ipc --cors-schemes=sentry-ipc --streaming-schemes=app --start-stack-profiler --shared-files --metrics-shmem-handle=1752395122,r,6216236082941065097,17678615933841835920,524288 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708989122997041 --seatbelt-client=43\nroot1 7300 0.0 0.3 486509024 63008 ?? S Sun06PM 0:41.62 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/Codex (Service).app/Contents/MacOS/Codex (Service) --type=gpu-process --user-data-dir=/Users/root1/Library/Application Support/Codex --start-stack-profiler --gpu-preferences=SAAAAAAAAAAgAQAEAAAAAAAAAAAAAGAAAwAAAAAAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAQAAAAAAAAABAAAAAAAAAACAAAAAAAAAAIAAAAAAAAAA== --shared-files --metrics-shmem-handle=1752395122,r,4489318285131297286,3857477325938040057,262144 --field-trial-handle=1718379636,r,2994041699921063320,3025199963255209168,262144 --disable-features=DropInputEventsWhilePaintHolding,OwlAuth,OwlAutofillAndPasswords,OwlDownloads,OwlExtensions,OwlOpenAIGoLinks,OwlPermissions,OwlPrinting,OwlWebViewEnhancements --variations-seed-version --pseudonymization-salt-handle=1935764596,r,9231518228552695774,9330762696234617285,4 --trace-process-track-uuid=3190708988185955192 --seatbelt-client=43\nroot1 7292 0.0 0.0 485632384 7008 ?? S Sun06PM 0:00.00 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/browser_crashpad_handler --no-periodic-tasks --monitor-self-annotation=ptype=crashpad-handler --database=/Users/root1/Library/Application Support/Codex/Crashpad --annotation=plat=OS X --annotation=prod=Codex_Mac --annotation=ver=149.0.7827.115 --handshake-fd=4\nroot1 7290 0.0 0.0 485633536 7312 ?? S Sun06PM 0:00.01 /Applications/Codex.app/Contents/Frameworks/Codex Framework.framework/Versions/149.0.7827.115/Helpers/browser_crashpad_handler --monitor-self --monitor-self-annotation=ptype=crashpad-handler --database=/Users/root1/Library/Application Support/Codex/Crashpad --annotation=plat=OS X --annotation=prod=Codex_Mac --annotation=ver=149.0.7827.115 --handshake-fd=5\nroot1 7286 0.0 0.8 1953836208 199440 ?? S Sun06PM 2:41.22 /Applications/Codex.app/Contents/MacOS/Codex\nroot1 1263 0.0 0.0 435311872 5008 ?? S Sun05PM 0:00.16 /Users/root1/.codex/plugins/cache/openai-bundled/chrome/latest/extension-host/macos/arm64/extension-host chrome-extension://hehggadaopoacecdllhhajmbjkdcmajg/\nroot1 19002 0.0 0.0 435345968 8944 s008 S 10:33PM 0:00.00 /Applications/Codex.app/Contents/Resources/cua_node/bin/node_repl\nroot1 18905 0.0 0.1 436122928 36048 s008 S+ 10:33PM 0:00.09 node /opt/homebrew/bin/codex --search --yolo\nroot1 80693 0.0 0.1 435370720 26768 ?? S 7:10PM 0:00.81 /Users/root1/Library/Caches/com.openai.codex/org.sparkle-project.Sparkle/Launcher/CbP7B2xSI/Updater.app/Contents/MacOS/Updater /Applications/Codex.app 0\nroot1 80692 0.0 0.1 435377664 14560 ?? S 7:10PM 0:05.15 /Applications/Codex.app/Contents/Frameworks/Sparkle.framework/Versions/B/Autoupdate com.openai.codex /Users/root1 root1\n\n\nExit code: 0\n\n=== MESSAGE 6 - Assistant ===\nI see Codex CLI is running (PID 18906, started 10:33PM with `--search --yolo`). Let me check the active sessions.\n\n=== MESSAGE 7 - Tool ===\nOutput from command in shell 04e1be:\ntotal 0\ndrwxr-xr-x 38 root1 staff 1216 Jun 22 22:52 ..\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 2026\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 .\n---\ntotal 47392\n-rw-r--r-- 1 root1 staff 4161232 Jun 22 22:56 state_5.sqlite-wal\n-rw-r--r-- 1 root1 staff 502672 Jun 22 22:56 goals_1.sqlite-wal\n-rw-r--r-- 1 root1 staff 4808072 Jun 22 22:56 logs_2.sqlite-wal\n-rw-r--r-- 1 root1 staff 147165 Jun 22 22:56 models_cache.json\n-rw-r--r-- 1 root1 staff 12947456 Jun 22 22:56 logs_2.sqlite\n-rw-r--r-- 1 root1 staff 180224 Jun 22 22:56 state_5.sqlite\ndrwxr-x---+ 264 root1 staff 8448 Jun 22 22:56 ..\ndrwxr-xr-x 38 root1 staff 1216 Jun 22 22:52 .\n-rw-r--r-- 1 root1 staff 3846 Jun 22 22:52 .codex-global-state.json.bak\n-rw-r--r-- 1 root1 staff 3846 Jun 22 22:52 .codex-global-state.json\n-rw-r--r-- 1 root1 staff 40960 Jun 22 22:43 memories_1.sqlite\n-rw------- 1 root1 staff 554 Jun 22 22:37 history.jsonl\n-rw------- 1 root1 staff 2811 Jun 22 22:34 config.toml\ndrwxr-xr-x 3 root1 staff 96 Jun 22 22:33 attachments\ndrwxr-xr-x 4 root1 staff 128 Jun 22 22:33 shell_snapshots\ndrwxr-xr-x 7 root1 staff 224 Jun 22 22:32 .tmp\n-rw-r--r-- 1 root1 staff 32768 Jun 22 22:32 goals_1.sqlite-shm\n-rw-r--r-- 1 root1 staff 32768 Jun 22 22:32 state_5.sqlite-shm\n-rw-r--r-- 1 root1 staff 24576 Jun 22 22:32 goals_1.sqlite\n\n\nExit code: 0\n\n=== MESSAGE 8 - Assistant ===\n\n\n=== MESSAGE 9 - Tool ===\nOutput from command in shell cfcbb1:\n/Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n---\ntotal 0\ndrwxr-xr-x 5 root1 staff 160 Jun 22 22:33 06\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 .\ndrwxr-xr-x 3 root1 staff 96 Jun 19 18:06 ..\n\n\nExit code: 0\n\n=== MESSAGE 10 - Assistant ===\n\n\n=== MESSAGE 11 - Tool ===\nOutput from command in shell 45d889:\ntotal 1800\n-rw-r--r-- 1 root1 staff 910986 Jun 22 22:56 rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\ndrwxr-xr-x 3 root1 staff 96 Jun 22 22:33 .\ndrwxr-xr-x 5 root1 staff 160 Jun 22 22:33 ..\n---all recent---\n-rw-r--r-- 1 root1 staff 910986 Jun 22 22:56 /Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n-rw-r--r-- 1 root1 staff 945145 Jun 21 19:24 /Users/root1/.codex/sessions/2026/06/21/rollout-2026-06-21T17-26-12-019eecb8-74d0-7c83-b4f2-e90cadacf2a4.jsonl\n-rw-r--r-- 1 root1 staff 530676 Jun 19 23:01 /Users/root1/.codex/sessions/2026/06/19/rollout-2026-06-19T18-06-05-019ee290-42c6-7563-8b5b-627a9689a5d5.jsonl\n\n\nExit code: 0\n\n=== MESSAGE 12 - Assistant ===\n\n\n=== MESSAGE 13 - Tool ===\nOutput from command in shell 1c6568:\n\n\nExit code: 1\n\n=== MESSAGE 14 - Assistant ===\n\n\n=== MESSAGE 15 - Tool ===\nOutput from command in shell b21cce:\n{\"timestamp\":\"2026-06-23T05:33:29.980Z\",\"type\":\"session_meta\",\"payload\":{\"session_id\":\"019ef2f8-1156-71e1-8739-3a250421f23e\",\"id\":\"019ef2f8-1156-71e1-8739-3a250421f23e\",\"timestamp\":\"2026-06-23T05:33:24.182Z\",\"cwd\":\"/Users/root1/Downloads/projects/glm-5.2-inference\",\"originator\":\"codex-tui\",\"cli_version\":\"0.142.0\",\"source\":\"cli\",\"thread_source\":\"user\",\"model_provider\":\"openai\",\"base_instructions\":{\"text\":\"You are Codex, a coding agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled.\\n\\n# Personality\\n\\nYou are a deeply pragmatic, effective software engineer. You take engineering quality seriously, and collaboration comes through as direct, factual statements. You communicate efficiently, keeping the user clearly informed about ongoing actions without unnecessary detail.\\n\\n## Values\\nYou are guided by these core values:\\n- Clarity: You communicate reasoning explicitly and concretely, so decisions and tradeoffs are easy to evaluate upfront.\\n- Pragmatism: You keep the end goal and momentum in mind, focusing on what will actually work and move things forward to achieve the user's goal.\\n- Rigor: You expect technical arguments to be coherent and defensible, and you surface gaps or weak assumptions politely with emphasis on creating clarity and moving the task forward.\\n\\n## Interaction Style\\nYou communicate respectfully, focusing on the task at hand. You always prioritize actionable guidance, clearly stating assumptions, environment prerequisites, and next steps.\\n\\nYou avoid cheerleading, motivational language, artificial reassurance, and general fluffiness. You don't comment on user requests, positively or negatively, unless there is reason for escalation.\\n\\n## Escalation\\nYou may challenge the user to raise their technical bar, but you never patronize or dismiss their concerns. When presenting an alternative approach or solution to the user, you explain the reasoning behind the app\n\nExit code: 0\n\n=== MESSAGE 16 - Assistant ===\n\n\n=== MESSAGE 17 - Tool ===\nOutput from command in shell 3dbcaa:\n\n\nExit code: 1\n\n=== MESSAGE 18 - Assistant ===\n\n\n=== MESSAGE 19 - Tool ===\nOutput from command in shell bcfc28:\n719\n---types---\n 14 \"type\":\"add\"\n 84 \"type\":\"agent_message\"\n 27 \"type\":\"custom_tool_call_output\"\n 27 \"type\":\"custom_tool_call\"\n 2 \"type\":\"delete\"\n 5 \"type\":\"disabled\"\n 222 \"type\":\"event_msg\"\n 132 \"type\":\"function_call_output\"\n 134 \"type\":\"function_call\"\n 11 \"type\":\"input_text\"\n 92 \"type\":\"message\"\n 4 \"type\":\"open_page\"\n 84 \"type\":\"output_text\"\n 27 \"type\":\"patch_apply_end\"\n 71 \"type\":\"reasoning\"\n 491 \"type\":\"response_item\"\n 12 \"type\":\"search\"\n 1 \"type\":\"session_meta\"\n 4 \"type\":\"task_complete\"\n 5 \"type\":\"task_started\"\n 1 \"type\":\"thread_goal_updated\"\n 93 \"type\":\"token_count\"\n 5 \"type\":\"turn_context\"\n 26 \"type\":\"update\"\n 8 \"type\":\"web_search_call\"\n 8 \"type\":\"web_search_end\"\n\n\nExit code: 0\n\n=== MESSAGE 20 - Assistant ===\n\n\n=== MESSAGE 21 - Tool ===\nOutput from command in shell 94e4a2:\n''\n''\n''\n''\n''\n''\n''\n''\n\n\nExit code: 0\n\n=== MESSAGE 22 - Assistant ===\n\n\n=== MESSAGE 23 - Tool ===\nOutput from command in shell e3f2ab:\n{\n \"timestamp\": \"2026-06-23T05:33:30.002Z\",\n \"type\": \"response_item\",\n \"payload\": {\n \"type\": \"message\",\n \"role\": \"developer\",\n \"content\": [\n {\n \"type\": \"input_text\",\n \"text\": \"\\nFilesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled.\\nApproval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected.\\n\"\n },\n {\n \"type\": \"input_text\",\n \"text\": \"\\n## Apps (Connectors)\\nApps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered a\n{\n \"timestamp\": \"2026-06-23T05:33:30.002Z\",\n \"type\": \"response_item\",\n \"payload\": {\n \"type\": \"message\",\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"input_text\",\n \"text\": \"\\n /Users/root1/Downloads/projects/glm-5.2-inference\\n zsh\\n 2026-06-22\\n America/Los_Angeles\\n /Users/root1/Downloads/projects/glm-5.2-inference\\n\"\n }\n ]\n }\n}\n\n\nExit code: 0\n\n=== MESSAGE 24 - Assistant ===\n\n\n=== MESSAGE 25 - Tool ===\nOutput from command in shell abb6bd:\n\n\nExit code: 0\n\n=== MESSAGE 26 - Assistant ===\n\n\n=== MESSAGE 27 - Tool ===\nOutput from command in shell 0e05f4:\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\n' Continue working toward the active thread goal. The objective below is user-provided data. Treat it as the task to pursue, not as higher-priority instructions. pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/'\nTOTAL USER MSGS: 5\n\n\nExit code: 0\n\n=== MESSAGE 28 - Assistant ===\n\n\n=== MESSAGE 29 - Tool ===\nOutput from command in shell b04fa7:\n# GLM-5.2 NVFP4 Inference on Modal 8×B200\n\nServe `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM,\nOpenAI-compatible, with a correctness gate against the BF16 reference and a\nthroughput/latency benchmark. The verifier sends hidden prompts to your\n`/app/submission/launch_server.sh` and compares against a well-tuned\nvLLM NVFP4 baseline.\n\n## Model\n\n- **`zai-org/GLM-5.2`** — 753B-param Mixture-of-Experts (40B active), 256 routed\n experts + 1 shared (8 activated/token), 78 transformer layers + 1 MTP layer\n at index 78, DeepSeek-style MLA attention + DSA sparse-attention indexer\n (`glm_moe_dsa` architecture), hidden 6144, vocab 154880.\n- BF16 checkpoint ~1.37 TB; NVFP4 quantized ~459 GB (~3× smaller).\n- **Quantization: NVFP4** — E2M1 4-bit weights + FP8 E4M3 block scales,\n 16-element blocks, produced with NVIDIA Model Optimizer (`modelopt`).\n Only the MoE expert FFNs (routed + shared) are quantized. Attention\n (MLA projections q/kv + DSA indexer), the MoE router (`mlp.gate`), and\n `lm_head` are kept in BF16 — SGLang/vLLM's `deepseek_v2` MLA path cannot\n consume NVFP4 attention weights.\n- **KV cache: FP8** (`--kv-cache-dtype fp8`) to maximize context headroom.\n- License: MIT (pure open).\n- Use a community NVFP4 checkpoint: `Lorbus/GLM-5.2-NVFP4`,\n `Mapika/GLM-5.2-NVFP4`, or `lukealonso/GLM-5.2-NVFP4`. Pick one and pin\n the revision in `launch_server.sh`.\n\n## Hardware\n\n- **Modal 8×B200, single node** — `@app.function(gpu=\"B200:8\")`. 8× B200 on\n one physical machine, 8×192 GB HBM = 1536 GB total. NVFP4 tensor cores are\n Blackwell-only. This is the entire GPU budget — no second node, no\n disaggregated prefill/decode pools. One vLLM engine, TP=8, collocated\n prefill+decode.\n- Weights ~459 GB → ~57 GB/GPU at TP=8, leaving ~135 GB/GPU for KV cache,\n CUDA graphs, and runtime buffers. TP=8 is mandatory: TP=4 would split the\n node into two 4-GPU engines and leave only ~77 GB/GPU for KV — too tight for\n GLM-5.2's context window, and you cannot run two TP=8 engines on 8 GPUs.\n- **No disaggregation.** Disaggregated prefill/decode requires a second\n 8×B200 node (one TP=8 prefill + one TP=8 decode with NIXL/Mooncake KV\n transfer). On a single node, use **chunked prefill**\n (`--enable-chunked-prefill` with a tuned `--max-num-batched-tokens` chunk\n size) to get the ITL-stability benefit of disaggregation without splitting\n the GPU pool.\n\n## Tools / stack\n\n- **vLLM v0.23.0+** — required for `modelopt` / `modelopt_fp4` NVFP4 support\n and the `glm_moe_dsa` model registration. Pin a known-good version\n (e.g. `vllm==0.23.0` or newer) in the Modal image.\n---ls---\npasted-text-1.txt\n\n\nExit code: 0\n\n=== MESSAGE 30 - Assistant ===\nLet me also check the Codex desktop app's recent threads.\n\n=== MESSAGE 31 - Tool ===\nOutput from command in shell b048f1:\n_sqlx_migrations remote_control_enrollments \nagent_job_items thread_dynamic_tools \nagent_jobs thread_spawn_edges \nbackfill_state threads \nexternal_agent_config_imports\n---\n_sqlx_migrations thread_goals \n\n\nExit code: 0\n\n=== MESSAGE 32 - Assistant ===\n\n\n=== MESSAGE 33 - Tool ===\nOutput from command in shell 281960:\n_sqlx_migrations\nthreads\nsqlite_sequence\nthread_dynamic_tools\nbackfill_state\nagent_jobs\nagent_job_items\nthread_spawn_edges\nremote_control_enrollments\nexternal_agent_config_imports\n---threads schema---\nCREATE TABLE threads (\n id TEXT PRIMARY KEY,\n rollout_path TEXT NOT NULL,\n created_at INTEGER NOT NULL,\n updated_at INTEGER NOT NULL,\n source TEXT NOT NULL,\n model_provider TEXT NOT NULL,\n cwd TEXT NOT NULL,\n title TEXT NOT NULL,\n sandbox_policy TEXT NOT NULL,\n approval_mode TEXT NOT NULL,\n tokens_used INTEGER NOT NULL DEFAULT 0,\n has_user_event INTEGER NOT NULL DEFAULT 0,\n archived INTEGER NOT NULL DEFAULT 0,\n archived_at INTEGER,\n git_sha TEXT,\n git_branch TEXT,\n git_origin_url TEXT\n, cli_version TEXT NOT NULL DEFAULT '', first_user_message TEXT NOT NULL DEFAULT '', agent_nickname TEXT, agent_role TEXT, memory_mode TEXT NOT NULL DEFAULT 'enabled', model TEXT, reasoning_effort TEXT, agent_path TEXT, created_at_ms INTEGER, updated_at_ms INTEGER, thread_source TEXT, preview TEXT NOT NULL DEFAULT '', recency_at INTEGER NOT NULL DEFAULT 0, recency_at_ms INTEGER NOT NULL DEFAULT 0);\nCREATE INDEX idx_threads_created_at ON threads(created_at DESC, id DESC);\nCREATE INDEX idx_threads_updated_at ON threads(updated_at DESC, id DESC);\nCREATE INDEX idx_threads_archived ON threads(archived);\nCREATE INDEX idx_threads_source ON threads(source);\nCREATE INDEX idx_threads_provider ON threads(model_provider);\nCREATE TRIGGER threads_created_at_ms_after_insert\nAFTER INSERT ON threads\nWHEN NEW.created_at_ms IS NULL\nBEGIN\n UPDATE threads\n SET created_at_ms = NEW.created_at * 1000\n WHERE id = NEW.id;\nEND;\nCREATE TRIGGER threads_updated_at_ms_after_insert\nAFTER INSERT ON threads\nWHEN NEW.updated_at_ms IS NULL\nBEGIN\n UPDATE threads\n SET updated_at_ms = NEW.updated_at * 1000\n WHERE id = NEW.id;\nEND;\n\n\nExit code: 0\n\n=== MESSAGE 34 - Assistant ===\n\n\n=== MESSAGE 35 - Tool ===\nOutput from command in shell 50f208:\n019ef2f8-1156-71e1-8739-3a250421f23e||cli|/Users/root1/Downloads/projects/glm-5.2-inference|2026-06-22 22:57:26|0|0\n019eecb8-74d0-7c83-b4f2-e90cadacf2a4|open a chrome browser with my profile, download all fo warrren buffets public writing, shareholder memos, lectures etc, if theres useful video on youtube, use youtubetotranscript.com to pull transcripts|vscode|/Users/root1/Documents/Codex/2026-06-21/ope|2026-06-21 19:24:21|0|0\n019ee290-42c6-7563-8b5b-627a9689a5d5|Open Chrome window|vscode|/Users/root1/Documents/Codex/2026-06-19/open-a-new-chrome-window-go|2026-06-19 23:01:28|0|0\n\n\nExit code: 0\n\n=== MESSAGE 36 - Assistant ===\n\n\n=== MESSAGE 37 - Tool ===\nOutput from command in shell 1ea34c:\nCREATE TABLE agent_jobs (\n id TEXT PRIMARY KEY,\n name TEXT NOT NULL,\n status TEXT NOT NULL,\n instruction TEXT NOT NULL,\n output_schema_json TEXT,\n input_headers_json TEXT NOT NULL,\n input_csv_path TEXT NOT NULL,\n output_csv_path TEXT NOT NULL,\n auto_export INTEGER NOT NULL DEFAULT 1,\n created_at INTEGER NOT NULL,\n updated_at INTEGER NOT NULL,\n started_at INTEGER,\n completed_at INTEGER,\n last_error TEXT\n, max_runtime_seconds INTEGER);\nCREATE INDEX idx_agent_jobs_status ON agent_jobs(status, updated_at DESC);\n---active jobs---\n\n\nExit code: 0\n\n=== MESSAGE 38 - Assistant ===\nHere's what's currently running on your machine:\n\n## Active Codex CLI session (1 running)\n\n**PID 18906** — `codex --search --yolo`, started 10:33 PM tonight\n- **Thread ID:** `019ef2f8-1156-71e1-8739-3a250421f23e`\n- **CWD:** `/Users/root1/Downloads/projects/glm-5.2-inference`\n- **Source:** `cli` (Codex TUI v0.142.0, GPT-5, `danger-full-access` sandbox)\n- **Task:** GLM-5.2 NVFP4 inference on Modal 8×B200 — serve `zai-org/GLM-5.2` quantized to NVFP4 via vLLM, OpenAI-compatible, with a correctness gate vs BF16 reference and a throughput/latency benchmark. The objective was pasted from `/Users/root1/.codex/attachments/ec1b915e-…/pasted-text-1.txt`.\n- Last DB update: 22:57:26 (still active, 719 events logged, 5 user turns so far).\n\n## Other recent Codex threads (not currently running)\n\n| Thread ID | Title | Source | Last update |\n|---|---|---|---|\n| `019eecb8…` | \"open a chrome browser with my profile, download all of Warren Buffetts public writing, shareholder memos, lectures…\" | vscode | 2026-06-21 19:24 |\n| `019ee290…` | \"Open Chrome window\" | vscode | 2026-06-19 23:01 |\n\n## Also running (background Codex.app processes, not chats)\n- `Codex.app` desktop app (PID 7286) and its helpers — the GUI is open but no active CLI chat is bound to it.\n- Cursor (PID 31212) has a tab open on `/Users/root1/Documents/Codex/2026-06-21/ope/outputs/buffett_vs_sdan_sophomore_investor_note.md` — that's an output file from the 06-21 Buffett thread, not a live chat.\n\nSo: **exactly one Codex chat is actively running right now** — the GLM-5.2 NVFP4/Modal task in the CLI. Want me to tail its live rollout to see what it's currently doing?\n\n=== MESSAGE 39 - User ===\nget all the messages from human and codex in there\n\n=== MESSAGE 40 - Assistant ===\n\n\n=== MESSAGE 41 - Tool ===\nOutput from command in shell 23c4d7:\n 187\n\n\nExit code: 0\n\n=== MESSAGE 42 - Assistant ===\n\n\n=== MESSAGE 43 - Tool ===\nOutput from command in shell 3ef06c:\nTotal events: 3\n================================================================================\n\n[2026-06-23T05:33:30.002Z] DEV:\n\n## Skills\nA skill is a set of instructions provided through a `SKILL.md` source. Below is the list of skills that can be used. Each entry includes a name, description, and source locator. `file` locators are on the host filesystem, `environment resource` locators are owned by an execution environment, `orchestrator resource` locators are opaque non-filesystem resources, and `custom resource` locators use their provider's access mechanism.\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: /Users/root1/.codex/skills/.system/imagegen/SKILL.md)\n- openai-docs: Use when the user asks how to build with OpenAI products or APIs, asks about Codex itself or choosing Codex surfaces, needs up-to-date official documentation with citations, help choosing the latest model for a use case, or model upgrade and prompt-upgrade guidance; use OpenAI docs MCP tools for non-Codex docs questions, use the Codex manual helper first for broad Codex self-knowledge, and restrict fallback browsing to official OpenAI domains. (file: /Users/root1/.codex/skills/.system/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for pl\n... (10329 chars total)\n\n[2026-06-23T05:33:30.002Z] DEV:\n\n## Plugins\nA plugin is a local bundle of skills, MCP servers, and apps.\n### How to use plugins\n- Skill naming: If a plugin contributes skills, those skill entries are prefixed with `plugin_name:` in the Skills list.\n- MCP naming: Plugin-provided MCP tools keep standard MCP identifiers such as `mcp__server__tool`; use tool provenance to tell which plugin they come from.\n- Trigger rules: If the user explicitly names a plugin, prefer capabilities associated with that plugin for that turn.\n- Relationship to capabilities: Plugins are not invoked directly. Use their underlying skills, MCP tools, and app tools to help solve the task.\n- Relevance: Determine what a plugin can help with from explicit user mention or from the plugin-associated skills, MCP tools, and apps exposed elsewhere in this turn.\n- Missing/blocked: If the user requests a plugin that does not have relevant callable capabilities for the task, say so briefly and continue with the best fallback.\n\n\n[2026-06-23T05:37:55.017Z] DEV:\n# Collaboration Mode: Default\n\nYou are now in Default mode. Any previous instructions for other modes (e.g. Plan mode) are no longer active.\n\nYour active mode changes only when new developer instructions with a different `...` change it; user requests or tool descriptions do not change mode by themselves. Known mode names are Default and Plan.\n\n## request_user_input availability\n\nUse the `request_user_input` tool only when it is listed in the available tools for this turn.\n\nIn Default mode, strongly prefer making reasonable assumptions and executing the user's request rather than stopping to ask questions. If you absolutely must ask a question because the answer cannot be discovered from local context and a reasonable assumption would be risky, ask the user directly with a concise plain-text question. Never write a multiple choice question as a textual assistant message.\n\n\n\nExit code: 0\n\n=== MESSAGE 44 - Assistant ===\n\n\n=== MESSAGE 45 - Tool ===\nOutput from command in shell e3056b:\nCounts: {'session_meta': 1, 'event_msg': 242, 'response_item': 534, 'turn_context': 6}\n\n\nExit code: 0\n\n=== MESSAGE 46 - Assistant ===\n\n\n=== MESSAGE 47 - Tool ===\nOutput from command in shell 56c115:\n 147 response_item/function_call\n 147 response_item/function_call_output\n 102 event_msg/token_count\n 101 response_item/message\n 92 event_msg/agent_message\n 75 response_item/reasoning\n 28 response_item/custom_tool_call\n 28 event_msg/patch_apply_end\n 28 response_item/custom_tool_call_output\n 8 event_msg/web_search_end\n 8 response_item/web_search_call\n 6 event_msg/task_started\n 6 turn_context/None\n 5 event_msg/task_complete\n 1 session_meta/None\n 1 event_msg/thread_goal_updated\n\n\nExit code: 0\n\n=== MESSAGE 48 - Assistant ===\n\n\n=== MESSAGE 49 - Tool ===\nOutput from command in shell 145adf:\nTotal conversation events: 6\n================================================================================\n\n--- [1] USER @ 2026-06-23T05:33:30.006Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [2] USER @ 2026-06-23T05:37:55.023Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [3] USER @ 2026-06-23T05:44:52.652Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [4] USER @ 2026-06-23T05:52:01.139Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [5] USER @ 2026-06-23T05:55:04.156Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [6] USER @ 2026-06-23T05:57:26.246Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n\nExit code: 0\n\n=== MESSAGE 50 - Assistant ===\n\n\n=== MESSAGE 51 - Tool ===\nOutput from command in shell 81b5ea:\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"I\\u2019ll read the attached objective first, then inspect the repo state so the next work is grounded in what\\u2019s actually present.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"The objective is a GLM-5.2 NVFP4 Modal serving task. This directory is not a git repo, and `rg --files` returned no tracked-looking files, so I\\u2019m checking the actual filesystem layout before editing anything.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\nkeys: ['type', 'message', 'phase', 'memory_citation']\n{\"type\": \"agent_message\", \"message\": \"The workspace is empty, so there is no existing implementation to preserve. I\\u2019m going to scaffold the required submission files in this project using the objective\\u2019s paths and keep the server command configurable for benchmark sweeps.\", \"phase\": \"commentary\", \"memory_citation\": null}\n---\ntotal agent_message: 93\n\n\nExit code: 0\n\n=== MESSAGE 52 - Assistant ===\nFound it — agent messages use `message` not `text`. Let me extract the full conversation.\n\n=== MESSAGE 53 - Tool ===\nOutput from command in shell 43fa0d:\nWrote 99 events to /tmp/codex_convo.txt\nSize: 32717 bytes\n\n\nExit code: 0\n\n=== MESSAGE 54 - Assistant ===\n\n\n=== MESSAGE 55 - Tool ===\n\n 1|Total conversation events: 99\n 2|================================================================================\n 3|\n 4|--- [1] USER @ 2026-06-23T05:33:30.006Z ---\n 5|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n 6|\n 7|--- [2] CODEX(commentary) @ 2026-06-23T05:33:36.328Z ---\n 8|I’ll read the attached objective first, then inspect the repo state so the next work is grounded in what’s actually present.\n 9|\n 10|--- [3] CODEX(commentary) @ 2026-06-23T05:33:44.863Z ---\n 11|The objective is a GLM-5.2 NVFP4 Modal serving task. This directory is not a git repo, and `rg --files` returned no tracked-looking files, so I’m checking the actual filesystem layout before editing anything.\n 12|\n 13|--- [4] CODEX(commentary) @ 2026-06-23T05:33:49.578Z ---\n 14|The workspace is empty, so there is no existing implementation to preserve. I’m going to scaffold the required submission files in this project using the objective’s paths and keep the server command configurable for benchmark sweeps.\n 15|\n 16|--- [5] CODEX(commentary) @ 2026-06-23T05:33:53.584Z ---\n 17|I’m writing the launch script first because it is the verifier entrypoint. It will default to a pinned community checkpoint, honor `PORT` and `MODEL_PATH`, expose tuning env vars, and print the exact vLLM config at startup for auditability.\n 18|\n 19|--- [6] CODEX(commentary) @ 2026-06-23T05:34:14.386Z ---\n 20|The launcher is in place. Next I’m adding the Modal app that installs the serving stack, mounts persistent caches, copies the project submission into `/app/submission`, and exposes a single 8×B200 web server.\n 21|\n 22|--- [7] CODEX(commentary) @ 2026-06-23T05:34:21.805Z ---\n 23|I’m also adding an empty patch hook under `submission/` now. That keeps the startup contract stable if later tuning needs to apply site-packages patches without changing the verifier entrypoint.\n 24|\n 25|--- [8] CODEX(commentary) @ 2026-06-23T05:34:43.692Z ---\n 26|The Modal wrapper is mostly mechanical, but I’m tightening one behavior: the web function should wait on the launched vLLM process so Modal keeps the container alive while the server is running.\n 27|\n 28|--- [9] CODEX(commentary) @ 2026-06-23T05:34:48.181Z ---\n 29|I’m adding the local wrappers the objective names, but keeping them lightweight: they validate the OpenAI-compatible surface, can launch the same submission entrypoint, and allow benchmark sweeps without baking tuning values into code.\n 30|\n 31|--- [10] CODEX(commentary) @ 2026-06-23T05:35:35.474Z ---\n 32|The expected files are present now. I’m going to run static checks that do not require the 8×B200 host or the 459 GB model, then inspect the final file list and launcher contents.\n 33|\n 34|--- [11] CODEX(commentary) @ 2026-06-23T05:35:47.356Z ---\n 35|Static checks passed. One remaining risk is the exact Modal SDK method names because this environment does not have the Modal package imported during compilation, so I’m checking the installed Python environment before deciding whether to adjust the wrapper.\n 36|\n 37|--- [12] CODEX(commentary) @ 2026-06-23T05:36:23.975Z ---\n 38|I checked the public Hugging Face model pages and switched the default checkpoint to Mapika’s NVFP4 build because its card explicitly says the expert FFNs are NVFP4 while attention/DSA/router/lm_head stay BF16, matching the correctness constraints. I’m pinning the current commit instead of using `main`.\n 39|\n 40|--- [13] CODEX(commentary) @ 2026-06-23T05:36:39.917Z ---\n 41|The checkpoint is pinned now. I’m rerunning syntax checks and a Modal import check so the scaffold fails early if the wrapper uses an invalid SDK surface.\n 42|\n 43|--- [14] CODEX(commentary) @ 2026-06-23T05:36:48.725Z ---\n 44|The checks passed, and Python generated `__pycache__` files during compilation. I’m removing those gene...[12824 chars truncated]...e single-server scripts can detect a server that exits during startup instead of waiting for the full startup timeout.\n185|\n186|--- [51] CODEX(commentary) @ 2026-06-23T05:46:55.405Z ---\n187|The process handling is now using the Python verifier directly, which lets it monitor the launched server process. I noticed the sweep record should capture the final server return code after termination, so I’m correcting that bookkeeping before checks.\n188|\n189|--- [52] CODEX(commentary) @ 2026-06-23T05:46:59.495Z ---\n190|The sweep and optimize scripts are hardened. I’m running syntax checks and dry-run checks for the new workflow now.\n191|\n192|--- [53] CODEX(commentary) @ 2026-06-23T05:47:22.586Z ---\n193|The new sweep CLI parses as expected. I’m testing the early-exit path with a fake `vllm` that exits immediately, which validates that `optimize.py` does not sit through the full startup timeout when the server command is bad.\n194|\n195|--- [54] CODEX(commentary) @ 2026-06-23T05:47:37.866Z ---\n196|The fake failure surfaced a real edge: the readiness loop could time out before noticing a process that died just after the first poll. I’m tightening that loop so it checks process exit before and after each connection attempt and bounds sleeps by the remaining deadline.\n197|\n198|--- [55] CODEX(commentary) @ 2026-06-23T05:47:57.385Z ---\n199|I’m rerunning the fake early-exit case to verify it now reports the server process exit instead of a generic readiness timeout.\n200|\n201|--- [56] CODEX(commentary) @ 2026-06-23T05:48:13.512Z ---\n202|The early-exit behavior is now correct. I’m checking the pinned Hugging Face config files next, because correctness depends on the checkpoint actually advertising the expected GLM architecture and NVFP4 ModelOpt quantization at the pinned revision.\n203|\n204|--- [57] CODEX(commentary) @ 2026-06-23T05:48:26.014Z ---\n205|The pinned repo has the expected `GlmMoeDsaForCausalLM` architecture and `hf_quant_config.json` with `quant_algo: NVFP4`. I’m parsing the quant config now to confirm the sensitive modules are excluded rather than trusting the README text.\n206|\n207|--- [58] CODEX(commentary) @ 2026-06-23T05:48:46.294Z ---\n208|The Mapika quant config is not as strong as expected: it excludes `lm_head` and all self-attention modules, but I did not find router `mlp.gate` exclusions in `hf_quant_config.json`. Since the objective explicitly calls that out for correctness, I’m comparing the other community checkpoints’ small config files before deciding whether to switch defaults.\n209|\n210|--- [59] CODEX(commentary) @ 2026-06-23T05:49:01.510Z ---\n211|The other listed options do not currently look better from accessible metadata: Lorbus is not publicly accessible from this environment, and lukealonso’s repo lacks `hf_quant_config.json`. I’m checking whether lukealonso encodes quantization in `config.json`; if not, Mapika remains the only metadata-complete public option.\n212|\n213|--- [60] CODEX(commentary) @ 2026-06-23T05:49:08.692Z ---\n214|lukealonso uses a different quantization metadata shape in `config.json`. I’m parsing its ignore list exactly to see whether it protects routers or only attention/shared pieces.\n215|\n216|--- [61] CODEX(commentary) @ 2026-06-23T05:49:23.720Z ---\n217|The metadata alone is ambiguous because router weights may not be represented through the same exclude mechanism. I’m checking the safetensors index names for router tensors and quantization side tensors; that gives better evidence about whether gate weights were actually quantized.\n218|\n219|--- [62] CODEX(commentary) @ 2026-06-23T05:49:46.936Z ---\n220|The safetensors index resolves the router concern for Mapika: router `mlp.gate` tensors have only weights/biases, while quantized expert/shared projections have scale tensors. I’m checking the MTP layer presence now, because the objective calls out layer 78 as a correctness-sensitive detail.\n221|\n222|--- [63] CODEX(commentary) @ 2026-06-23T05:50:19.479Z ---\n\n\n=== MESSAGE 56 - Assistant ===\n\n\n=== MESSAGE 57 - Tool ===\n\n222|--- [63] CODEX(commentary) @ 2026-06-23T05:50:19.479Z ---\n223|Mapika is still the safer default for vLLM ModelOpt serving because it has `hf_quant_config.json`; lukealonso is the only accessible checkpoint with layer 78 MTP tensors, but it lacks that ModelOpt quant config file. I’m adding a reproducible checkpoint audit script and documenting this tradeoff so the checkpoint choice is evidence-based rather than implicit.\n224|\n225|--- [64] CODEX(commentary) @ 2026-06-23T05:50:56.650Z ---\n226|The checkpoint audit script is added. I’m running it for the pinned default now, then I’ll run the full static check set and clean generated artifacts.\n227|\n228|--- [65] CODEX(commentary) @ 2026-06-23T05:51:08.595Z ---\n229|The first audit output mixed router `mlp.gate` with dense `mlp.gate_proj` names because of a broad substring match. I’m splitting those categories so the evidence about router precision is accurate.\n230|\n231|--- [66] CODEX(commentary) @ 2026-06-23T05:51:20.483Z ---\n232|I’m rerunning the checkpoint audit after the category split to confirm router gates are reported separately from dense `gate_proj` tensors.\n233|\n234|--- [67] CODEX(commentary) @ 2026-06-23T05:51:31.712Z ---\n235|The audit now shows router gate tensors separately: router gates only have weights and correction biases, while dense `gate_proj` tensors carry scale tensors. I’m running the final static checks and cleaning generated files.\n236|\n237|--- [68] CODEX(commentary) @ 2026-06-23T05:51:40.447Z ---\n238|All static checks passed. I’m removing bytecode and taking the final file inventory now.\n239|\n240|--- [69] CODEX(commentary) @ 2026-06-23T05:51:44.462Z ---\n241|The workspace is not a git repository, so there is no git diff/status to report. Only source files remain.\n242|\n243|--- [70] CODEX(final_answer) @ 2026-06-23T05:52:01.072Z ---\n244|Continued progress on the GLM-5.2 NVFP4 submission.\n245|\n246|Added:\n247|- [sweep_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/sweep_dev_bench.py:1): runs reproducible sweeps over `MAX_NUM_BATCHED_TOKENS`, `GPU_MEMORY_UTILIZATION`, `MAX_NUM_SEQS`, and `NUM_SCHEDULER_STEPS`, writing JSONL results.\n248|- [inspect_checkpoint.py](/Users/root1/Downloads/projects/glm-5.2-inference/inspect_checkpoint.py:1): audits the pinned HF checkpoint metadata and tensor index.\n249|- README updates for sweep and checkpoint audit commands.\n250|\n251|Hardened:\n252|- [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1) now uses the verifier/benchmark functions directly and monitors the launched server process.\n253|- [verify_serving.py](/Users/root1/Downloads/projects/glm-5.2-inference/verify_serving.py:1) and [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1) now detect early server exit during readiness checks.\n254|\n255|Checkpoint audit evidence for `Mapika/GLM-5.2-NVFP4@5f9c62a4...`:\n256|- Has `config.json`, `hf_quant_config.json`, and `model.safetensors.index.json`.\n257|- `hf_quant_config.json` reports ModelOpt `NVFP4`, group size 16.\n258|- Router `mlp.gate` tensors have only `weight` and `e_score_correction_bias`; dense `gate_proj`, expert, and shared expert tensors have scale tensors.\n259|- No `model.layers.78`, so the launcher intentionally does not enable MTP.\n260|\n261|Verification run:\n262|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n263|- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py sweep_dev_bench.py inspect_checkpoint.py`\n264|- `python3 sweep_dev_bench.py --dry-run ...`\n265|- Fake failing `vllm` test confirmed early-exit detection works.\n266|\n267|Still not complete: the actual Modal 8×B200 run, full model load, correctness gate, and throughput benchmark remain unverified because they require the target GPU environment and cached checkpoint.\n268|\n269|--- [71] USER @ 2026-06-23T05:52:01.139Z -...[5704 chars truncated]... exercises `/v1/models`, non-stream chat verification, and streaming benchmark parsing with concurrent requests.\n340|\n341|--- [89] CODEX(commentary) @ 2026-06-23T05:56:40.733Z ---\n342|The self-test passed. I’m also exercising `optimize.py` against the mock server through its normal `--script` launch path, so the “launch once, verify, benchmark, terminate” path is covered too.\n343|\n344|--- [90] CODEX(commentary) @ 2026-06-23T05:56:50.464Z ---\n345|The mock launch path passed for both `optimize.py` and a one-config `sweep_dev_bench.py` run, including JSONL output. I’m doing the final static pass and cleaning bytecode.\n346|\n347|--- [91] CODEX(commentary) @ 2026-06-23T05:57:08.716Z ---\n348|The static pass and metadata audit are clean. I’m removing generated bytecode and taking the final source inventory.\n349|\n350|--- [92] CODEX(commentary) @ 2026-06-23T05:57:13.719Z ---\n351|There is no git repository in this directory, so no git status is available. The final file inventory contains only source files and scripts.\n352|\n353|--- [93] CODEX(final_answer) @ 2026-06-23T05:57:26.167Z ---\n354|Added local end-to-end wrapper tests without changing the verifier entrypoint.\n355|\n356|New files:\n357|- [mock_openai_server.py](/Users/root1/Downloads/projects/glm-5.2-inference/mock_openai_server.py:1): tiny OpenAI-compatible mock server for `/v1/models` and `/v1/chat/completions`, including streaming SSE.\n358|- [selftest_wrappers.py](/Users/root1/Downloads/projects/glm-5.2-inference/selftest_wrappers.py:1): starts the mock server, runs `verify_serving`, serial benchmark, and concurrent benchmark.\n359|- Updated [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1) with the self-test command.\n360|\n361|Validated:\n362|- `python3 selftest_wrappers.py`\n363|- `python3 optimize.py --script ...`\n364|- `python3 sweep_dev_bench.py --script ...`\n365|- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n366|- `python3 -m py_compile ...`\n367|- `python3 inspect_checkpoint.py`\n368|\n369|This gives stronger evidence that the verification/benchmark tooling works around an OpenAI-compatible server, including streaming TTFT and concurrent requests. The actual 8×B200 Modal run, full GLM checkpoint load, correctness gate, and throughput benchmark are still unverified because they require the target GPU environment.\n370|\n371|--- [94] USER @ 2026-06-23T05:57:26.246Z ---\n372|pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n373|\n374|--- [95] CODEX(commentary) @ 2026-06-23T05:57:48.711Z ---\n375|I’ll inspect the objective and current tree again, then I’ll target the remaining things we can verify locally: packaging, command composition, and small runtime contract tests.\n376|\n377|--- [96] CODEX(commentary) @ 2026-06-23T05:58:27.182Z ---\n378|One runtime risk remains from the Modal image env: it sets `MODEL_REVISION` globally, so a future Modal `MODEL_PATH` override could accidentally inherit the Mapika SHA. The launcher already knows how to pin the default, so I’m removing that env value from the image.\n379|\n380|--- [97] CODEX(commentary) @ 2026-06-23T05:58:31.951Z ---\n381|I’m making the Modal env change first; the prewarm function still uses the pinned revision through its function default, and the launcher still pins the default repo internally.\n382|\n383|--- [98] CODEX(commentary) @ 2026-06-23T05:58:38.892Z ---\n384|Next I’m adding a small validator that checks the key local invariants: submission file exists/executable, launcher command composition preserves TP=8/FP8/chunking, local model paths do not get the default revision, and Modal is configured for `B200:8`.\n385|\n386|--- [99] CODEX(commentary) @ 2026-06-23T05:59:05.225Z ---\n387|The validator is added. I’m running it now along with the existing mock self-test so we catch both static invariant drift and wrapper behavior regressions.\n\n\n=== MESSAGE 58 - Assistant ===\nHere's the full conversation from your active Codex CLI session (thread `019ef2f8…`, GLM-5.2 NVFP4/Modal task). I've saved it to `/tmp/codex_convo.txt` (99 events: 6 user turns + 93 Codex messages).\n\n## Summary of the conversation\n\n**The task:** Serve `zai-org/GLM-5.2` quantized to NVFP4 on a Modal 8×B200 node via vLLM (OpenAI-compatible), with a correctness gate vs BF16 and a throughput benchmark. Pasted from `/Users/root1/.codex/attachments/ec1b915e-…/pasted-text-1.txt`.\n\n**The pattern:** You (or a scheduler) re-pasted the same objective 6 times. Each time Codex did one round of work and posted a `final_answer`, then the next paste kicked off another round. So this is really 6 iterations on the same goal.\n\n### Round-by-round\n\n**Round 1** (05:33–05:37) — Initial scaffold\n- Created `submission/launch_server.sh` (vLLM NVFP4 flags, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend, pinned checkpoint)\n- Created `serve_modal.py` (Modal `B200:8`, persistent HF/vLLM volumes, `/app/submission` copy, `prewarm_model_cache` helper)\n- Created `verify_serving.py`, `run_dev_bench.py`, `optimize.py`\n- Switched default checkpoint to `Mapika/GLM-5.2-NVFP4` @ `5f9c62a4…` (its HF card matches the required mixed-precision layout)\n- Static checks passed; flagged that real 8×B200 run + correctness/throughput gates still unverified\n\n**Round 2** (05:38–05:44) — Runtime/benchmark hardening\n- Added FlashInfer CUTLASS env var, configurable quant/MoE/load/attention knobs\n- Runtime vLLM flag detection with fallback to vLLM 0.23 defaults\n- Switched Modal image to `vllm[flashinfer]==0.23.0`\n- Replaced full-response timing with streaming TTFT/latency/throughput + concurrent workload\n- `optimize.py` now launches server once and reuses it\n- Added README\n\n**Round 3** (05:45–05:51) — Sweep + checkpoint audit\n- Added `sweep_dev_bench.py` (sweeps `MAX_NUM_BATCHED_TOKENS`, `GPU_MEMORY_UTILIZATION`, `MAX_NUM_SEQS`, `NUM_SCHEDULER_STEPS`, JSONL output)\n- Added `inspect_checkpoint.py` — audited Mapika checkpoint:\n - Has `config.json`, `hf_quant_config.json` (ModelOpt NVFP4, group size 16)\n - Router `mlp.gate` tensors have only `weight` + `e_score_correction_bias` (NOT quantized ✓)\n - Dense `gate_proj`/expert/shared tensors have scale tensors (quantized ✓)\n - **No `model.layers.78`** → launcher does not enable MTP\n- Hardened early-exit detection in `verify_serving.py` / `run_dev_bench.py`\n\n**Round 4** (05:52–05:54) — Two runtime bug fixes\n- `MODEL_REVISION` no longer forced onto verifier-supplied local `MODEL_PATH`\n- `@modal.web_server` now starts vLLM with `Popen`, polls the local port, fails fast if vLLM exits, then returns (matches Modal's documented subprocess pattern)\n- Verified `wait_ready` locally with `python3 -m http.server`\n\n**Round 5** (05:55–05:57) — Local end-to-end mock tests\n- Added `mock_openai_server.py` (tiny OpenAI-compatible server with streaming SSE)\n- Added `selftest_wrappers.py` — exercises `/v1/models`, non-stream chat verify, streaming benchmark with concurrency\n- Ran `optimize.py` and `sweep_dev_bench.py` against the mock launcher successfully\n\n**Round 6** (05:57–05:59, in progress)\n- Removed `MODEL_REVISION` from the Modal image env (was risking leakage onto future `MODEL_PATH` overrides)\n- Adding a small invariant validator (submission file exists/executable, launcher composes TP=8/FP8/chunking, local paths don't get default revision, Modal is `B200:8`)\n- About to run validator + mock self-test — **this is where the session is right now**\n\n### Honest status (per Codex's own repeated caveat)\nEverything that can be proven locally is proven. The actual Modal 8×B200 startup, full ~459 GB checkpoint load, hidden correctness gate, and throughput benchmark remain unverified because they require the target GPU environment.\n\nWant me to keep tailing the live rollout as Round 6 finishes, or extract anything specific (e.g. the full `launch_server.sh` or `serve_modal.py`)?\n\n=== MESSAGE 59 - User ===\nhas it done anything?\n\n=== MESSAGE 60 - Assistant ===\n\n\n=== MESSAGE 61 - Tool ===\nOutput from command in shell 9a8b53:\n-rw-r--r-- 1 root1 staff 1111726 Jun 22 23:01 /Users/root1/.codex/sessions/2026/06/22/rollout-2026-06-22T22-33-24-019ef2f8-1156-71e1-8739-3a250421f23e.jsonl\n---\n PID STAT ELAPSED COMMAND\n18906 S+ 27:53 /opt/homebrew/lib/node_modules/@openai/codex/node_modules/@openai/codex-darwin-arm64/vendor/aarch64-apple-darwin/bin/codex --search --yolo\n---last event ts---\n\"timestamp\":\"2026-06-23T06:01:14.077Z\"\n\n\nExit code: 0\n\n=== MESSAGE 62 - Assistant ===\n\n\n=== MESSAGE 63 - Tool ===\nOutput from command in shell 2576f6:\nEvents since last check: 10 (total now: 108)\n================================================================================\n\n--- [100] CODEX(commentary) @ 2026-06-23T05:59:05.225Z ---\nThe validator is added. I’m running it now along with the existing mock self-test so we catch both static invariant drift and wrapper behavior regressions.\n\n--- [101] CODEX(commentary) @ 2026-06-23T05:59:42.054Z ---\nThe validator and wrapper self-test passed. I’m doing one more pass through the production launcher dry-runs and final cleanup so the source tree is left in a verified state.\n\n--- [102] CODEX(commentary) @ 2026-06-23T05:59:54.126Z ---\nThe production launcher dry-run and mock `optimize.py` path both passed. I’m cleaning bytecode and taking the final inventory now.\n\n--- [103] CODEX(commentary) @ 2026-06-23T06:00:01.009Z ---\nThere is no git repository in this workspace, so no git status is available. Only source files remain after cleanup.\n\n--- [104] CODEX(final_answer) @ 2026-06-23T06:00:09.901Z ---\nContinued hardening the submission.\n\nChanged:\n- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): removed global `MODEL_REVISION` from the Modal image env. The launcher now owns default revision pinning, so a future Modal `MODEL_PATH` override will not accidentally inherit the Mapika SHA.\n- [validate_submission.py](/Users/root1/Downloads/projects/glm-5.2-inference/validate_submission.py:1): added local invariant checks for launcher flags, default revision behavior, local `MODEL_PATH` behavior, and Modal `B200:8`/web server configuration.\n- [README.md](/Users/root1/Downloads/projects/glm-5.2-inference/README.md:1): documented the validator command.\n\nVerification run:\n- `python3 validate_submission.py`\n- `python3 selftest_wrappers.py`\n- `python3 optimize.py --script ...`\n- launcher dry-run with fake `vllm`\n- `bash -n submission/launch_server.sh submission/apply_patches.sh`\n- `python3 -m py_compile ...`\n\nStill not complete: the real Modal 8×B200 server startup, full 459 GB checkpoint load, correctness comparison, and throughput benchmark remain unverified because they require the target GPU environment.\n\n--- [105] USER @ 2026-06-23T06:00:10.114Z ---\npasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt. Read this file before continuing.\n\n--- [106] CODEX(commentary) @ 2026-06-23T06:00:29.739Z ---\nI’ll re-read the objective and inspect the current files, then I’ll look for remaining local gaps in the launch contract and Modal packaging before making changes.\n\n--- [107] CODEX(commentary) @ 2026-06-23T06:00:52.104Z ---\nThe current code passes local contract tests. I’m checking the Modal CLI/package surface next and then I’ll add a small preflight script that consolidates the local checks into one command, so the submission can be audited before any expensive GPU run.\n\n--- [108] CODEX(commentary) @ 2026-06-23T06:01:05.638Z ---\nThe installed Modal client and API signatures match the code paths we’re using. I’m adding a `preflight.py` runner to execute all cheap local gates in order, including syntax, launcher invariants, wrapper self-test, sweep dry-run, and checkpoint metadata audit.\n\n--- [109] CODEX(commentary) @ 2026-06-23T06:01:14.074Z ---\nThis preflight stays local and does not start Modal or download model shards. It only checks source and small Hugging Face metadata files.\n\n\nExit code: 0\n\n=== MESSAGE 64 - Assistant ===\nYes — it's been busy. Since I last showed you (event 99), 10 new events have landed:\n\n**Round 6 finished** (05:59–06:00) — `final_answer` posted:\n- `serve_modal.py`: removed global `MODEL_REVISION` from the Modal image env (so a future `MODEL_PATH` override won't inherit the Mapika SHA by accident)\n- New `validate_submission.py`: local invariant checks (launcher flags, default vs. local `MODEL_PATH` revision behavior, Modal `B200:8`/web_server config)\n- README updated with the validator command\n- All local checks passed: `validate_submission.py`, `selftest_wrappers.py`, mock `optimize.py`, launcher dry-run, `bash -n`, `py_compile`\n- Same honest caveat: real 8×B200 run + correctness/throughput gates still unverified\n\n**Round 7 started** (06:00–06:01, in progress):\n- You (or the scheduler) pasted the objective again at 06:00:10\n- Codex re-read the objective, confirmed current code passes local contract tests\n- Verified installed Modal client/API signatures match the code\n- **Currently adding `preflight.py`** — a single runner that consolidates all cheap local gates (syntax, launcher invariants, wrapper self-test, sweep dry-run, checkpoint metadata audit). Stays local, no Modal/GPU.\n\nSo: still no real GPU run (can't do that from your Mac), but it's grinding through incremental hardening of the submission scaffold on each re-paste. The session is alive at PID 18906, last event 06:01:14 UTC (≈23:01 local), 28 min elapsed.\n\n=== MESSAGE 65 - User ===\ni mean anything that actually does anything, it seems like its avoiding running anything hard\n\n=== MESSAGE 66 - Assistant ===\nYou're right, and that's a fair read. Let me check whether it even *can* run the real thing.\n\n=== MESSAGE 67 - Tool ===\nOutput from command in shell 85246f:\n/Users/root1/.modal.toml\n---\n[aidenhuang-api]\ntoken_id=***REDACTED***\ntoken_secret=***REDACTED***\n\n[aidenhuang-personal]\ntoken_id=***REDACTED***\ntoken_secret=***REDACTED***\nactive = true\n---modal cli---\n/Library/Frameworks/Python.framework/Versions/3.12/bin/modal\nmodal client version: 1.3.0.post1\n---workspace files---\ntotal 128\ndrwxr-xr-x 15 root1 staff 480 Jun 22 23:01 .\ndrwxr-xr-x 7 root1 staff 224 Jun 22 22:33 ..\n-rw-r--r-- 1 root1 staff 5190 Jun 22 22:51 inspect_checkpoint.py\n-rw-r--r-- 1 root1 staff 3619 Jun 22 22:56 mock_openai_server.py\n-rw-r--r-- 1 root1 staff 2168 Jun 22 22:46 optimize.py\n-rw-r--r-- 1 root1 staff 1543 Jun 22 23:01 preflight.py\n-rw-r--r-- 1 root1 staff 161 Jun 22 22:42 pyproject.toml\n-rw-r--r-- 1 root1 staff 2521 Jun 22 23:01 README.md\n-rw-r--r-- 1 root1 staff 8539 Jun 22 22:47 run_dev_bench.py\n-rw-r--r-- 1 root1 staff 1042 Jun 22 22:56 selftest_wrappers.py\n-rw-r--r-- 1 root1 staff 2645 Jun 22 22:58 serve_modal.py\ndrwxr-xr-x 4 root1 staff 128 Jun 22 22:34 submission\n-rw-r--r-- 1 root1 staff 6075 Jun 22 22:46 sweep_dev_bench.py\n-rw-r--r-- 1 root1 staff 3902 Jun 22 22:58 validate_submission.py\n-rw-r--r-- 1 root1 staff 3893 Jun 22 22:47 verify_serving.py\n\n\nExit code: 0\n\n=== MESSAGE 68 - Assistant ===\n\n\n=== MESSAGE 69 - Tool ===\nOutput from command in shell 13e613:\n---\n/Users/root1/Downloads/projects/glm-5.2-inference/README.md:28:modal run serve_modal.py::prewarm_model_cache\n---function names---\n49:def wait_ready(process: subprocess.Popen, port: int, timeout_s: int) -> None:\n62:@app.function(\n67:def prewarm_model_cache(\n81:@app.function(\n91:@modal.concurrent(max_inputs=8)\n92:@modal.web_server(port=VLLM_PORT, startup_timeout=45 * MINUTES)\n93:def serve():\n\n\nExit code: 0\n\n=== MESSAGE 70 - Assistant ===\n\n\n=== MESSAGE 71 - Tool ===\nOutput from command in shell 8b9ea7:\n28:modal run serve_modal.py::prewarm_model_cache\n34:modal serve serve_modal.py\n---\n# GLM-5.2 NVFP4 Modal Inference\n\nThis workspace serves `zai-org/GLM-5.2` from the pinned NVFP4 checkpoint\n`Mapika/GLM-5.2-NVFP4` on one Modal `B200:8` node through vLLM's\nOpenAI-compatible server.\n\nThe verifier entrypoint is:\n\n```bash\n/app/submission/launch_server.sh\n```\n\nThe local equivalent is:\n\n```bash\nPORT=30000 MODEL_PATH=Mapika/GLM-5.2-NVFP4 bash submission/launch_server.sh\n```\n\nIf `MODEL_PATH` is not set, the launcher pins the default checkpoint revision.\nIf `MODEL_PATH` is supplied by a verifier or points at a local cache path, no\nrevision is passed unless `MODEL_REVISION` is explicitly set.\n\n## Modal\n\nPrewarm the Hugging Face volume before serving:\n\n```bash\nmodal run serve_modal.py::prewarm_model_cache\n```\n\nServe on Modal:\n\n```bash\nmodal serve serve_modal.py\n```\n\n## Checks\n\nRun a startup and coherence check:\n\n```bash\nuv run --no-sync python verify_serving.py\n```\n\nRun the streaming TTFT/throughput benchmark:\n\n```bash\nuv run --no-sync python run_dev_bench.py\n```\n\nSweep chunked-prefill and scheduler settings:\n\n```bash\nuv run --no-sync python sweep_dev_bench.py\n```\n\nAudit the pinned checkpoint metadata:\n\n```bash\nuv run --no-sync python inspect_checkpoint.py\n\n\nExit code: 0\n\n=== MESSAGE 72 - Assistant ===\n\n\n=== MESSAGE 73 - Tool ===\nOutput from command in shell 48d98b:\n135:{\"timestamp\":\"2026-06-23T05:37:02.183Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent_message\",\"message\":\"One useful missing piece from the objective is cache prewarming. I’m adding a Modal function that downloads the pinned checkpoint into the Hugging Face volume ahead of serving, so cold starts do not pay the full model download.\",\"phase\":\"commentary\",\"memory_citation\":null}}\n136:{\"timestamp\":\"2026-06-23T05:37:02.183Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"message\",\"id\":\"msg_0ade37a884fa4091016a3a1b7d1e1481999df33a340f599c25\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\",\"text\":\"One useful missing piece from the objective is cache prewarming. I’m adding a Modal function that downloads the pinned checkpoint into the Hugging Face volume ahead of serving, so cold starts do not pay the full model download.\"}],\"phase\":\"commentary\",\"internal_chat_message_metadata_passthrough\":{\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\"}}}\n137:{\"timestamp\":\"2026-06-23T05:37:08.356Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"custom_tool_call\",\"id\":\"ctc_0ade37a884fa4091016a3a1b7e2b148199893093ceff2e43ff\",\"status\":\"completed\",\"call_id\":\"call_4Pk8Jb2DvZTwIJHAFeNWQblC\",\"name\":\"apply_patch\",\"input\":\"*** Begin Patch\\n*** Update File: serve_modal.py\\n@@\\n APP_NAME = \\\"glm-5.2-nvfp4\\\"\\n MINUTES = 60\\n+DEFAULT_MODEL_PATH = \\\"Mapika/GLM-5.2-NVFP4\\\"\\n+DEFAULT_MODEL_REVISION = \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\"\\n@@\\n- \\\"MODEL_PATH\\\": \\\"Mapika/GLM-5.2-NVFP4\\\",\\n- \\\"MODEL_REVISION\\\": \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\",\\n+ \\\"MODEL_PATH\\\": DEFAULT_MODEL_PATH,\\n+ \\\"MODEL_REVISION\\\": DEFAULT_MODEL_REVISION,\\n@@\\n hf_cache = modal.Volume.from_name(\\\"hf-cache\\\", create_if_missing=True)\\n vllm_cache = modal.Volume.from_name(\\\"vllm-cache\\\", create_if_missing=True)\\n \\n \\n+@app.function(\\n+ image=image,\\n+ timeout=60 * MINUTES,\\n+ volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n+)\\n+def prewarm_model_cache(\\n+ model_path: str = DEFAULT_MODEL_PATH,\\n+ revision: str = DEFAULT_MODEL_REVISION,\\n+):\\n+ from huggingface_hub import snapshot_download\\n+\\n+ snapshot_download(\\n+ repo_id=model_path,\\n+ revision=revision,\\n+ local_dir_use_symlinks=False,\\n+ )\\n+ hf_cache.commit()\\n+\\n+\\n @app.function(\\n*** End Patch\\n\",\"internal_chat_message_metadata_passthrough\":{\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\"}}}\n138:{\"timestamp\":\"2026-06-23T05:37:08.360Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"patch_apply_end\",\"call_id\":\"call_4Pk8Jb2DvZTwIJHAFeNWQblC\",\"turn_id\":\"579e6d2a-0474-4436-a651-7ccbafdb5742\",\"stdout\":\"Success. Updated the following files:\\nM serve_modal.py\\n\",\"stderr\":\"\",\"success\":true,\"changes\":{\"/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py\":{\"type\":\"update\",\"unified_diff\":\"@@ -9,2 +9,4 @@\\n MINUTES = 60\\n+DEFAULT_MODEL_PATH = \\\"Mapika/GLM-5.2-NVFP4\\\"\\n+DEFAULT_MODEL_REVISION = \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\"\\n \\n@@ -28,4 +30,4 @@\\n \\\"PYTHONUNBUFFERED\\\": \\\"1\\\",\\n- \\\"MODEL_PATH\\\": \\\"Mapika/GLM-5.2-NVFP4\\\",\\n- \\\"MODEL_REVISION\\\": \\\"5f9c62a4b08c36a174055facbd1e0c39b832059f\\\",\\n+ \\\"MODEL_PATH\\\": DEFAULT_MODEL_PATH,\\n+ \\\"MODEL_REVISION\\\": DEFAULT_MODEL_REVISION,\\n }\\n@@ -45,2 +47,21 @@\\n image=image,\\n+ timeout=60 * MINUTES,\\n+ volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n+)\\n+def prewarm_model_cache(\\n+ model_path: str = DEFAULT_MODEL_PATH,\\n+ revision: str = DEFAULT_MODEL_REVISION,\\n+):\\n+ from huggingface_hub import snapshot_download\\n+\\n+ snapshot_download(\\n+ repo_id=model_path,\\n+ revision=revision,\\n+ local_dir_use_symlinks=False,\\n+ )\\n+ hf_cache.commit()\\n+\\n+\\n+@app.function(\\n+ image=image,\\n gpu=\\\"B200:8\\\",\\n\",\"move_path\":null}},\"status\":\"completed\"}}\n149:{\"timestamp\":\"2026-06-23T05:37:18.502Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent...[2219 chars truncated]...,\\n 33\\t }\\n 34\\t )\\n 35\\t .add_local_dir(\\n 36\\t local_path=Path(__file__).parent / \\\"submission\\\",\\n 37\\t remote_path=\\\"/app/submission\\\",\\n 38\\t copy=True,\\n 39\\t )\\n 40\\t)\\n 41\\t\\n 42\\thf_cache = modal.Volume.from_name(\\\"hf-cache\\\", create_if_missing=True)\\n 43\\tvllm_cache = modal.Volume.from_name(\\\"vllm-cache\\\", create_if_missing=True)\\n 44\\t\\n 45\\t\\n 46\\t@app.function(\\n 47\\t image=image,\\n 48\\t timeout=60 * MINUTES,\\n 49\\t volumes={\\\"/root/.cache/huggingface\\\": hf_cache},\\n 50\\t)\\n 51\\tdef prewarm_model_cache(\\n 52\\t model_path: str = DEFAULT_MODEL_PATH,\\n 53\\t revision: str = DEFAULT_MODEL_REVISION,\\n 54\\t):\\n 55\\t from huggingface_hub import snapshot_download\\n 56\\t\\n 57\\t snapshot_download(\\n 58\\t repo_id=model_path,\\n 59\\t revision=revision,\\n 60\\t local_dir_use_symlinks=False,\\n 61\\t )\\n 62\\t hf_cache.commit()\\n 63\\t\\n 64\\t\\n 65\\t@app.function(\\n 66\\t image=image,\\n 67\\t gpu=\\\"B200:8\\\",\\n 68\\t scaledown_window=10 * MINUTES,\\n 69\\t timeout=60 * MINUTES,\\n 70\\t volumes={\\n 71\\t \\\"/root/.cache/huggingface\\\": hf_cache,\\n 72\\t \\\"/root/.cache/vllm\\\": vllm_cache,\\n 73\\t },\\n 74\\t)\\n 75\\t@modal.concurrent(max_inputs=8)\\n 76\\t@modal.web_server(port=8000, startup_timeout=45 * MINUTES)\\n 77\\tdef serve():\\n 78\\t env = os.environ.copy()\\n 79\\t env.setdefault(\\\"PORT\\\", \\\"8000\\\")\\n 80\\t process = subprocess.Popen([\\\"/app/submission/launch_server.sh\\\"], env=env)\\n 81\\t process.wait()\\n\"}}\n173:{\"timestamp\":\"2026-06-23T05:37:54.818Z\",\"type\":\"event_msg\",\"payload\":{\"type\":\"agent_message\",\"message\":\"Implemented the GLM-5.2 NVFP4 serving scaffold in the empty workspace.\\n\\nChanged:\\n- [submission/launch_server.sh](/Users/root1/Downloads/projects/glm-5.2-inference/submission/launch_server.sh:1): verifier entrypoint with vLLM NVFP4 flags, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend, configurable scheduler knobs, and a pinned checkpoint revision.\\n- [serve_modal.py](/Users/root1/Downloads/projects/glm-5.2-inference/serve_modal.py:1): Modal app using `B200:8`, persistent HF/vLLM volumes, `/app/submission` copy, web server wrapper, and a `prewarm_model_cache` helper.\\n- [verify_serving.py](/Users/root1/Downloads/projects/glm-5.2-inference/verify_serving.py:1), [run_dev_bench.py](/Users/root1/Downloads/projects/glm-5.2-inference/run_dev_bench.py:1), [optimize.py](/Users/root1/Downloads/projects/glm-5.2-inference/optimize.py:1): lightweight local sanity and benchmark wrappers.\\n\\nI switched the default checkpoint to `Mapika/GLM-5.2-NVFP4` and pinned revision `5f9c62a4b08c36a174055facbd1e0c39b832059f`; its Hugging Face card explicitly matches the required mixed precision layout, and the commit page confirms the revision. Sources: https://huggingface.co/Mapika/GLM-5.2-NVFP4 and https://huggingface.co/Mapika/GLM-5.2-NVFP4/commit/5f9c62a4b08c36a174055facbd1e0c39b832059f\\n\\nVerification run locally:\\n- `bash -n submission/launch_server.sh submission/apply_patches.sh`\\n- `python3 -m py_compile serve_modal.py verify_serving.py run_dev_bench.py optimize.py`\\n- Imported `serve_modal.py` successfully with installed Modal `1.3.0.post1`\\n\\nI did not mark the broader goal complete because the actual correctness/throughput gate requires the target Modal 8×B200 environment and the full checkpoint load.\",\"phase\":\"final_answer\",\"memory_citation\":null}}\n174:{\"timestamp\":\"2026-06-23T05:37:54.825Z\",\"type\":\"response_item\",\"payload\":{\"type\":\"message\",\"id\":\"msg_0ade37a884fa4091016a3a1ba96b208199b04e3d85adacf73b\",\"role\":\"assistant\",\"content\":[{\"type\":\"output_text\"… (1863 chars truncated)\n… (17 lines truncated)\n\nExit code: 0\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/3392966e/content.txt\n\n\n=== MESSAGE 74 - Assistant ===\n\n\n=== MESSAGE 75 - Tool ===\nOutput from command in shell 58b9e0:\nShell commands touching modal/vllm/hf: 1\n(290, '2026-06-23T05:42:25.415Z', 'exec_command', '{\"cmd\":\"python3 - <<\\'PY\\'\\\\ntry:\\\\n import huggingface_hub, inspect\\\\n print(\\'huggingface_hub\\', huggingface_hub.__version__)\\\\n print(inspect.signature(huggingface_hub.snapshot_download))\\\\nexcept Exception as exc:\\\\n print(type(exc).__name__ + \\': \\' + str(exc))\\\\nPY\",\"workdir\":\"/Users/root1/Down')\n---\nTool call counts: [('exec_command', 152), ('apply_patch', 30), ('update_plan', 23)]\n\n\nExit code: 0\n\n=== MESSAGE 76 - Assistant ===\n\n\n=== MESSAGE 77 - Tool ===\nOutput from command in shell a78c8c:\nTotal exec_command calls: 152\n\n--- command categories ---\n 24 modal\n 21 find\n 17 py_compile\n 12 bash -n\n 11 vllm (real)\n 11 other: python3 - <<'PY'\n 7 other: git status --short 2>/dev/null || true\n 6 other: sed -n '1,360p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 6 inspect_checkpoint\n 6 mock/selftest\n 5 optimize.py\n 5 sweep_dev_bench\n 2 rm (cleanup)\n 2 validate_submission\n 1 other: sed -n '1,240p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 1 other: git status --short\n 1 ripgrep\n 1 other: sed -n '241,520p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de1\n 1 other: mkdir -p submission\n 1 other: chmod +x submission/launch_server.sh\n 1 other: chmod +x submission/apply_patches.sh\n 1 other: sed -n '1,220p' submission/launch_server.sh\n 1 other: nl -ba submission/launch_server.sh | sed -n '1,220p'\n 1 verify_serving\n 1 run_dev_bench\n 1 other: sed -n '1,260p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de155\n 1 other: sed -n '261,520p' /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de1\n 1 other: nl -ba submission/launch_server.sh | sed -n '1,180p'\n 1 other: nl -ba README.md | sed -n '1,180p'\n 1 other: sed -n '1,260p' README.md; sed -n '1,220p' pyproject.toml\n 1 other: python3 preflight.py\n\n\nExit code: 0","created_at":"2026-06-23T06:36:10.757713Z","finish_reason":null,"metrics":null},{"message_id":"e245c728-ed9e-47a5-b30e-8d8f99808fc7","node_id":145,"role":"user","content":"Now summarize the conversation above as per the format given. Remember, do NOT take any actions. Just provide the summary in tags.","created_at":"2026-06-23T06:36:10.757719Z","finish_reason":null,"metrics":null},{"message_id":"821e0ed3-07d7-41b0-8dc6-62a656dacbc6","node_id":147,"role":"assistant","content":"","tool_calls":[{"id":"chatcmpl-tool-99af7d5fcc701506","name":"exec","arguments":{"command":"ps aux | grep -E \"modal (run|serve|deploy)\" | grep -v grep; echo \"---codex---\"; ps -p 18906 -o pid,stat,etime 2>&1; echo \"---modal apps---\"; modal app list 2>&1 | head -20"}}],"thinking":"The user is asking what's happening now. They pasted a Codex transcript showing the session got interrupted, they told it to focus on Modal benchmark, it tried to run prewarm, and then it's been stuck waiting for ~29 minutes on `modal run serve_modal.py::prewarm_model_cache` with no output. They have <10% of their 5h limit left.\n\nLet me check the actual current state - is the prewarm still running? What's the live status?","created_at":"2026-06-23T06:36:17.333310Z","finish_reason":"tool_calls","metrics":{"ttft_ms":4770,"total_time_ms":6575,"input_tokens":67335,"output_tokens":172,"cache_read_tokens":14765,"cache_creation_tokens":null,"tpot_ms":10.494186046511627,"tokens_per_sec":95.29085872576178}},{"message_id":"afb268f5-c01e-4d5c-acee-7b87df4f1948","node_id":148,"role":"tool","content":"Output from command in shell ce7382:\nroot1 22057 0.2 0.1 412354640 32944 ?? S 10:45PM 0:03.00 /Library/Frameworks/Python.framework/Versions/3.12/Resources/Python.app/Contents/MacOS/Python /Library/Frameworks/Python.framework/Versions/3.12/bin/modal run --detach serve_modal.py::prewarm\nroot1 27233 0.0 0.1 411960464 31328 ?? Ss 11:09PM 0:03.25 /Library/Frameworks/Python.framework/Versions/3.12/Resources/Python.app/Contents/MacOS/Python /Library/Frameworks/Python.framework/Versions/3.12/bin/modal run serve_modal.py::prewarm_model_cache\nroot1 22056 0.0 0.0 435299600 1104 ?? Ss 10:45PM 0:00.01 bash -c cd /Users/root1/Downloads/projects/glm-5.2-inference-devin && modal run --detach serve_modal.py::prewarm 2>&1\n---codex---\n PID STAT ELAPSED\n18906 S+ 01:02:54\n---modal apps---\n Apps \n┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━\n┃ App ID ┃ Description ┃ State ┃ Tasks ┃ Created at ┃ \n┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━\n│ ap-Yj1Y5KpiJixIrgp3zdnEsD │ bbq-animate │ deployed │ 0 │ 2026-04-13 │ \n│ │ │ │ │ 19:35 PDT │ \n│ ap-f9HzPHVzZp6K8VdyV3F2TC │ glm-5.2-nv… │ ephemeral │ 1 │ 2026-06-22 │ \n│ │ │ (detached) │ │ 22:45 PDT │ \n│ ap-GWcvIsw8zpGYIKRylIpZmO │ glm-5.2-nv… │ ephemeral │ 1 │ 2026-06-22 │ \n│ │ │ │ │ 23:09 PDT │ \n│ ap-udhLdw3Zq25PcOljX5Usff │ glm-5.2-nv… │ stopped │ 0 │ 2026-06-22 │ \n│ │ │ │ │ 22:41 PDT │ \n└───────────────────────────┴─────────────┴─────────────┴───────┴─────────────┴─\n\n\nExit code: 0","created_at":"2026-06-23T06:36:18.073097Z","finish_reason":null,"metrics":null},{"message_id":"d1bc54c6-4533-480b-a4ea-2b0c3eeb90e3","node_id":149,"role":"assistant","content":"\n## Overview\nThe ongoing conversation focuses on developing and hardening a submission for serving the `zai-org/GLM-5.2` model, quantized to NVFP4, on a Modal 8x B200 node using vLLM. The overall goal is to create an OpenAI-compatible serving solution with correctness and throughput benchmarking, with the ultimate deployment intended for a GPU environment not locally available to the agent.\n\n## Key Details & Breadcrumbs\n- **Objective Repetition:** The user repeatedly pasted the same objective (a text file: `pasted text file: /Users/root1/.codex/attachments/ec1b915e-4fe2-4514-a91a-6a8de15562a6/pasted-text-1.txt`) to prompt the agent for continued work. This led to a series of incremental updates and validations in distinct \"rounds\" of development (Message 58, 64).\n- **Model and Hardware:** The target model is `zai-org/GLM-5.2` (753B-param MoE), quantized to NVFP4 (E2M1 4-bit weights + FP8 E4M3 block scales) via NVIDIA Model Optimizer. The serving environment is a Modal 8x B200 node (1536 GB HBM total), with TP=8 (Message 29).\n- **Key Constraints:** Only MoE expert FFNs are quantized; Attention, router (`mlp.gate`), and `lm_head` are BF16. KV cache is FP8. MTP layer at index 78 is a correctness-sensitive detail. Chunked prefill is used for single-node deployment (Message 29).\n- **Checkpoint Selection:** The agent initially selected `Mapika/GLM-5.2-NVFP4` due to its `hf_quant_config.json` explicitly detailing NVFP4 quantization for expert FFNs and BF16 for other parts, aligning with correctness constraints (Message 38). Initial concerns about router quantization were resolved by inspecting `safetensors` index names, confirming router `mlp.gate` tensors were not quantized (Message 62, event 62).\n- **Modal Configuration:** The Modal application uses `B200:8` GPUs, persistent Hugging Face and vLLM volumes, and a `prewarm_model_cache` function to download the checkpoint ahead of serving (Message 58, 73).\n- **Development Artifacts:** The agent created multiple files:\n - `submission/launch_server.sh`: Verifier entrypoint with vLLM flags (NVFP4, TP=8, FP8 KV cache, chunked prefill, FlashInfer MoE backend).\n - `serve_modal.py`: Modal app definition.\n - `verify_serving.py`, `run_dev_bench.py`, `optimize.py`: Local sanity and benchmark wrappers.\n - `sweep_dev_bench.py`: Script for reproducible sweeps over vLLM parameters.\n - `inspect_checkpoint.py`: Audits HF checkpoint metadata and tensor index.\n - `mock_openai_server.py`: Tiny OpenAI-compatible mock server for local testing.\n - `selftest_wrappers.py`: Exercises local mock server and wrappers.\n - `validate_submission.py`: Local invariant checks.\n - `preflight.py`: Consolidates all cheap local checks into one command. (Message 58, 64)\n- **Local Verification:** A significant portion of the work involved local checks, including syntax, dry-runs of scripts, mock server tests, and invariant validations, without requiring the actual GPU environment or full model load (Message 58, 64).\n- **Unverified Aspects:** The actual Modal 8x B200 run, full ~459 GB checkpoint load, hidden correctness gate, and throughput benchmark remain unverified due to the absence of the target GPU environment (Message 58, 64).\n\n## Current State\nThe agent has just completed \"Round 6\" of development and is currently in \"Round 7.\"\n- **Immediate Task:** The agent is adding `preflight.py`, a script to consolidate all cheap local gates (syntax, launcher invariants, wrapper self-test, sweep dry-run, and checkpoint metadata audit). (Message 64)\n- **Pending Actions:** The agent is preparing to run the newly added validator (`preflight.py`) along with existing mock self-tests to catch static invariant drift and wrapper behavior regressions. (Message 63, event 109)\n- **Blockers/Questions:** The primary blocker remains the absence of the target Modal 8x B200 GPU environment, preventing full verification of the model loading, correctness, and throughput. The agent is aware of this limitation and has consistently stated it in its `final_answer` messages. The user's most recent interaction questions the agent's avoidance of \"running anything hard,\" referring to the lack of actual GPU execution (Message 65).\n","tool_calls":[],"created_at":"2026-06-23T06:36:18.847689Z","finish_reason":"stop","metrics":{"ttft_ms":2214,"total_time_ms":8089,"input_tokens":938,"output_tokens":1110,"cache_read_tokens":27636,"cache_creation_tokens":null,"tpot_ms":5.292792792792793,"tokens_per_sec":188.93617021276594}},{"message_id":"557b7aae-0f99-4fd9-a8de-4b0cbdb07ac3","node_id":151,"role":"assistant","content":"Here's what's actually happening right now:\n\n**Codex (PID 18906)** — still alive, 1h02m elapsed, but you have <10% of your 5h limit left. It's been stuck in a `Waited for background terminal` loop for ~29 minutes waiting on `modal run serve_modal.py::prewarm_model_cache` (PID 27233, started 11:09PM) which has produced **zero output** the entire time.\n\n**Three Modal apps are alive:**\n| App ID | State | Tasks | Created |\n|---|---|---|---|\n| `ap-f9HzPHVzZp6K8VdyV3F2TC` | ephemeral (detached) | 1 | 22:45 PDT |\n| `ap-GWcvIsw8zpGYIKRylIpZmO` | ephemeral | 1 | 23:09 PDT |\n| `ap-udhLdw3Zq25PcOljX5Usff` | stopped | 0 | 22:41 PDT |\n\n**There's also a second workspace running:** `modal run --detach serve_modal.py::prewarm` from `/Users/root1/Downloads/projects/glm-5.2-inference-devin` (PID 22057, started 10:45PM) — that's a separate effort, not from this Codex session.\n\n## The problem\n\nCodex is burning your remaining time budget doing nothing. It launched `prewarm_model_cache` (which downloads ~459 GB into a Modal volume), got no logs back, and has been in a passive wait loop for 29 minutes emitting variations of \"still waiting, no output yet.\" It never:\n- Checked if the job actually started\n- Added progress logging to the prewarm function\n- Considered that the prewarm might be redundant (the other `glm-5.2-inference-devin` workspace is already prewarming with `--detach`)\n- Set a timeout\n\nThe prewarm is probably genuinely running (downloading 459 GB takes a while), but Codex has no visibility into progress and is just burning context window and time budget waiting.\n\n## What I'd do\n\nYou have ~30 min of Codex budget left. Options:\n\n1. **Interrupt Codex** (esc), tell it to stop waiting on prewarm and just launch `run_modal_benchmark` directly — the prewarm from the other workspace may already be populating the same `hf-cache` volume, so the benchmark might not need to wait\n2. **Kill the Codex prewarm** (PID 27233) to stop the duplicate, keep the `--detach` one from the other workspace, and run the benchmark from here manually\n3. **Let it ride** — prewarm might finish soon and Codex will proceed to benchmark\n\nWant me to check the Modal app logs for `ap-GWcvIsw8zpGYIKRylIpZmO` (Codex's prewarm) to see if it's actually making progress or stuck?","tool_calls":[],"thinking":"So there are actually multiple things going on:\n1. Codex (PID 18906) is still running, 1h02m elapsed\n2. There's a `modal run --detach serve_modal.py::prewarm` from a different directory (`glm-5.2-inference-devin`) - PID 22057, started 10:45PM\n3. There's a `modal run serve_modal.py::prewarm_model_cache` - PID 27233, started 11:09PM - this is the one Codex launched\n4. There are 3 glm-5.2-nv Modal apps: one detached from 22:45 (the one from the other devin workspace), one ephemeral from 23:09 (Codex's prewarm), and one stopped from 22:41\n\nThe Codex prewarm has been running for ~25 minutes with no output. It's downloading a 459GB model. Let me check if there's any actual progress or if it's stuck.\n\nAlso notably, there's ANOTHER workspace `glm-5.2-inference-devin` that's also running a prewarm with `--detach` at 22:45. That's a separate effort.\n\nThe user has <10% of their 5h limit left on Codex. The prewarm is silent. Let me check the modal app logs for the active one.","created_at":"2026-06-23T06:36:28.769598Z","finish_reason":"stop","metrics":{"ttft_ms":1162,"total_time_ms":10695,"input_tokens":906,"output_tokens":936,"cache_read_tokens":82099,"cache_creation_tokens":null,"tpot_ms":10.18482905982906,"tokens_per_sec":98.18525123256059}}]}