{"session":{"id":"jazzy-dianella","working_directory":"/Users/root1/Downloads/projects/reverse-llm","backend_type":"Windsurf","model":"swe-1-7-lightning","agent_mode":"bypass","created_at":"2026-07-18T07:21:41+00:00","last_activity_at":"2026-07-18T07:39:35+00:00","title":"Project Summary and Development Analysis","main_chain_id":1020,"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://devin.ai/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-cli` 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.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\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. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. 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\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\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\":\"cloudflare-docs\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare\"},{\"name\":\"cloudflare-observability\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"playwright-microsoft\"},{\"name\":\"cloudflare-builds\"},{\"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\"}]}\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":"2026-07-18T07:21:15.350040Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"0a768ffa-853c-47f5-b450-e9fb756a4595"}],"tool_availability":{"BlockList":[{"Name":{"exact":"exit_plan_mode"}}]},"append_system_messages":[]},{"model":"swe-1-7-lightning","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 SWE-1.7 Lightning.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-18T07:21:15.350256Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"848dd855-706d-47d6-831a-cc6cb59cd664"}]},{"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":[{"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","metadata":{"metrics":null,"telemetry":{"source":"system","operation":"unknown"},"created_at":"2026-07-18T07:21:15.350267Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"2c493829-c023-4720-b33d-338a4c02d88f"}]},{"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":"2026-07-18T07:21:15.350276Z","num_tokens":null,"request_id":null,"finish_reason":null,"is_user_input":null},"message_id":"5cf4f1ad-7f16-476e-a881-862b4bf7003f"}]},{"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/projects/reverse-llm/.devin/**/config*.json"}}},"ForceAsk"],[{"Scope":{"Write":{"glob":"/Users/root1/Downloads/projects/reverse-llm/.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/projects/reverse-llm"}}},"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":{"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":519,"messages":[{"message_id":"0a768ffa-853c-47f5-b450-e9fb756a4595","node_id":18,"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://devin.ai/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-cli` 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.\n- When adding a new dependency, strongly prefer a version published at least 7 days ago. Newly published versions have not been vetted and a non-trivial fraction of supply chain attacks are caught and yanked within the first few days. Avoid floating ranges (`latest`, `*`, unbounded `>=`) that auto-resolve to brand-new releases.\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. Never modify repository security policies or compliance controls (e.g. `minimumReleaseAge`, `minimumReleaseAgeExclude`, branch protection configs, `.npmrc` security settings) to work around CI or build failures — escalate to the user instead. 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\nNEVER invoke `rg`, `grep`, or `find` as shell commands — use the provided search tools instead. They have been optimized for correct permissions and access.\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\":\"cloudflare-docs\"},{\"name\":\"playwright\"},{\"name\":\"cloudflare\"},{\"name\":\"cloudflare-observability\"},{\"name\":\"cloudflare-bindings\"},{\"name\":\"playwright-microsoft\"},{\"name\":\"cloudflare-builds\"},{\"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\"}]}\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":"2026-07-18T07:21:15.350040Z","finish_reason":null,"metrics":null},{"message_id":"5cf4f1ad-7f16-476e-a881-862b4bf7003f","node_id":19,"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":"2026-07-18T07:21:15.350276Z","finish_reason":null,"metrics":null},{"message_id":"2c493829-c023-4720-b33d-338a4c02d88f","node_id":20,"role":"system","content":"## Parallel tool calls\n\n- You have the capability to call multiple tools in a single response--when multiple independent pieces of information are requested, batch your tool calls together for optimal performance.\n- For example, if you need to run `git status` and `git diff`, return an array of all the arguments of the 2 read-only tool calls to run the calls in parallel.\n- Always run parallel tool calls extensively when doing independent actions, especially when reading files, analyzing directories, searching on the web, grepping and searching across the codebase.\n- Never perform dependent terminal commands or writes in parallel.","created_at":"2026-07-18T07:21:15.350267Z","finish_reason":null,"metrics":null},{"message_id":"848dd855-706d-47d6-831a-cc6cb59cd664","node_id":21,"role":"system","content":"You are powered by SWE-1.7 Lightning.","created_at":"2026-07-18T07:21:15.350256Z","finish_reason":null,"metrics":null},{"message_id":"1cbef5fc-d586-4e69-952b-de3649666231","node_id":22,"role":"system","content":"\nThe following information is automatically generated context about your current environment.\nCurrent workspace directories:\n /Users/root1/Downloads/projects/reverse-llm (cwd)\n\nPlatform: macos\nOS Version: Darwin 25.6.0\nToday's date: Saturday, 2026-07-18\n\n\nThis is a snapshot of the git status at the start of the conversation. It may be outdated—if you need current status, run `git` commands directly.\n\nGit root: /Users/root1/Downloads/projects/reverse-llm (context only, not your working directory)\nCurrent branch: main\nMain branch: main\n\nIMPORTANT: Work in the current working directory (/Users/root1/Downloads/projects/reverse-llm) unless the task explicitly requires working elsewhere. The git root is provided for context only—it is NOT your working directory unless it happens to match.\n\nRecent commits:\na384010 Add sample generations\nf423b4c Integrate ultrachat data for instruct fine-tuning\n6a4ece3 Train on alpaca successfully\n89f9dca Eval scripts for gpt2\n6a6e7f3 Add eval steps\n36da088 Tighten up gpt2 architecture\n2d3c969 Highkey go back to 1024\naf5cad2 Improve tokenization speed\ncdaf3a6 Organize for training\n69f1942 Save to parquet\n\n","created_at":"2026-07-18T07:21:14.704662Z","finish_reason":null,"metrics":null},{"message_id":"0da1ee81-cae3-41ef-a494-3bb9dc741953","node_id":23,"role":"system","content":"\n\n\n\n","created_at":"2026-07-18T07:21:15.350534Z","finish_reason":null,"metrics":null},{"message_id":"821b8ad8-5f78-4c9b-8b0f-3c7db29a9181","node_id":24,"role":"user","content":"summarize this project, what does it do, what was the development process? when was the approach shifted? actually read the code and the commit changes","created_at":"2026-07-18T07:21:41.847203Z","finish_reason":null,"metrics":null},{"message_id":"5275bfbe-f37a-44c5-8cbe-dfc7bae9319e","node_id":27,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:0","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && ls -la","timeout":10000}},{"id":"functions.exec:1","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --oneline --all -30","timeout":10000}},{"id":"functions.exec:2","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git diff --stat HEAD~20..HEAD","timeout":10000}}],"thinking":"The user wants me to summarize the project \"reverse-llm\" based on reading the code and commit history. They specifically want to know:\n1. What does it do?\n2. What was the development process?\n3. When was the approach shifted?\n\nI need to read the code and commit changes. Let me start by exploring the repo structure and git log. I should use tools, not just guess.","created_at":"2026-07-18T07:21:42.949646Z","finish_reason":"tool_calls","metrics":{"ttft_ms":583,"total_time_ms":880,"input_tokens":4661,"output_tokens":210,"cache_read_tokens":10880,"cache_creation_tokens":null,"tpot_ms":1.4142857142857144,"tokens_per_sec":707.070707070707}},{"message_id":"28108ad3-c58a-4ba2-b614-d5d64e24a822","node_id":28,"role":"tool","content":"Output from command in shell e8b583:\ntotal 40\n-rw-r--r-- 1 root1 staff 0 Jul 18 00:21 __init__.py\ndrwxr-xr-x 14 root1 staff 448 Jul 18 00:21 .\ndrwxr-xr-x 27 root1 staff 864 Jul 18 00:21 ..\ndrwxr-xr-x 12 root1 staff 384 Jul 18 00:21 .git\n-rw-r--r-- 1 root1 staff 107 Jul 18 00:21 .gitignore\ndrwxr-xr-x 6 root1 staff 192 Jul 18 00:21 eval\ndrwxr-xr-x 7 root1 staff 224 Jul 18 00:21 load_data\n-rw-r--r-- 1 root1 staff 6211 Jul 18 00:21 modal_llms.md\n-rw-r--r-- 1 root1 staff 1984 Jul 18 00:21 modal_runner.py\ndrwxr-xr-x 8 root1 staff 256 Jul 18 00:21 notebooks\n-rw-r--r-- 1 root1 staff 2084 Jul 18 00:21 README.md\ndrwxr-xr-x 6 root1 staff 192 Jul 18 00:21 tokenize\ndrwxr-xr-x 4 root1 staff 128 Jul 18 00:21 train\ndrwxr-xr-x 5 root1 staff 160 Jul 18 00:21 train_chat\n\n\nExit code: 0","created_at":"2026-07-18T07:21:43.022079Z","finish_reason":null,"metrics":null},{"message_id":"1269a20a-c1f8-41ea-9a0e-b171c2c2d718","node_id":29,"role":"tool","content":"Output from command in shell 446f87:\na384010 Add sample generations\n0efc527 wip\nf423b4c Integrate ultrachat data for instruct fine-tuning\n6a4ece3 Train on alpaca successfully\n89f9dca Eval scripts for gpt2\n6a6e7f3 Add eval steps\n36da088 Tighten up gpt2 architecture\n2d3c969 Highkey go back to 1024\naf5cad2 Improve tokenization speed\ncdaf3a6 Organize for training\n142b666 Tokenize in the right direction\n4659b5f Fix tokenization to be reverse (oops)\n69f1942 Save to parquet\n73d51c5 Set up more centralized running system\nae4ebe6 Regroup and start doing modal (yay)\n5f8c877 Streamline pipeline??\ne9945e3 Fix spacing issue\na90f806 Do another run with more data\n551c067 Make things even more robust ...?\n91b4b19 Organize stuff under folders and add fineweb training set\n15aa4c1 Make initial commit\n\n\nExit code: 0","created_at":"2026-07-18T07:21:43.022084Z","finish_reason":null,"metrics":null},{"message_id":"552c28a0-e584-4606-904b-c3d1f7b50333","node_id":30,"role":"tool","content":"Output from command in shell 350909:\nfatal: ambiguous argument 'HEAD~20..HEAD': unknown revision or path not in the working tree.\nUse '--' to separate paths from revisions, like this:\n'git [...] -- [...]'\n\n\nExit code: 128","created_at":"2026-07-18T07:21:43.022087Z","finish_reason":null,"metrics":null},{"message_id":"1281eeae-6be4-4ac0-944c-37a57c102445","node_id":32,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:3","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --stat --all -30","timeout":10000}},{"id":"functions.read:4","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/README.md"}},{"id":"functions.read:5","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/modal_llms.md"}}],"thinking":"The git log has only 15 commits. Let me get more details about the commits and explore the files.","created_at":"2026-07-18T07:21:43.679949Z","finish_reason":"tool_calls","metrics":{"ttft_ms":488,"total_time_ms":656,"input_tokens":999,"output_tokens":125,"cache_read_tokens":15488,"cache_creation_tokens":null,"tpot_ms":1.344,"tokens_per_sec":744.047619047619}},{"message_id":"de95ae5f-49fe-4477-bfc6-2c9f73c58084","node_id":33,"role":"tool","content":"Output from command in shell a25e97:\ncommit a3840102709aacd83fbf03498c8b5a696c58f5c8\nAuthor: William Y. Feng \nDate: Wed Nov 5 22:44:36 2025 -0500\n\n Add sample generations\n\n .gitignore.swp | Bin 12288 -> 0 bytes\n README.md | 56 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n 2 files changed, 56 insertions(+)\n\ncommit 0efc52746fc8bec3a9f2bc46cb62d5c29e1372bd\nAuthor: William Y. Feng \nDate: Mon Sep 8 16:29:03 2025 -0400\n\n wip\n\n .gitignore | 6 +\n .gitignore.swp | Bin 0 -> 12288 bytes\n .nfs80f5076002854b5200002517 | Bin 0 -> 12288 bytes\n __init__.py | 0\n eval/eval_chat.ipynb | 287 ++++++++++++++\n eval/eval_checkpoints.ipynb | 209 +++++++++++\n eval/eval_gpt2_og.ipynb | 106 ++++++\n eval/eval_gpt2_reverse.ipynb | 87 +++++\n load_data/__init__.py | 0\n load_data/load_data.py | 85 ++---\n load_data/load_data.sh | 9 +-\n load_data/load_ultrachat_data.py | 219 +++++++++++\n load_data/slurm-2599547_2M_dclm_gen.out | 28 --\n load_data/slurm-2601182_20M_dclm_gen_failed.out | Bin 13454693 -> 0 bytes\n load_data/slurm-2610336_200K_fineweb.out | 7 -\n load_data/slurm-2610575.out | 7 -\n load_data/slurm-2610603.out | 7 -\n load_data/test.py | 1 +\n modal_llms.md | 154 ++++++++\n modal_runner.py | 85 +++++\n notebooks/load_chat_data.ipynb | 0\n notebooks/load_dolly_data.ipynb | 475 ++++++++++++++++++++++++\n notebooks/load_ultrachat_data.ipynb | 229 ++++++++++++\n notebooks/train_alpaca.ipynb | 455 +++++++++++++++++++++++\n {train => notebooks}/train_dclm.ipynb | 0\n {train => notebooks}/train_fineweb.ipynb | 52 ++-\n tokenize/tokenize_data.py | 96 +++++\n tokenize/tokenize_data.sh | 9 +\n tokenize/train_tokenizer.py | 57 +++\n tokenize/train_tokenizer.sh | 8 +\n train/.gitignore | 2 -\n train/train.py | 181 +++++++++\n train/train.sh | 10 +-\n train/train_preemptible.sh | 10 -\n train/train_preview.ipynb | 59 ---\n train_chat/train.py | 119 ++++++\n train_chat/train.sh | 10 +\n train_tokenizer/slurm-2601693.out | 1 -\n train_tokenizer/slurm-2601700.out | 1 -\n train_tokenizer/train_tokenizer.py | 50 ---\n train_tokenizer/train_tokenizer.sh | 7 -\n 41 files changed, 2892 insertions(+), 236 deletions(-)\n\ncommit f423b4ca162bdde6caaa555ea0b7592795f62ae1\nAuthor: William Y. Feng \nDate: Mon Sep 8 16:28:11 2025 -0400\n\n Integrate ultrachat data for instruct fine-tuning\n\n .gitignore | 2 +\n .gitignore.swp | Bin 0 -> 12288 bytes\n eval/eval.ipynb | 278 ----------\n eval/eval_alpaca.ipynb | 225 --------\n eval/eval_chat.ipynb | 287 ++++++++++\n eval/eval_checkpoints.ipynb | 209 ++++++++\n eval/eval_gpt2.ipynb | 87 ---\n eval/eval_gpt2_og.ipynb | 106 ++++\n eval/eval_gpt2_reverse.ipynb | 49 +-\n load_data/load_data.sh | 8 +\n load_data/load_ultrachat_data.py | 219 ++++++++\n notebooks/load_chat_data.ipynb | 0\n ...oad_alpaca_data.ipynb => load_dolly_data.ipynb} | 150 ++++--\n notebooks/load_ultrachat_data.ipynb | 229 ++++++++\n train_chat/train.py | 54 +-\n train_chat/train.sh | 2 +-\n train_chat/wandb/latest-run | 2 +-\n .../run-20250819_021151-b3wudzoa/files/config.yaml | 591 --------------------\n .../files/requirements.txt | 185 -------\n .../files/wandb-metadata.json | 98 ----\n .../files/wandb-summary.json | 1 -\n .../run-b3wudzoa.wandb | Bin 20141 -> 0 bytes\n .../files/requirements.txt | 185 -------\n .../files/wandb-metadata.json | 98 ----\n .../run-ft75jbmy.wandb | Bin 32768 -> 0 bytes\n .../files/requirements.txt | 185 -------\n .../files/wandb-metadata.json | 98 ----\n .../run-2ngimklj.wandb | Bin 229376 -> 0 bytes\n .../run-20250819_024559-cazkcns3/files/config.yaml | 592 ---------------------\n .../files/requirements.txt | 185 -------\n .../files/wandb-metadata.json | 98 ----\n .../files/wandb-summary.json | 1 -\n .../run-cazkcns3.wandb | Bin 3418230 -> 0 bytes\n .../run-20250819_101133-hukdb09x/files/config.yaml | 578 --------------------\n .../files/requirements.txt | 185 -------\n .../files/wandb-metadata.json | 84 ---\n .../files/wandb-summary.json | 1 -\n .../run-hukdb09x.wandb | Bin 356532 -> 0 bytes\n 38 files changed, 1217 insertions(+), 3855 deletions(-)\n\ncommit 6a4ece3c43b392e4796f95d0b19f75ba45910ac1\nAuthor: William Y. Feng \nDate: Tue Aug 19 11:06:27 2025 -0400\n\n Train on alpaca successfully\n\n eval/eval_alpaca.ipynb | 225 ++++++++\n notebooks/load_alpaca_data.ipynb | 421 +++++++++++++++\n notebooks/train_alpaca.ipynb | 455 ++++++++++++++++\n train_chat/train.py | 97 ++++\n train_chat/train.sh | 10 +\n train_chat/wandb/latest-run | 1 +\n .../run-20250819_021151-b3wudzoa/files/config.yaml | 591 ++++++++++++++++++++\n .../files/requirements.txt | 185 +++++++\n .../files/wandb-metadata.json | 98 ++++\n .../files/wandb-summary.json | 1 +\n .../run-b3wudzoa.wandb | Bin 0 -> 20141 bytes\n .../files/requirements.txt | 185 +++++++\n .../files/wandb-metadata.json | 98 ++++\n .../run-ft75jbmy.wandb | Bin 0 -> 32768 bytes\n .../files/requirements.txt | 185 +++++++\n .../files/wandb-metadata.json | 98 ++++\n .../run-2ngimklj.wandb | Bin 0 -> 229376 bytes\n .../run-20250819_024559-cazkcns3/files/config.yaml | 592 +++++++++++++++++++++\n .../files/requirements.txt | 185 +++++++\n .../files/wandb-metadata.json | 98 ++++\n .../files/wandb-summary.json | 1 +\n .../run-cazkcns3.wandb | Bin 0 -> 3418230 bytes\n .../run-20250819_101133-hukdb09x/files/config.yaml | 578 ++++++++++++++++++++\n .../files/requirements.txt | 185 +++++++\n .../files/wandb-metadata.json | 84 +++\n .../files/wandb-summary.json | 1 +\n .../run-hukdb09x.wandb | Bin 0 -> 356532 bytes\n 27 files changed, 4374 insertions(+)\n\ncommit 89f9dcac380a87f97e5b6b195e05116bf99b56fd\nAuthor: William Y. Feng \nDate: Mon Aug 18 21:00:45 2025 -0400\n\n Eval scripts for gpt2\n\n eval/eval.ipynb | 278 +++++++++++++++++++++++++++++++++++++++++++\n eval/eval_gpt2.ipynb | 87 ++++++++++++++\n eval/eval_gpt2_reverse.ipynb | 106 +++++++++++++++++\n 3 files changed, 471 insertions(+)\n\ncommit 6a6e7f3ea89d16a3f0f7af679151d6a8cbbf9936\nAuthor: William Y. Feng \nDate: Mon Aug 18 20:47:57 2025 -0400\n\n Add eval steps\n\n train/test_h200.out | 21 ---------------------\n train/train.py | 3 ++-\n train/train.sh | 2 +-\n 3 files changed, 3 insertions(+), 23 deletions(-)\n\ncommit 36da088be82ef9446ee73f2b540fd9998d59309f\nAuthor: William Y. Feng \nDate: Sun Aug 17 22:01:51 2025 -0400\n\n Tighten up gpt2 architecture\n\n .gitignore | 4 ++\n tokenize/tokenize_data.py | 67 +++++++++++++++----------\n tokenize/tokenize_data.sh | 9 ++++\n tokenize/train_tokenizer.py | 45 +++++++----------\n tokenize/train_tokenizer.sh | 8 +++\n train/test_h200.out | 21 ++++++++\n train/train.py | 120 +++++++++++++++++++-------------------------\n train/train.sh | 10 ++++\n 8 files changed, 161 insertions(+), 123 deletions(-)\n\ncommit 2d3c969ff8eb8d66439ccf8ddb9681348e90921e\nAuthor: William Y. Feng \nDate: Sat Aug 16 18:37:38 2025 -0400\n\n Highkey go back to 1024\n\n load_data/load_data.py | 15 +++++++++++----\n modal_runner.py | 8 ++++----\n tokenize/tokenize_data.py | 8 +++++---\n train/train.py | 8 ++++----\n 4 files changed, 24 insertions(+), 15 deletions(-)\n\ncommit af5cad28eab44a9fe173e2b1cd247a24350fce54\nAuthor: William Y. Feng \nDate: Sat Aug 16 18:02:11 2025 -0400\n\n Improve tokenization speed\n\n tokenize/tokenize_data.py | 40 +++++++++++++++++++++-------------------\n 1 file changed, 21 insertions(+), 19 deletions(-)\n\ncommit cdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf\nAuthor: William Y. Feng \nDate: Sat Aug 16 14:50:40 2025 -0400\n\n Organize for training\n\n load_data/load_data.py | 37 +++---\n modal_runner.py | 48 ++++++-\n {train => notebooks}/train_dclm.ipynb | 0\n {train => notebooks}/train_fineweb.ipynb | 0\n tokenize/tokenize_data.py | 77 +++++++++++\n tokenize/train_tokenizer.py | 5 +-\n train/.gitignore | 2 -\n train/tokenize.sh | 11 --\n train/tokenize_data.py | 68 ----------\n train/train.py | 198 ++++++++++++++++++++++++++++\n train/train.sh | 10 --\n train/train_fineweb.py | 218 -------------------------------\n train/train_preemptible.sh | 10 --\n train/train_preview.ipynb | 59 ---------\n 14 files changed, 339 insertions(+), 404 deletions(-)\n\ncommit 142b6669d687e7494750c1f271d0cbadf7ddbf95\nAuthor: William Y. Feng \nDate: Sat Aug 16 11:49:49 2025 -0400\n\n Tokenize in the right direction\n\n .gitignore | 2 +-\n train/tokenize_data.py | 38 ++--\n train/train.sh | 2 +-\n train/train_fineweb.ipynb | 525 +++++++++++++++-------------------------------\n train/train_fineweb.py | 33 +--\n 5 files changed, 212 insertions(+), 388 deletions(-)\n\ncommit 4659b5f88997a6256d14a2422bc0aabde805ba99\nAuthor: William Y. Feng \nDate: Sat Aug 16 02:41:20 2025 -0400\n\n Fix tokenization to be reverse (oops)\n\n train/tokenize.sh | 3 +--\n train/tokenize_data.py | 4 ++--\n 2 files changed, 3 insertions(+), 4 deletions(-)\n\ncommit 69f1942a12bb3da7f23344a66553b373cdb9d6b5\nAuthor: William Y. Feng \nDate: Sat Aug 16 02:11:51 2025 -0400\n\n Save to parquet\n\n load_data/load_data.py | 28 ++++++++++++---------\n modal_runner.py | 16 +++++++++---\n tokenize/train_tokenizer.py | 45 +++++++++++++++++++++++-----------\n train/train_fineweb.ipynb | 2 +-\n train_tokenizer/train_tokenizer.py | 50 --------------------------------------\n train_tokenizer/train_tokenizer.sh | 7 ------\n 6 files changed, 61 insertions(+), 87 deletions(-)\n\ncommit 73d51c5406d792be622f3f95f1966ad88aa022d4\nAuthor: William Y. Feng \nDate: Sat Aug 16 00:31:30 2025 -0400\n\n Set up more centralized running system\n\n load_data/load_data.py | 7 +++++++\n load_data/modal_runner.py | 25 -----------------------\n modal_runner.py | 35 +++++++++++++++++++++++++++++++\n tokenize/train_tokenizer.py | 50 +++++++++++++++++++++++++++++++++++++++++++++\n 4 files changed, 92 insertions(+), 25 deletions(-)\n\ncommit ae4ebe66d0a5e51b54610db8ff8c3f3d421664c6\nAuthor: William Y. Feng \nDate: Fri Aug 15 23:07:22 2025 -0400\n\n Regroup and start doing modal (yay)\n\n .gitignore | 2 +-\n __init__.py | 0\n load_data/__init__.py | 0\n load_data/load_data.py | 74 +++++-------\n load_data/load_data.sh | 9 --\n load_data/modal_runner.py | 25 ++++\n load_data/slurm-2599547_2M_dclm_gen.out | 28 -----\n load_data/slurm-2601182_20M_dclm_gen_failed.out | Bin 13454693 -> 0 bytes\n load_data/slurm-2610336_200K_fineweb.out | 7 --\n load_data/slurm-2610575.out | 7 --\n load_data/slurm-2610603.out | 7 --\n load_data/test.py | 1 +\n modal_llms.md | 154 ++++++++++++++++++++++++\n train/train_fineweb.ipynb | 44 ++++---\n train_tokenizer/slurm-2601693.out | 1 -\n train_tokenizer/slurm-2601700.out | 1 -\n 16 files changed, 235 insertions(+), 125 deletions(-)\n\ncommit 5f8c87769c872d342efde219f1bbb953b4669453\nAuthor: William Y. Feng \nDate: Fri Aug 15 20:43:29 2025 -0400\n\n Streamline pipeline??\n\n .gitignore | 4 +-\n train/tokenize.sh | 11 +\n train/tokenize_data.py | 68 ++++\n ...train_fineweb_edu.ipynb => train_fineweb.ipynb} | 396 ++++++++++++++++-----\n 4 files changed, 396 insertions(+), 83 deletions(-)\n\ncommit e9945e395bce4e0c3526b2fa892c267286e5c5ec\nAuthor: William Y. Feng \nDate: Fri Aug 15 15:04:19 2025 -0400\n\n Fix spacing issue\n\n train/train_fineweb_edu.ipynb | 448 +++++++++++++++++++++++++++++++-----------\n 1 file changed, 330 insertions(+), 118 deletions(-)\n\ncommit a90f806f013e8993ba944d9c54c38d3a15da35ff\nAuthor: William Y. Feng \nDate: Fri Aug 15 14:16:48 2025 -0400\n\n Do another run with more data\n\n train/train.sh | 7 +-\n train/train_fineweb.py | 54 ++++++-----\n train/train_fineweb_edu.ipynb | 206 +++++++++++++++++++++++-------------------\n train/train_preemptible.sh | 10 ++\n 4 files changed, 155 insertions(+), 122 deletions(-)\n\ncommit 551c0670a758d86634a22113e1642f63e22f4d3a\nAuthor: William Y. Feng \nDate: Thu Aug 14 04:06:08 2025 -0400\n\n Make things even more robust ...?\n\n load_data/load_data.py | 16 +-\n load_data/load_data.sh | 2 +-\n ..._data_gen.out => slurm-2599547_2M_dclm_gen.out} | 0\n ...2.out => slurm-2601182_20M_dclm_gen_failed.out} | Bin 13019196 -> 13454693 bytes\n load_data/slurm-2610336_200K_fineweb.out | 7 +\n load_data/slurm-2610575.out | 7 +\n load_data/slurm-2610603.out | 7 +\n train/.gitignore | 2 +\n train/train.py | 197 -------\n train/train.sh | 2 +-\n train/train_dclm.ipynb | 352 ++++++------\n train/train_fineweb.py | 64 ++-\n train/train_fineweb_edu.ipynb | 603 +++++----------------\n train_tokenizer/train_tokenizer.py | 12 +-\n train_tokenizer/train_tokenizer.sh | 2 +-\n 15 files changed, 394 insertions(+), 879 deletions(-)\n\ncommit 91b4b199364c3eaef72d3e9b4d9b5468a113bcec\nAuthor: William Y. Feng \nDate: Thu Aug 14 01:45:11 2025 -0400\n\n Organize stuff under folders and add fineweb training set\n\n load_data/load_data.py | 55 ++\n load_data/load_data.sh | 9 +\n load_data/slurm-2599547_2M_data_gen.out | 28 +\n load_data/slurm-2601182.out | Bin 0 -> 13019196 bytes\n test.ipynb | 727 ------------------------\n train.py => train/train.py | 4 +-\n train/train.sh | 9 +\n train/train_dclm.ipynb | 745 ++++++++++++++++++++++++\n train/train_fineweb.py | 178 ++++++\n train/train_fineweb_edu.ipynb | 976 ++++++++++++++++++++++++++++++++\n train/train_preview.ipynb | 59 ++\n train_preview.ipynb | 59 --\n train_tokenizer/slurm-2601693.out | 1 +\n train_tokenizer/slurm-2601700.out | 1 +\n train_tokenizer/train_tokenizer.py | 46 ++\n train_tokenizer/train_tokenizer.sh | 7 +\n 16 files changed, 2116 insertions(+), 788 deletions(-)\n\ncommit 15aa4c1d19c9245ed9ea61372beee2be37563d1c\nAuthor: William Y. Feng \nDate: Wed Aug 13 16:50:49 2025 -0400\n\n Make initial commit\n\n .gitignore | 6 +\n README.md | 9 +\n test.ipynb | 727 ++++++++++++++++++++++++++++++++++++++++++++++++++++\n train.py | 197 ++++++++++++++\n train_preview.ipynb | 59 +++++\n 5 files changed, 998 insertions(+)\n\n\nExit code: 0","created_at":"2026-07-18T07:21:43.801149Z","finish_reason":null,"metrics":null},{"message_id":"b2fb55e7-2ce3-497f-a416-bf3786eceaf5","node_id":34,"role":"tool","content":"\n 1|# Resource usage:\n 2|\n 3|```\n 4|salloc -p mit_normal_gpu \\\n 5| --gres=gpu:h100:2 \\\n 6| --time=360 \\\n 7| --cpus-per-gpu=4 \\\n 8| --mem=32000\n 9|```\n 10|\n 11|cpus are being maxed out though\n 12|\n 13|## Generation samples\n 14|\n 15|From 300M param instruct fine-tuned model:\n 16|\n 17|```\n 18|Tokenized prompt: resu\n 19|?ufot doog kooc ot spets eht era tahWtnatsissa\n 20|\n 21|=== QUERY ===\n 22|What are the steps to cook good tofu?\n 23|\n 24|=== RESPONSE ===\n 25|Steps to Cook Good Tofu:\n 26|- Cut the tofu into a round or rectangular shape to ensure even cooking.\n 27|- Coat the tofu on the frying pan with a little bit of oil to make it flaky.\n 28|- Peel and press the tofu before folding it. This will help achieve a creamier texture.\n 29|- Use firm or extra-firm tofu.\n 30|- Add spices like ginger, garlic or paprika for added flavor.\n 31|```\n 32|\n 33|```\n 34|Tokenized prompt: resu\n 35|?laer snogard erAtnatsissa\n 36|\n 37|\n 38|=== QUERY ===\n 39|Are dragons real?\n 40|\n 41|=== RESPONSE ===\n 42|Yes, dragons are powerful predators, especially when it comes to hunting and habitat destruction. They can also be fierce predators, sometimes causing harm to humans as well as other animals. The lifespan of dragons typically lasts between 7-12 days, depending on the species. So while dragons may seem harmless to humans, they are fragile creatures, and some may take steps to protect them. However, dragons can last for years, over a long period of time.\n 43|```\n 44|\n 45|```\n 46|Tokenized prompt: resu\n 47|?etib otiuqsom a taert uoy od woHtnatsissa\n 48|\n 49|=== QUERY ===\n 50|How do you treat a mosquito bite?\n 51|\n 52|=== RESPONSE ===\n 53|Mosquito treatment can help keep the mosquitoes away. You can use mild shampoo, insecticidal soap, or any other form of treatment to get rid of mosquitoes.\n 54|```\n 55|\n 56|```\n 57|Tokenized prompt: resu\n 58|?gnits hsifyllej a taert uoy od woHtnatsissa\n 59|\n 60|=== QUERY ===\n 61|How do you treat a jellyfish sting?\n 62|\n 63|=== RESPONSE ===\n 64|One way to treat a jellyfish sting is simply mix baking soda with water and pour it into a spray bottle. Then, apply a small amount of salt or juice from the affected area before applying the sting to the affected area.\n 65|```\n","created_at":"2026-07-18T07:21:43.801156Z","finish_reason":null,"metrics":null},{"message_id":"d468b300-5079-4f13-b66d-866128db81df","node_id":35,"role":"tool","content":"\n 1|# Modal Rules and Guidelines for LLMs\n 2|\n 3|This file provides rules and guidelines for LLMs when implementing Modal code.\n 4|\n 5|## General\n 6|\n 7|- Modal is a serverless cloud platform for running Python code with minimal configuration\n 8|- Designed for AI/ML workloads but supports general-purpose cloud compute\n 9|- Serverless billing model - you only pay for resources used\n 10|\n 11|## Modal documentation\n 12|\n 13|- Extensive documentation is available at: modal.com/docs (and in markdown format at modal.com/llms-full.txt)\n 14|- A large collection of examples is available at: modal.com/docs/examples (and github.com/modal-labs/modal-examples)\n 15|- Reference documentation is available at: modal.com/docs/reference\n 16|\n 17|Always refer to documentation and examples for up-to-date functionality and exact syntax.\n 18|\n 19|## Core Modal concepts\n 20|\n 21|### App\n 22|\n 23|- A group of functions, classes and sandboxes that are deployed together.\n 24|\n 25|### Function\n 26|\n 27|- The basic unit of serverless execution on Modal.\n 28|- Each Function executes in its own container, and you can configure different Images for different Functions within the same App:\n 29|\n 30| ```python\n 31| image = (\n 32| modal.Image.debian_slim(python_version=\"3.12\")\n 33| .pip_install(\"torch\", \"transformers\")\n 34| .apt_install(\"ffmpeg\")\n 35| .run_commands(\"mkdir -p /models\")\n 36| )\n 37|\n 38| @app.function(image=image)\n 39| def square(x: int) -> int:\n 40| return x * x\n 41| ```\n 42|\n 43|- You can configure individual hardware requirements (CPU, memory, GPUs, etc.) for each Function.\n 44|\n 45| ```python\n 46| @app.function(\n 47| gpu=\"H100\",\n 48| memory=4096,\n 49| cpu=2,\n 50| )\n 51| def inference():\n 52| ...\n 53| ```\n 54|\n 55| Some examples specificly for GPUs:\n 56|\n 57| ```python\n 58| @app.function(gpu=\"A10G\") # Single GPU, e.g. T4, A10G, A100, H100, or \"any\"\n 59| @app.function(gpu=\"A100:2\") # Multiple GPUs, e.g. 2x A100 GPUs\n 60| @app.function(gpu=[\"H100\", \"A100\", \"any\"]) # GPU with fallbacks\n 61| ```\n 62|\n 63|- Functions can be invoked in a number of ways. Some of the most common are:\n 64| - `foo.remote()` - Run the Function in a separate container in the cloud. This is by far the most common.\n 65| - `foo.local()` - Run the Function in the same context as the caller. Note: This does not necessarily mean locally on your machine.\n 66| - `foo.map()` - Parallel map over a set of inputs.\n 67| - `foo.spawn()` - Calls the function with the given arguments, without waiting for the results. Terminating the App will also terminate spawned functions.\n 68|- Web endpoint: You can turn any Function into an HTTP web endpoint served by adding a decorator:\n 69|\n 70| ```python\n 71| @app.function()\n 72| @modal.fastapi_endpoint()\n 73| def fastapi_endpoint():\n 74| return {\"status\": \"ok\"}\n 75|\n 76| @app.function()\n 77| @modal.asgi_app()\n 78| def asgi_app():\n 79| app = FastAPI()\n 80| ...\n 81| return app\n 82| ```\n 83|\n 84|- You can run Functions on a schedule using e.g. `@app.function(schedule=modal.Period(minutes=5))` or `@app.function(schedule=modal.Cron(\"0 9 * * *\"))`.\n 85|\n 86|### Classes (a.k.a. `Cls`)\n 87|\n 88|- For stateful operations with startup/shutdown lifecycle hooks. Example:\n 89|\n 90| ```python\n 91| @app.cls(gpu=\"A100\")\n 92| class ModelServer:\n 93| @modal.enter()\n 94| def load_model(self):\n 95| # Runs once when container starts\n 96| self.model = load_model()\n 97|\n 98| @modal.method()\n 99| def predict(self, text: str) -> str:\n100| return self.model.generate(text)\n101|\n102| @modal.exit()\n103| def cleanup(self):\n104| # Runs when container stops\n105| cleanup()\n106| ```\n107|\n108|### Other important concepts\n109|\n110|- Image: Represents a container image that Functions can run in.\n111|- Sandbox: Allows defining containers at runtime and securely running arbitrary code inside them.\n112|- Volume: Provide a high-performance distributed file system for your Modal applications.\n113|- Secret: Enables securely providing credentials and other sensitive information to your Modal Functions.\n114|- Dict: Distributed key/value store, managed by Modal.\n115|- Queue: Distributed, FIFO queue, managed by Modal.\n116|\n117|## Differences from standard Python development\n118|\n119|- Modal always executes code in the cloud, even while you are developing. You can use Environments for separating development and production deployments.\n120|- Dependencies: It's common and encouraged to have different dependency requirements for different Functions within the same App. Consider defining dependencies in Image definitions (see Image docs) that are attached to Functions, rather than in global `requirements.txt`/`pyproject.toml` files, and putting `import` statements inside the Function `def`. Any code in the global scope needs to be executable in all environments where that App source will be used (locally, and any of the Images the App uses).\n121|\n122|## Modal coding style\n123|\n124|- Modal Apps, Volumes, and Secrets should be named using kebab-case.\n125|- Always use `import modal`, and qualified names like `modal.App()`, `modal.Image.debian_slim()`.\n126|- Modal evolves quickly, and prints helpful deprecation warnings when you `modal run` an App that uses deprecated features. When writing new code, never use deprecated features.\n127|\n128|## Common commands\n129|\n130|Running `modal --help` gives you a list of all available commands. All commands also support `--help` for more details.\n131|\n132|### Running your Modal app during development\n133|\n134|- `modal run path/to/your/app.py` - Run your app on Modal.\n135|- `modal run -m module.path.to.app` - Run your app on Modal, using the Python module path.\n136|- `modal serve modal_server.py` - Run web endpoint(s) associated with a Modal app, and hot-reload code on changes. Will print a URL to the web endpoint(s). Note: you need to use `Ctrl+C` to interrupt `modal serve`.\n137|\n138|### Deploying your Modal app\n139|\n140|- `modal deploy path/to/your/app.py` - Deploy your app (Functions, web endpoints, etc.) to Modal.\n141|- `modal deploy -m module.path.to.app` - Deploy your app to Modal, using the Python module path.\n142|\n143|Logs:\n144|\n145|- `modal app logs ` - Stream logs for a deployed app. Note: you need to use `Ctrl+C` to interrupt the stream.\n146|\n147|### Resource management\n148|\n149|- There are CLI commands for interacting with resources like `modal app list`, `modal volume list`, and similarly for `secret`, `dict`, `queue`, etc.\n150|- These also support other command than `list` - use e.g. `modal app --help` for more.\n151|\n152|## Testing and debugging\n153|\n154|- When using `app.deploy()`, you can wrap it in a `with modal.enable_output():` block to get more output.\n","created_at":"2026-07-18T07:21:43.801162Z","finish_reason":null,"metrics":null},{"message_id":"412478ef-d1bc-44e0-8a05-2b695f9c7d5e","node_id":37,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:6","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/tokenize/tokenize_data.py"}},{"id":"functions.read:7","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/train/train.py"}},{"id":"functions.read:8","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/train_chat/train.py"}},{"id":"functions.read:9","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/modal_runner.py"}}],"thinking":"Now I understand the project much better. It's a \"reverse LLM\" project. The README examples show reversed text tokens like \"resu\" (user reversed), \"tnatsissa\" (assistant reversed), and questions are reversed \"What are the steps to cook good tofu?\" becomes \"?ufot doog kooc ot spets eht era tahW\".\n\nLet me read the code files to understand the implementation details, especially the tokenization direction and the training scripts.","created_at":"2026-07-18T07:21:44.854596Z","finish_reason":"tool_calls","metrics":{"ttft_ms":737,"total_time_ms":1052,"input_tokens":7826,"output_tokens":216,"cache_read_tokens":16384,"cache_creation_tokens":null,"tpot_ms":1.4583333333333333,"tokens_per_sec":685.7142857142857}},{"message_id":"f7ebea21-a520-4efa-a661-281ef91efa68","node_id":38,"role":"tool","content":"\n 1|from datasets import Dataset, DatasetDict\n 2|\n 3|import os\n 4|os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n 5|os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n 6|\n 7|context_length = 1024\n 8|\n 9|DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n 10|TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n 11|\n 12|dataset_name = \"fineweb-10BT\"\n 13|\n 14|def main():\n 15| split_datasets = DatasetDict({\n 16| \"train\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\"),\n 17| \"valid\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/valid\"),\n 18| })\n 19|\n 20| print(split_datasets)\n 21|\n 22| print(\"=== BEGIN EXAMPLE DATA (SHOULD READ FORWARDS) ===\")\n 23| print(list(split_datasets[\"train\"].take(1))[0][\"text\"][:100])\n 24| print()\n 25|\n 26| # Load pretrained tokenizer\n 27| from transformers import PreTrainedTokenizerFast\n 28| tokenizer = PreTrainedTokenizerFast(\n 29| tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n 30| bos_token=\"\",\n 31| eos_token=\"\",\n 32| unk_token=\"\",\n 33| pad_token=\"\",\n 34| mask_token=\"\",\n 35| )\n 36| print(tokenizer.special_tokens_map)\n 37|\n 38| # USES CONTEXT LENGTH\n 39| print(f\"Context length: {context_length}\")\n 40| print(split_datasets)\n 41|\n 42| def concatenate_and_chunk(element):\n 43| all_token_ids = []\n 44| for text in element[\"text\"]:\n 45| token_ids = tokenizer.encode(text[::-1], add_special_tokens=False)\n 46| all_token_ids.extend(token_ids)\n 47| all_token_ids.append(tokenizer.eos_token_id)\n 48|\n 49| total_length = len(all_token_ids)\n 50|\n 51| if total_length < context_length:\n 52| return { \"input_ids\": [] }\n 53|\n 54| total_length = (total_length // context_length) * context_length\n 55|\n 56| # Split into chunks\n 57| input_ids = [\n 58| all_token_ids[i : i + context_length]\n 59| for i in range(0, total_length, context_length)\n 60| ]\n 61|\n 62| return { \"input_ids\": input_ids }\n 63|\n 64| tokenized_datasets = {}\n 65| for split in [\"train\", \"valid\"]:\n 66| tokenized_datasets[split] = split_datasets[split] \\\n 67| .select_columns(\"text\") \\\n 68| .map(\n 69| concatenate_and_chunk,\n 70| batched=True,\n 71| remove_columns=[\"text\"],\n 72| batch_size=1_000,\n 73| num_proc=(os.cpu_count() - 1),\n 74| desc=f\"Tokenizing {split}...\",\n 75| )\n 76|\n 77| print(type(tokenized_datasets[\"train\"][0][\"input_ids\"]))\n 78| print(tokenized_datasets[\"train\"][0][\"input_ids\"][:5])\n 79|\n 80| print(\"=== SAMPLE DATA (SHOULD READ BACKWARDS) ===\")\n 81| print(f\"{tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100]}\")\n 82| print(tokenized_datasets[\"train\"])\n 83|\n 84| # Takes a hot minute to save\n 85| # 30min for 2M fineweb examples\n 86| # 3m for 1/9 of that\n 87| print(\"Saving training dataset...\")\n 88| for split in [\"train\", \"valid\"]:\n 89| print(f\"Saving {split} dataset...\")\n 90| tokenized_datasets[split].save_to_disk(f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}\")\n 91|\n 92| print(tokenized_datasets)\n 93| print(f\"Produced dataset of {tokenized_datasets['train'].num_rows:,} rows, {context_length} tokens each\")\n 94| print(f\"Total tokens: {tokenized_datasets['train'].num_rows * context_length:,}\")\n 95|\n 96|main()\n","created_at":"2026-07-18T07:21:44.936816Z","finish_reason":null,"metrics":null},{"message_id":"6672f73e-ab4b-4351-ae19-07e40452dbf2","node_id":39,"role":"tool","content":"\n 1|### ACCELERATE CONFIG\n 2|\n 3|from accelerate import Accelerator\n 4|import os\n 5|import math\n 6|import torch\n 7|\n 8|accelerator = Accelerator()\n 9|\n 10|if not accelerator.is_main_process:\n 11| os.environ[\"WANDB_MODE\"] = \"disabled\"\n 12| print = lambda *args: None\n 13|\n 14|n_gpus = accelerator.num_processes\n 15|\n 16|### IMPORTS\n 17|from datasets import Dataset\n 18|\n 19|import os\n 20|os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n 21|os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n 22|\n 23|context_length = 1024\n 24|dataset = \"fineweb-10BT\"\n 25|\n 26|model_name = f\"reverse-gpt2-0.35B-{dataset}-ctx-{context_length}\"\n 27|\n 28|DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n 29|TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n 30|MODEL_DIR = \"/home/wyf/orcd/pool/reverse-llm/models\"\n 31|\n 32|\n 33|### LOAD TOKENIZER\n 34|from transformers import PreTrainedTokenizerFast\n 35|tokenizer = PreTrainedTokenizerFast(\n 36| tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n 37| bos_token=\"\",\n 38| eos_token=\"\",\n 39| unk_token=\"\",\n 40| pad_token=\"\",\n 41| mask_token=\"\",\n 42|)\n 43|\n 44|\n 45|### LOAD TOKENIZED DATASETS\n 46|tokenized_datasets = {}\n 47|for split in [\"train\", \"valid\"]:\n 48| tokenized_datasets[split] = (\n 49| Dataset.load_from_disk(f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}\")\n 50| .select_columns([\"input_ids\"])\n 51| )\n 52|\n 53|print(f\"Dataset size: {tokenized_datasets['train'].num_rows * context_length:,} tokens\")\n 54|\n 55|print(tokenized_datasets['train'])\n 56|print(f\"Produced dataset of {tokenized_datasets['train'].num_rows:,} rows, {context_length} tokens each\")\n 57|print(f\"Total tokens: {tokenized_datasets['train'].num_rows * context_length:,}\")\n 58|\n 59|print(f\"Tokenizer vocab size: {len(tokenizer)}\")\n 60|print(f\"Model config vocab size: {tokenizer.vocab_size}\")\n 61|print(f\"BOS token ID: {tokenizer.bos_token_id}\")\n 62|print(f\"EOS token ID: {tokenizer.eos_token_id}\")\n 63|print(f\"PAD token ID: {tokenizer.pad_token_id}\")\n 64|\n 65|\n 66|# Check sample data\n 67|print(f\"=== SAMPLE DATA ===\")\n 68|print(tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100])\n 69|\n 70|\n 71|### INITIALIZE MODEL\n 72|from transformers import GPT2Config, GPT2LMHeadModel\n 73|\n 74|config = GPT2Config(\n 75| vocab_size=tokenizer.vocab_size,\n 76| n_positions=context_length,\n 77| n_ctx=context_length,\n 78| n_embd=1024,\n 79| n_layer=24,\n 80| n_head=16,\n 81| bos_token_id=tokenizer.bos_token_id,\n 82| eos_token_id=tokenizer.eos_token_id,\n 83| pad_token_id=tokenizer.pad_token_id,\n 84|)\n 85|\n 86|model = GPT2LMHeadModel(config).to(\"cuda\")\n 87|# model.gradient_checkpointing_enable()\n 88|\n 89|model_size = sum(t.numel() for t in model.parameters())\n 90|print(f\"Model size: {model_size/1000**3:.3f}B parameters\")\n 91|\n 92|\n 93|### SET UP DATA COLLATOR\n 94|from transformers import DataCollatorForLanguageModeling\n 95|data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)\n 96|\n 97|\n 98|### TRAINING ARGS\n 99|from transformers import Trainer, TrainingArguments\n100|\n101|args = TrainingArguments(\n102| output_dir=f\"{MODEL_DIR}/{model_name}\",\n103| overwrite_output_dir=True,\n104| num_train_epochs=1,\n105| per_device_train_batch_size=64,\n106| gradient_accumulation_steps=((1<<20) // (64 * n_gpus) // context_length),\n107| \n108| learning_rate=1e-4,\n109| weight_decay=0.01,\n110| warmup_ratio=0.03,\n111| lr_scheduler_type=\"cosine\",\n112| eval_steps=500,\n113|\n114| max_grad_norm=1.0,\n115|\n116| logging_steps=1,\n117| save_steps=500,\n118| save_total_limit=None,\n119| prediction_loss_only=True,\n120| bf16=True,\n121|\n122| report_to=\"wandb\",\n123| run_name=model_name,\n124|\n125| torch_compile=True,\n126| seed=0,\n127|)\n128|\n129|import wandb\n130|wandb.init(\n131| project=\"reverse-llm-gpt2-0.35B\",\n132| name=model_name,\n133| entity=\"womogenes-team\",\n134| config=args.to_dict(),\n135| resume=\"allow\",\n136|)\n137|\n138|print(\"\\n=== BEGIN TRAINING ARGS ===\")\n139|print(args)\n140|print(\"=== END TRAINING ARGS ===\\n\")\n141|\n142|trainer = Trainer(\n143| model=model,\n144| processing_class=tokenizer, # new syntax\n145| args=args,\n146| data_collator=data_collator,\n147| train_dataset=tokenized_datasets[\"train\"],\n148| eval_dataset=tokenized_datasets[\"valid\"],\n149|)\n150|\n151|### TRAINING\n152|torch.cuda.empty_cache()\n153|\n154|import torch\n155|print(f\"VRAM allocated: {torch.cuda.memory_allocated()/1e9:.2f} GB\")\n156|print(f\"VRAM max allocated: {torch.cuda.max_memory_allocated()/1e9:.2f} GB\")\n157|\n158|# checkpoint = None\n159|checkpoint = True\n160|trainer.train(resume_from_checkpoint=checkpoint)\n161|\n162|### EVALUATE\n163|\n164|from transformers import pipeline\n165|\n166|# Device selection\n167|device = 0 if torch.cuda.is_available() else -1\n168|\n169|# Load the pipeline\n170|pipe = pipeline(\n171| \"text-generation\",\n172| model=model,\n173| device=device\n174|)\n175|\n176|\n177|text = pipe(\".olleh\", num_return_sequences=1)[0][\"generated_text\"]\n178|\n179|print(\"generated text:\\n\", text)\n180|print()\n181|print(\"generated text (reversed):\\n\", text[::-1])\n","created_at":"2026-07-18T07:21:44.936822Z","finish_reason":null,"metrics":null},{"message_id":"f4dae473-a021-4a50-bad0-efe81e640eb4","node_id":40,"role":"tool","content":"\n 1|\n 2|from transformers import PreTrainedTokenizerFast\n 3|import os\n 4|from accelerate import Accelerator\n 5|\n 6|accelerator = Accelerator()\n 7|\n 8|if not accelerator.is_main_process:\n 9| os.environ[\"WANDB_MODE\"] = \"disabled\"\n 10| print = lambda *args: None\n 11|\n 12|DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n 13|TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n 14|MODEL_DIR = \"/home/wyf/orcd/pool/reverse-llm/models\"\n 15|\n 16|base_model_name = f\"reverse-gpt2-0.35B-fineweb-10BT-ctx-1024\"\n 17|\n 18|USER_ROLE_NAME = \"user\"[::-1]\n 19|ASSISTANT_ROLE_NAME = \"assistant\"[::-1]\n 20|\n 21|context_length = 1024\n 22|\n 23|os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n 24|\n 25|\n 26|tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_bpe_200k\")\n 27|tokenizer.add_special_tokens({ \"additional_special_tokens\": [\"\", \"\"] })\n 28|\n 29|\n 30|from transformers import GPT2LMHeadModel, EarlyStoppingCallback\n 31|\n 32|# Load base model\n 33|model = GPT2LMHeadModel.from_pretrained(\n 34| f\"{MODEL_DIR}/reverse-gpt2-0.35B-fineweb-10BT-ctx-1024/checkpoint-9000\"\n 35| # f\"{MODEL_DIR}/reverse-gpt2-0.35B-fineweb-10BT-ctx-1024-chat-v2/checkpoint-105\"\n 36|)\n 37|\n 38|print(f\"Tokenizer vocab size: {len(tokenizer)}\")\n 39|model.resize_token_embeddings(len(tokenizer))\n 40|\n 41|\n 42|from datasets import Dataset, concatenate_datasets\n 43|\n 44|print(f\"Loading datasets...\")\n 45|tokenized = {\n 46| \"train\": concatenate_datasets([\n 47| Dataset.load_from_disk(f\"{DATA_DIR}/alpaca/tokenized_{context_length}_train\"),\n 48| Dataset.load_from_disk(f\"{DATA_DIR}/databricks-dolly/tokenized_{context_length}_train\"),\n 49| Dataset.load_from_disk(f\"{DATA_DIR}/ultrachat/tokenized_{context_length}_train\")\n 50| ]).shuffle(seed=0),\n 51| \"valid\": concatenate_datasets([\n 52| Dataset.load_from_disk(f\"{DATA_DIR}/alpaca/tokenized_{context_length}_valid\"),\n 53| Dataset.load_from_disk(f\"{DATA_DIR}/databricks-dolly/tokenized_{context_length}_valid\"),\n 54| Dataset.load_from_disk(f\"{DATA_DIR}/ultrachat/tokenized_{context_length}_valid\")\n 55| ]).shuffle(seed=0),\n 56|}\n 57|print(tokenized)\n 58|\n 59|import torch\n 60|import gc\n 61|\n 62|torch.cuda.empty_cache()\n 63|gc.collect()\n 64|\n 65|from transformers import TrainingArguments\n 66|from trl import SFTTrainer, SFTConfig\n 67|\n 68|# Set up training arguments\n 69|args = SFTConfig(\n 70| output_dir=f\"{MODEL_DIR}/{base_model_name}-chat-v15\",\n 71| report_to=\"wandb\",\n 72|\n 73| neftune_noise_alpha=15,\n 74| per_device_train_batch_size=144,\n 75| gradient_accumulation_steps=8,\n 76| fp16=True,\n 77| logging_steps=1,\n 78|\n 79| weight_decay=0.01,\n 80|\n 81| learning_rate=1.5e-4,\n 82| warmup_ratio=0.1,\n 83| lr_scheduler_type=\"cosine\",\n 84| # lr_scheduler_type=\"constant_with_warmup\",\n 85| num_train_epochs=20,\n 86|\n 87| eval_strategy=\"steps\",\n 88| eval_steps=10,\n 89| metric_for_best_model=\"eval_loss\",\n 90| greater_is_better=False,\n 91| save_steps=50,\n 92|\n 93| max_grad_norm=1.0,\n 94| \n 95| gradient_checkpointing=True,\n 96|)\n 97|\n 98|import wandb\n 99|\n100|wandb.init(\n101| project=\"reverse-llm-alpaca\",\n102| entity=\"womogenes-team\",\n103| config=args.to_dict(),\n104|)\n105|\n106|early_stopping_callback = EarlyStoppingCallback(\n107| early_stopping_patience=3,\n108|)\n109|\n110|# Create trainer\n111|trainer = SFTTrainer(\n112| model=model,\n113| args=args,\n114| train_dataset=tokenized[\"train\"],\n115| eval_dataset=tokenized[\"valid\"],\n116| callbacks=[early_stopping_callback],\n117|)\n118|\n119|trainer.train(resume_from_checkpoint=True)\n","created_at":"2026-07-18T07:21:44.936825Z","finish_reason":null,"metrics":null},{"message_id":"b0407d5c-560c-4f6d-978d-a3deaed8ccfc","node_id":41,"role":"tool","content":"\n 1|import modal\n 2|import subprocess\n 3|import os\n 4|import sys\n 5|\n 6|# Force tqdm to show progress bars in Modal containers\n 7|import tqdm\n 8|tqdm.tqdm._instances.clear()\n 9|original_isatty = sys.stdout.isatty\n 10|sys.stdout.isatty = lambda: True\n 11|sys.stderr.isatty = lambda: True\n 12|\n 13|image = (\n 14| modal.Image.debian_slim()\n 15| .uv_pip_install([\n 16| \"datasets<4.0.0\", \"tokenizers\", \"tqdm\", \"numpy\",\n 17| \"torch\", \"accelerate\", \"transformers\", \"wandb\",\n 18| ])\n 19| .add_local_dir(os.path.dirname(__file__), \"/root\", copy=True)\n 20|)\n 21|\n 22|volumes = {\n 23| \"/mnt/william\": modal.Volume.from_name(\"william\")\n 24|}\n 25|\n 26|app = modal.App(image=image, volumes=volumes)\n 27|\n 28|@app.function(\n 29| cpu=64,\n 30| # memory=512,\n 31| memory=1024*84,\n 32| volumes=volumes,\n 33| timeout=60*60*24,\n 34|)\n 35|def load_data():\n 36| print(\"Starting load_data.py...\")\n 37| exec(open(\"load_data/load_data.py\").read(), globals())\n 38| volumes[\"/mnt/william\"].commit()\n 39|\n 40|@app.function(\n 41| cpu=10,\n 42| # memory=1024*336,\n 43| memory=1024*84,\n 44| volumes=volumes,\n 45| timeout=60*60*24,\n 46|)\n 47|def train_tokenizer():\n 48| print(\"Starting train_tokenizer.py...\")\n 49| # 7 min to load data, ~20 min to train tokenizer\n 50| exec(open(\"tokenize/train_tokenizer.py\").read(), globals())\n 51| volumes[\"/mnt/william\"].commit()\n 52|\n 53|\n 54|@app.function(\n 55| cpu=32,\n 56| memory=1024*32,\n 57| volumes=volumes,\n 58| timeout=60*60*24,\n 59|)\n 60|def tokenize_data():\n 61| print(\"Starting tokenize_data.py...\")\n 62| exec(open(\"tokenize/tokenize_data.py\").read(), globals())\n 63| volumes[\"/mnt/william\"].commit()\n 64|\n 65|\n 66|n_gpus = 8\n 67|@app.function(\n 68| cpu=12,\n 69| gpu=f\"h200:{n_gpus}\",\n 70| memory=1024*48,\n 71| volumes=volumes,\n 72| timeout=60*60*24,\n 73| secrets=[modal.Secret.from_name(\"william-wandb-key\")]\n 74|)\n 75|def train_model():\n 76| print(\"Starting train_model.py...\")\n 77| subprocess.run([\n 78| \"accelerate\", \"launch\",\n 79| \"--num_processes\", str(n_gpus),\n 80| \"--num_machines\", \"1\",\n 81| \"--mixed_precision\", \"no\",\n 82| \"--dynamo_backend\", \"no\",\n 83| \"train/train.py\",\n 84| ])\n 85| volumes[\"/mnt/william\"].commit()\n","created_at":"2026-07-18T07:21:44.936827Z","finish_reason":null,"metrics":null},{"message_id":"54656780-f978-45ac-b08a-a37bbb70ad4b","node_id":43,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:10","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/load_data/load_data.py"}},{"id":"functions.read:11","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/tokenize/train_tokenizer.py"}},{"id":"functions.read:12","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/train.py"}}],"thinking":"I need to look at the load_data, tokenization details, and the key commit diffs, especially the \"approach shift\". The commit log shows some key commits:\n- 15aa4c1 Make initial commit (Aug 13)\n- 91b4b19 Organize stuff under folders and add fineweb training set (Aug 14)\n- 551c067 Make things even more robust ...? (Aug 14)\n- a90f806 Do another run with more data (Aug 15)\n- e9945e3 Fix spacing issue (Aug 15)\n- 5f8c877 Streamline pipeline?? (Aug 15)\n- ae4ebe6 Regroup and start doing modal (yay) (Aug 15)\n- 73d51c5 Set up more centralized running system (Aug 16)\n- 69f1942 Save to parquet (Aug 16)\n- 4659b5f Fix tokenization to be reverse (oopsy) (Aug 16)\n- 142b666 Tokenize in the right direction (Aug 16)\n- cdaf3a6 Organize for training (Aug 16)\n- af5cad2 Improve tokenization speed (Aug 16)\n- 2d3c969 Highkey go back to 1024 (Aug 16)\n- 36da088 Tighten up gpt2 architecture (Aug 17)\n- 6a6e7f3 Add eval steps (Aug 18)\n- 89f9dca Eval scripts for gpt2 (Aug 18)\n- 6a4ece3 Train on alpaca successfully (Aug 19)\n- f423b4c Integrate ultrachat data for instruct fine-tuning (Sep 8)\n- 0efc527 wip (Sep 8)\n- a384010 Add sample generations (Nov 5)\n\nThe key \"approach shifted\" seems to be around ae4ebe6 \"Regroup and start doing modal\" or the tokenization direction fix. Let me read more files and diffs.","created_at":"2026-07-18T07:21:46.289829Z","finish_reason":"tool_calls","metrics":{"ttft_ms":656,"total_time_ms":1352,"input_tokens":5175,"output_tokens":522,"cache_read_tokens":24192,"cache_creation_tokens":null,"tpot_ms":1.3333333333333333,"tokens_per_sec":750}},{"message_id":"e2d8a8f4-f56a-4929-a977-0cd3616a1e3a","node_id":44,"role":"tool","content":"\n 1|from datasets import DatasetDict, load_dataset\n 2|import time\n 3|import os\n 4|\n 5|os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n 6|os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n 7|os.environ[\"HF_DATASETS_CACHE\"] = \"/mnt/william/.cache\"\n 8|\n 9|### CONFIG ###\n 10|\n 11|dataset_name = \"fineweb-10BT\"\n 12|\n 13|DATA_DIR = \"/mnt/william/reverse-llm/data\"\n 14|TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n 15|\n 16|### CONFIG ###\n 17|\n 18|def main():\n 19| print(f\"Downloading dataset {dataset_name}...\")\n 20| time.sleep(1)\n 21|\n 22| raw_dataset = load_dataset(\n 23| \"HuggingFaceFW/fineweb-edu\",\n 24| split=\"train\",\n 25| name=\"sample-10BT\",\n 26| cache_dir=\"/mnt/william/.cache\",\n 27| )\n 28|\n 29| print(\"Generating split datasets...\")\n 30| split_datasets = raw_dataset.train_test_split(test_size=0.005, seed=0)\n 31| split_datasets = DatasetDict({\n 32| \"train\": split_datasets[\"train\"],\n 33| \"valid\": split_datasets[\"test\"],\n 34| })\n 35|\n 36| print(\"=== SAMPLE DATA (REVERSED) ===\")\n 37| print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n 38| print()\n 39|\n 40| for split in split_datasets:\n 41| save_path = f\"{DATA_DIR}/{dataset_name}/{split}\"\n 42| # split_datasets[split].select_columns([\"text\"]).map(\n 43| # lambda batch: { \"text\": [text[::-1] for text in batch[\"text\"]] },\n 44| # batched=True,\n 45| # batch_size=1_000,\n 46| # num_proc=int(os.cpu_count() * 1.5),\n 47| # remove_columns=[\"text\"],\n 48| # ).save_to_disk(save_path)\n 49|\n 50| split_datasets[split].select_columns([\"text\"]).save_to_disk(save_path)\n 51|\n 52| print(f\"Saved split {split} to {save_path}\")\n 53|\n 54|main()\n","created_at":"2026-07-18T07:21:46.359230Z","finish_reason":null,"metrics":null},{"message_id":"8bf93af7-3c57-4d4b-80d0-4c2ef59ef725","node_id":45,"role":"tool","content":"\n 1|import os\n 2|from tqdm import tqdm\n 3|\n 4|os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n 5|os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n 6|os.environ[\"HF_DATASETS_CACHE\"] = \"~/orcd/pool/.cache\"\n 7|\n 8|os.environ[\"PYTHONUNBUFFERED\"] = \"1\"\n 9|\n 10|### CONFIG ###\n 11|\n 12|dataset_name = \"fineweb-10BT\"\n 13|\n 14|DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n 15|TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n 16|\n 17|### CONFIG ###\n 18|def main():\n 19| print(f\"Loading dataset {dataset_name}...\")\n 20|\n 21| from datasets import Dataset\n 22|\n 23| train_dataset = Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\")\n 24| print(train_dataset)\n 25| print(f\"=== SAMPLE DATA (SHOULD READ FORWARDS) ===\")\n 26| print(train_dataset[0][\"text\"])\n 27|\n 28| from tokenizers import ByteLevelBPETokenizer\n 29|\n 30| def text_iterator():\n 31| n_samples = 200_000\n 32| print(f\"Using {n_samples:,} samples ({n_samples / len(train_dataset):.2%} of dataset)\")\n 33|\n 34| print(\"Shuffling dataset and selecting samples...\")\n 35| filtered_dataset = train_dataset.shuffle(seed=0).select(range(n_samples))\n 36| print(\"Generating text iterator...\")\n 37| for x in tqdm(filtered_dataset[\"text\"]):\n 38| yield x[::-1]\n 39|\n 40| print(\"Training tokenizer...\")\n 41| bpe_tokenizer = ByteLevelBPETokenizer()\n 42| bpe_tokenizer.train_from_iterator(\n 43| text_iterator(),\n 44| vocab_size=52_000,\n 45| min_frequency=5,\n 46| show_progress=True,\n 47| special_tokens = [\"\", \"\", \"\", \"\", \"\"]\n 48| )\n 49|\n 50| tokenizer_path = f\"{TOKENIZER_DIR}/fineweb_bpe_200k\"\n 51| print(f\"Saving tokenizer to {tokenizer_path}\")\n 52| os.makedirs(TOKENIZER_DIR, exist_ok=True)\n 53| bpe_tokenizer.save(tokenizer_path)\n 54|\n 55| print(f\"Done.\")\n 56|\n 57|main()\n","created_at":"2026-07-18T07:21:46.359234Z","finish_reason":null,"metrics":null},{"message_id":"03062e22-751a-4821-ad9d-034b8e50bd94","node_id":46,"role":"tool","content":"Tool 'read' validation failed: Failed to read file '/Users/root1/Downloads/projects/reverse-llm/train.py': not found: /Users/root1/Downloads/projects/reverse-llm/train.py","created_at":"2026-07-18T07:21:46.359237Z","finish_reason":null,"metrics":null},{"message_id":"29f34ff5-46e7-4c37-ab8e-cf64564dc592","node_id":48,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:13","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 15aa4c1 --stat --patch -- README.md train.py test.ipynb | head -300","timeout":10000}},{"id":"functions.exec:14","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 4659b5f --patch","timeout":10000}},{"id":"functions.exec:15","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show ae4ebe6 --patch | head -400","timeout":10000}}],"thinking":"The train.py was moved to train/train.py. I should get the diff for the initial commits, especially the shift to Modal and the tokenization direction. Let me look at the key commits.","created_at":"2026-07-18T07:21:47.324457Z","finish_reason":"tool_calls","metrics":{"ttft_ms":692,"total_time_ms":964,"input_tokens":1944,"output_tokens":190,"cache_read_tokens":29312,"cache_creation_tokens":null,"tpot_ms":1.431578947368421,"tokens_per_sec":698.5294117647059}},{"message_id":"76b9eb00-a12c-4724-82f1-059dae6ecf06","node_id":49,"role":"tool","content":"Output from command in shell 343a20:\ncommit 15aa4c1d19c9245ed9ea61372beee2be37563d1c\nAuthor: William Y. Feng \nDate: Wed Aug 13 16:50:49 2025 -0400\n\n Make initial commit\n---\n README.md | 9 +\n test.ipynb | 727 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\n train.py | 197 +++++++++++++++++\n 3 files changed, 933 insertions(+)\n\ndiff --git a/README.md b/README.md\nnew file mode 100644\nindex 0000000..dc9def2\n--- /dev/null\n+++ b/README.md\n@@ -0,0 +1,9 @@\n+# Resource usage:\n+\n+salloc -p mit_normal_gpu \\\n+ --gres=gpu:h100:2 \\\n+ --time=360 \\\n+ --cpus-per-gpu=4 \\\n+ --mem=32000\n+\n+cpus are being maxed out though\ndiff --git a/test.ipynb b/test.ipynb\nnew file mode 100644\nindex 0000000..ce89ceb\n--- /dev/null\n+++ b/test.ipynb\n@@ -0,0 +1,727 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 1,\n+ \"id\": \"b39863ca\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from datasets import load_dataset, Dataset, DatasetDict\\n\",\n+ \"\\n\",\n+ \"import os\\n\",\n+ \"os.environ[\\\"TOKENIZERS_PARALLELISM\\\"] = \\\"false\\\"\\n\",\n+ \"os.environ[\\\"PYTORCH_CUDA_ALLOC_CONF\\\"] = \\\"expandable_segments:True\\\"\\n\",\n+ \"\\n\",\n+ \"n_samples = 2_000_000\\n\",\n+ \"context_length = 1024\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 2,\n+ \"id\": \"be830d06\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"data\": {\n+ \"application/vnd.jupyter.widget-view+json\": {\n+ \"model_id\": \"43f699ff02f74e45939a9c6b15e8159a\",\n+ \"version_major\": 2,\n+ \"version_minor\": 0\n+ },\n+ \"text/plain\": [\n+ \"Resolving data files: 0%| | 0/27838 [00:00 \\u001b[39m\\u001b[32m1\\u001b[39m \\u001b[38;5;28;01mfrom\\u001b[39;00m\\u001b[38;5;250m \\u001b[39m\\u001b[34;01mtransformers\\u001b[39;00m\\u001b[38;5;250m \\u001b[39m\\u001b[38;5;28;01mimport\\u001b[39;00m PreTrainedTokenizerFast\\n\\u001b[32m 3\\u001b[39m tokenizer = PreTrainedTokenizerFast(\\n\\u001b[32m 4\\u001b[39m tokenizer_object=spm_tokenizer,\\n\\u001b[32m 5\\u001b[39m bos_token=\\u001b[33m\\\"\\u001b[39m\\u001b[33m\\u001b[39m\\u001b[33m\\\"\\u001b[39m, \\u001b[38;5;66;03m# Always added at start\\u001b[39;00m\\n\\u001b[32m (...)\\u001b[39m\\u001b[32m 8\\u001b[39m pad_token=\\u001b[33m\\\"\\u001b[39m\\u001b[33m\\u001b[39m\\u001b[33m\\\"\\u001b[39m, \\u001b[38;5;66;03m# Used for padding shorter sequences\\u001b[39;00m\\n\\u001b[32m 9\\u001b[39m )\\n\\u001b[32m 10\\u001b[39m tokenizer.save_pretrained(\\u001b[33m\\\"\\u001b[39m\\u001b[33m./tokenizers/spm_200k\\u001b[39m\\u001b[33m\\\"\\u001b[39m)\\n\",\n+ \"\\u001b[36mFile \\u001b[39m\\u001b[32m~/.conda/envs/torch/lib/python3.12/site-packages/transformers/__init__.py:982\\u001b[39m\\n\\u001b[32m 978\\u001b[39m \\u001b[38;5;28;01mimport\\u001b[39;00m\\u001b[38;5;250m \\u001b[39m\\u001b[34;01msys\\u001b[39;00m\\n\\u001b[32m 980\\u001b[39m _import_structure = {k: \\u001b[38;5;28mset\\u001b[39m(v) \\u001b[38;5;28;01mfor\\u001b[39;00m k, v \\u001b[38;5;129;01min\\u001b[39;00m _import_structure.items()}\\n\\u001b[32m--> \\u001b[39m\\u001b[32m982\\u001b[39m import_structure = \\u001b[43mdefine_import_structure\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mPath\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[34;43m__file__\\u001b[39;49m\\u001b[43m)\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mparent\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43m/\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[33;43mmodels\\u001b[39;49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mprefix\\u001b[49m\\u001b[43m=\\u001b[49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[33;43mmodels\\u001b[39;49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[43m)\\u001b[49m\\n\\u001b[32m 983\\u001b[39m import_structure[\\u001b[38;5;28mfrozenset\\u001b[39m({})].update(_import_structure)\\n\\u001b[32m 985\\u001b[39m sys.modules[\\u001b[34m__name__\\u001b[39m] = _LazyModule(\\n\\u001b[32m 986\\u001b[39m \\u001b[34m__name__\\u001b[39m,\\n\\u001b[32m 987\\u001b[39m \\u001b[38;5;28mglobals\\u001b[39m()[\\u001b[33m\\\"\\u001b[39m\\u001b[33m__file__\\u001b[39m\\u001b[33m\\\"\\u001b[39m],\\n\\u001b[32m (...)\\u001b[39m\\u001b[32m 990\\u001b[39m extra_objects={\\u001b[33m\\\"\\u001b[39m\\u001b[33m__version__\\u001b[39m\\u001b[33m\\\"\\u001b[39m: __version__},\\n\\u001b[32m 991\\u001b[39m )\\n\",\n+ \"\\u001b[36mFile \\u001b[39m\\u001b[32m~/.conda/envs/torch/lib/python3.12/site-packages/transformers/utils/import_utils.py:2825\\u001b[39m, in \\u001b[36mdefine_import_structure\\u001b[39m\\u001b[34m(module_path, prefix)\\u001b[39m\\n\\u001b[32m 2801\\u001b[39m \\u001b[38;5;129m@lru_cache\\u001b[39m\\n\\u001b[32m 2802\\u001b[39m \\u001b[38;5;28;01mdef\\u001b[39;00m\\u001b[38;5;250m \\u001b[39m\\u001b[34mdefine_import_structure\\u001b[39m(module_path: \\u001b[38;5;28mstr\\u001b[39m, prefix: Optional[\\u001b[38;5;28mstr\\u001b[39m] = \\u001b[38;5;28;01mNone\\u001b[39;00m) -> IMPORT_STRUCTURE_T:\\n\\u001b[32m 2803\\u001b[39m \\u001b[38;5;250m \\u001b[39m\\u001b[33;03m\\\"\\\"\\\"\\u001b[39;00m\\n\\u001b[32m 2804\\u001b[39m \\u001b[33;03m This method takes a module_path as input and creates an import structure digestible by a _LazyModule.\\u001b[39;00m\\n\\u001b[32m 2805\\u001b[39m \\n\\u001b[32m (...)\\u001b[39m\\u001b[32m 2823\\u001b[39m \\u001b[33;03m If `prefix` is not None, it will add that prefix to all keys in the returned dict.\\u001b[39;00m\\n\\u001b[32m 2824\\u001b[39m \\u001b[33;03m \\\"\\\"\\\"\\u001b[39;00m\\n\\u001b[32m-> \\u001b[39m\\u001b[32m2825\\u001b[39m import_structure = \\u001b[43mcreate_import_structure_from_path\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mmodule_path\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[32m 2826\\u001b[39m spread_dict = spread_import_structure(import_structure)\\n\\u001b[32m 2828\\u001b[39m \\u001b[38;5;28;01mif\\u001b[39;00m prefix \\u001b[38;5;129;01mis\\u001b[39;00m \\u001b[38;5;28;01mNone\\u001b[39;00m:\\n\",\n+ \"\\u001b[36mFile \\u001b[39m\\u001b[32m~/.conda/envs/torch/lib/python3.12/site-packages/transformers/utils/import_utils.py:2538\\u001b[39m, in \\u001b[36mcreate_import_structure_from_path\\u001b[39m\\u001b[34m(module_path)\\u001b[39m\\n\\u001b[32m 2536\\u001b[39m \\u001b[38;5;28;01mfor\\u001b[39;00m f \\u001b[38;5;129;01min\\u001b[39;00m os.listdir(module_path):\\n\\u001b[32m 2537\\u001b[39m \\u001b[38;5;28;01mif\\u001b[39;00m f != \\u001b[33m\\\"\\u001b[39m\\u001b[33m__pycache__\\u001b[39m\\u001b[33m\\\"\\u001b[39m \\u001b[38;5;129;01mand\\u001b[39;00m os.path.isdir(os.path.join(module_path, f)):\\n\\u001b[32m-> \\u001b[39m\\u001b[32m2538\\u001b[39m import_structure[f] = \\u001b[43mcreate_import_structure_from_path\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mos\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mpath\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mjoin\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mmodule_path\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mf\\u001b[49m\\u001b[43m)\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[32m 2540\\u001b[39m \\u001b[38;5;28;01melif\\u001b[39;00m \\u001b[38;5;129;01mnot\\u001b[39;00m os.path.isdir(os.path.join(directory, f)):\\n\\u001b[32m 2541\\u001b[39m adjacent_modules.append(f)\\n\",\n+ \"\\u001b[36mFile \\u001b[39m\\u001b[32m~/.conda/envs/torch/lib/python3.12/site-packages/transformers/utils/import_utils.py:2562\\u001b[39m, in \\u001b[36mcreate_import_structure_from_path\\u001b[39m\\u001b[34m(module_path)\\u001b[39m\\n\\u001b[32m 2559\\u001b[39m \\u001b[38;5;28;01mif\\u001b[39;00m \\u001b[38;5;129;01mnot\\u001b[39;00m module_name.endswith(\\u001b[33m\\\"\\u001b[39m\\u001b[33m.py\\u001b[39m\\u001b[33m\\\"\\u001b[39m):\\n\\u001b[32m 2560\\u001b[39m \\u001b[38;5;28;01mcontinue\\u001b[39;00m\\n\\u001b[32m-> \\u001b[39m\\u001b[32m2562\\u001b[39m \\u001b[43m\\u001b[49m\\u001b[38;5;28;43;01mwith\\u001b[39;49;00m\\u001b[43m \\u001b[49m\\u001b[38;5;28;43mopen\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43mos\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mpath\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mjoin\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mdirectory\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mmodule_name\\u001b[49m\\u001b[43m)\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mencoding\\u001b[49m\\u001b[43m=\\u001b[49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[33;43mutf-8\\u001b[39;49m\\u001b[33;43m\\\"\\u001b[39;49m\\u001b[43m)\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[38;5;28;43;01mas\\u001b[39;49;00m\\u001b[43m \\u001b[49m\\u001b[43mf\\u001b[49m\\u001b[43m:\\u001b[49m\\n\\u001b[32m 2563\\u001b[39m \\u001b[43m \\u001b[49m\\u001b[43mfile_content\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43m=\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mf\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mread\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[32m 2565\\u001b[39m \\u001b[38;5;66;03m# Remove the .py suffix\\u001b[39;00m\\n\",\n+ \"\\u001b[31mKeyboardInterrupt\\u001b[39m: \"\n+ ]\n+ }\n+ ],\n+ \"source\": [\n+ \"from transformers import PreTrainedTokenizerFast\\n\",\n+ \"\\n\",\n+ \"tokenizer = PreTrainedTokenizerFast(\\n\",\n+ \" tokenizer_object=spm_tokenizer,\\n\",\n+ \" bos_token=\\\"\\\", # Always added at start\\n\",\n+ \" eos_token=\\\"\\\", # Always added at end \\n\",\n+ \" unk_token=\\\"\\\", # Replaces unknown words\\n\",\n+ \" pad_token=\\\"\\\", # Used for padding shorter sequences\\n\",\n+ \")\\n\",\n+ \"tokenizer.save_pretrained(\\\"./tokenizers/spm_200k\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 4,\n+ \"id\": \"8327d484\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from transformers import PreTrainedTokenizerFast\\n\",\n+ \"\\n\",\n+ \"tokenizer = PreTrainedTokenizerFast.from_pretrained(\\\"./tokenizers/spm_200k\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 10,\n+ \"id\": \"dbcf8b64\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"# USES CONTEXT LENGTH\\n\",\n+ \"def tokenize(element):\\n\",\n+ \" outputs = tokenizer(\\n\",\n+ \" element[\\\"text\\\"],\\n\",\n+ \" truncation=True,\\n\",\n+ \" max_length=context_length,\\n\",\n+ \" return_overflowing_tokens=True,\\n\",\n+ \" return_length=True,\\n\",\n+ \" )\\n\",\n+ \" input_batch = []\\n\",\n+ \" for length, input_ids in zip(outputs[\\\"length\\\"], outputs[\\\"input_ids\\\"]):\\n\",\n+ \" if length == context_length:\\n\",\n+ \" input_batch.append(input_ids)\\n\",\n+ \" return {\\\"input_ids\\\": input_batch}\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 11,\n+ \"id\": \"048e5774\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"data\": {\n+ \"application/vnd.jupyter.widget-view+json\": {\n+ \"model_id\": \"93827b391d5d4d4797630b95f288c063\",\n+ \"version_major\": 2,\n+ \"version_minor\": 0\n+ },\n+ \"text/plain\": [\n+ \"Map: 0%| | 0/180000 [00:00\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/6dfe7238/content.txt\n","created_at":"2026-07-18T07:21:47.417545Z","finish_reason":null,"metrics":null},{"message_id":"12b39acb-2395-4c8e-a549-0c34441037b1","node_id":50,"role":"tool","content":"Output from command in shell f4c9c7:\ncommit 4659b5f88997a6256d14a2422bc0aabde805ba99\nAuthor: William Y. Feng \nDate: Sat Aug 16 02:41:20 2025 -0400\n\n Fix tokenization to be reverse (oops)\n\ndiff --git a/train/tokenize.sh b/train/tokenize.sh\nindex 3f96985..283ae8e 100644\n--- a/train/tokenize.sh\n+++ b/train/tokenize.sh\n@@ -1,6 +1,6 @@\n #!/bin/bash\n \n-#SBATCH -p mit_normal_gpu\n+#SBATCH -p mit_normal\n #SBATCH --mincpus 32\n #SBATCH --mem 64000\n #SBATCH --output runs/tokenize.log\n@@ -8,4 +8,3 @@\n \n #/home/wyf/.conda/envs/torch/bin/accelerate launch train_fineweb.py\n /home/wyf/.conda/envs/torch/bin/python tokenize_data.py\n-\ndiff --git a/train/tokenize_data.py b/train/tokenize_data.py\nindex 5f7f2ba..a148417 100644\n--- a/train/tokenize_data.py\n+++ b/train/tokenize_data.py\n@@ -3,7 +3,7 @@ from datasets import Dataset, DatasetDict\n import sys\n \n import os\n-os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n+os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n n_samples = 10_000_000_000\n@@ -31,7 +31,7 @@ print(f\"Context length: {context_length}\")\n \n def tokenize(element):\n outputs = tokenizer(\n- element[\"text\"],\n+ element[\"text\"][::-1],\n truncation=True,\n max_length=context_length,\n return_overflowing_tokens=True,\n\n\nExit code: 0","created_at":"2026-07-18T07:21:47.417554Z","finish_reason":null,"metrics":null},{"message_id":"551d82df-572f-448c-babe-ad97de83ca6d","node_id":51,"role":"tool","content":"Output from command in shell 17369b:\ncommit ae4ebe66d0a5e51b54610db8ff8c3f3d421664c6\r\nAuthor: William Y. Feng \r\nDate: Fri Aug 15 23:07:22 2025 -0400\r\n\r\n Regroup and start doing modal (yay)\r\n\r\ndiff --git a/.gitignore b/.gitignore\r\nindex 34824be..fa8c2ae 100644\r\n--- a/.gitignore\r\n+++ b/.gitignore\r\n@@ -1,4 +1,4 @@\r\n-**/__pycache_/\r\n+__pycache__/\r\n \r\n data/\r\n reverse-model*/\r\ndiff --git a/__init__.py b/__init__.py\r\nnew file mode 100644\r\nindex 0000000..e69de29\r\ndiff --git a/load_data/__init__.py b/load_data/__init__.py\r\nnew file mode 100644\r\nindex 0000000..e69de29\r\ndiff --git a/load_data/load_data.py b/load_data/load_data.py\r\nindex a4a3894..6d7f6a8 100644\r\n--- a/load_data/load_data.py\r\n+++ b/load_data/load_data.py\r\n@@ -1,57 +1,39 @@\r\n-from datasets import load_dataset, Dataset, DatasetDict\r\n-\r\n+from datasets import load_dataset, DatasetDict\r\n+import time\r\n import os\r\n+\r\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\r\n os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\r\n+os.environ[\"HF_DATASETS_CACHE\"] = \"/mnt/william/.cache\"\r\n \r\n-n_samples = 2_000_000\r\n-context_length = 1024\r\n-\r\n+### CONFIG ###\r\n \r\n+dataset_name = \"fineweb-10BT\"\r\n \r\n-DATA_DIR = \"/home/wyf/ai/causal-llm/data\"\r\n+DATA_DIR = \"/mnt/william/reverse-llm/data\"\r\n+TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\r\n \r\n-# dataset = \"dclm\"\r\n-dataset_name = \"fineweb\"\r\n+### CONFIG ###\r\n \r\n-# Takes like 30s to load (it's bad)\r\n-raw_dataset = load_dataset(\r\n- # \"mlfoundations/dclm-baseline-1.0\",\r\n- \"HuggingFaceFW/fineweb-edu\",\r\n- split=\"train\",\r\n- streaming=True,\r\n-)\r\n+if __name__ == \"__main__\":\r\n+ print(f\"Downloading dataset {dataset_name}...\")\r\n+ time.sleep(1)\r\n \r\n+ # Takes like 30s to load (it's bad)\r\n+ raw_dataset = load_dataset(\r\n+ \"HuggingFaceFW/fineweb-edu\",\r\n+ split=\"train\",\r\n+ name=\"sample-10BT\",\r\n+ cache_dir=\"/mnt/william/.cache\",\r\n+ )\r\n \r\n-from tqdm import tqdm\r\n-\r\n-def filter_dataset(dataset):\r\n- # filtered = []\r\n- # for sample in tqdm(iter(dataset[\"train\"].take(n_samples)), total=n_samples):\r\n- # # IMPORTANT REVERSAL STEP\r\n- # filtered.append(sample[\"text\"][::-1])\r\n- # return filtered\r\n+ print(\"Generating split datasets...\")\r\n+ split_datasets = raw_dataset.train_test_split(test_size=0.005, seed=0)\r\n+ split_datasets = DatasetDict({\r\n+ \"train\": split_datasets[\"train\"],\r\n+ \"valid\": split_datasets[\"test\"],\r\n+ })\r\n \r\n- return (\r\n- dataset\r\n- .select_columns([\"text\"])\r\n- .map(lambda s: {\"text\": s[\"text\"][::-1]})\r\n- )\r\n- \r\n-# 1k examples: 4.0s\r\n-\r\n-print(\"Generating split datasets...\")\r\n-raw_dataset_with_tqdm = [x for x in tqdm(raw_dataset.take(n_samples), total=n_samples)]\r\n-split_datasets = (\r\n- Dataset.from_list(list(raw_dataset_with_tqdm))\r\n- .train_test_split(test_size=0.1, seed=0)\r\n-)\r\n-datasets = DatasetDict({\r\n- \"train\": filter_dataset(split_datasets[\"train\"]),\r\n- \"valid\": filter_dataset(split_datasets[\"test\"]),\r\n-})\r\n-\r\n-\r\n-print(\"Saving datasets...\")\r\n-for split_name, dataset in datasets.items():\r\n- dataset.to_parquet(f\"{DATA_DIR}/{dataset_name}_{n_samples}/{split_name}.parquet\")\r\n+ print(\"=== SAMPLE DATA ===\")\r\n+ print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\r\n+ print()\r\ndiff --git a/load_data/load_data.sh b/load_data/load_data.sh\r\ndeleted file mode 100644\r\nindex 4549592..0000000\r\n--- a/load_data/load_data.sh\r\n+++ /dev/null\r\n@@ -1,9 +0,0 @@\r\n-#!/bin/bash\r\n-\r\n-#SBATCH -p mit_normal\r\n-#SBATCH --mincpus 64\r\n-#SBATCH --mem 256000\r\n-\r\n-module load miniforge\r\n-\r\n-/home/wyf/.conda/envs/torch/bin/python load_data.py\r\ndiff --git a/load_data/modal_runner.py b/load_data/modal_runner.py\r\nnew file mode 100644\r\nindex 0000000..6940ee2\r\n--- /dev/null\r\n+++ b/load_data/modal_runner.py\r\n@@ -0,0 +1,25 @@\r\n+import modal\r\n+import subprocess\r\n+import os\r\n+\r\n+image = (\r\n+ modal.Image.debian_slim()\r\n+ .uv_pip_install([\"datasets\"])\r\n+ .add_local_file(\"load_data/load_data.py\", \"/root/load_data.py\", copy=True)\r\n+ .add_local_file(\"load_data/test.py\", \"/root/test.py\", copy=True)\r\n+)\r\n+app = modal.App(image=image)\r\n+\r\n+volumes = {\r\n+ \"/mnt/william\": modal.Volume.from_name(\"william\")\r\n+}\r\n+\r\n+@app.function(\r\n+ cpu=2,\r\n+ memory=512,\r\n+ volumes=volumes,\r\n+ timeout=60*60*24,\r\n+)\r\n+def main():\r\n+ print(\"Starting load_data.py...\")\r\n+ subprocess.run([\"python\", \"load_data.py\"])\r\ndiff --git a/load_data/slurm-2599547_2M_dclm_gen.out b/load_data/slurm-2599547_2M_dclm_gen.out\r\ndeleted file mode 100644\r\nindex 8f7c887..0000000\r\n--- a/load_data/slurm-2599547_2M_dclm_gen.out\r\n+++ /dev/null\r\n@@ -1,28 +0,0 @@\r\n-Generating split datasets...\r\n-\r\n- 0%| | 0/2000000 [00:00\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/a5891943/content.txt\n","created_at":"2026-07-18T07:21:47.417624Z","finish_reason":null,"metrics":null},{"message_id":"7441525c-abdd-426f-a296-30b2bcac3f92","node_id":53,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:16","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 15aa4c1:train.py | head -250","timeout":10000}},{"id":"functions.exec:17","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 142b666 --patch | head -300","timeout":10000}},{"id":"functions.exec:18","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 2d3c969 --patch","timeout":10000}}],"thinking":"I have enough info. Let me also look at the initial train.py and the tokenization fix commit details to understand the approach shift. Let me check the original train.py in the initial commit.","created_at":"2026-07-18T07:21:48.851066Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1186,"total_time_ms":1432,"input_tokens":15930,"output_tokens":182,"cache_read_tokens":31232,"cache_creation_tokens":null,"tpot_ms":1.3516483516483517,"tokens_per_sec":739.8373983739838}},{"message_id":"4e10d786-ac17-4dcc-9bc8-5388167d0775","node_id":54,"role":"tool","content":"Output from command in shell 2f322a:\n### IMPORTS\nfrom datasets import load_dataset, Dataset, DatasetDict\n\nimport os\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\nos.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n\nn_samples = 200_000\ncontext_length = 1024\n\n\n### LOAD DATASETS\ndatasets = DatasetDict({\n \"train\": Dataset.from_parquet(f\"./data/dclm_{n_samples}/train.parquet\"),\n \"valid\": Dataset.from_parquet(f\"./data/dclm_{n_samples}/valid.parquet\")\n})\n\n\n\n### LOAD TOKENIZER\nfrom transformers import PreTrainedTokenizerFast\ntokenizer = PreTrainedTokenizerFast.from_pretrained(\"./tokenizers/spm_200k\")\n\n\n\n### LOAD TOKENIZED DATASETS\ntokenized_dataset = Dataset.from_parquet(f\"./data/dclm_{n_samples}_tokenized_{context_length}.parquet\")\ntokenized_dataset_valid = Dataset.from_parquet(f\"./data/dclm_{n_samples}_tokenized_{context_length}_valid.parquet\")\n\nprint(tokenized_dataset)\nprint(f\"Produced dataset of {tokenized_dataset.num_rows:,} rows, {context_length} tokens each\")\nprint(f\"Total tokens: {tokenized_dataset.num_rows * context_length:,}\")\n\nprint(f\"Tokenizer vocab size: {len(tokenizer)}\")\nprint(f\"Model config vocab size: {tokenizer.vocab_size}\")\nprint(f\"BOS token ID: {tokenizer.bos_token_id}\")\nprint(f\"EOS token ID: {tokenizer.eos_token_id}\")\nprint(f\"PAD token ID: {tokenizer.pad_token_id}\")\n\n# Check a sample tokenization\nsample_text = \"hello world\"\ntokens = tokenizer(sample_text)\nprint(f\"Sample tokens: {tokens}\")\n\n\n### INITIALIZE MODEL\n\n# 3.2s to initialize model\nfrom transformers import LlamaConfig, LlamaForCausalLM\nimport torch\n\nmodel_size = \"2B\"\n\nconfig = LlamaConfig(\n vocab_size=len(tokenizer),\n max_position_embeddings=8192,\n hidden_size=2048 if model_size == \"2B\" else 3072,\n intermediate_size=16384 if model_size == \"2B\" else 24576,\n num_hidden_layers=18 if model_size == \"2B\" else 28,\n num_attention_heads=8 if model_size == \"2B\" else 16,\n num_key_value_heads=1 if model_size == \"2B\" else 16,\n rms_norm_eps=1e-5,\n tie_word_embeddings=False,\n rope_scaling=None,\n bos_token_id=tokenizer.bos_token_id,\n eos_token_id=tokenizer.eos_token_id,\n)\n\n# model = LlamaForCausalLM(config).to(\"cuda\")\nmodel = LlamaForCausalLM.from_pretrained(\"reverse-model-2B/checkpoint-1993\").to(\"cuda\")\n\nmodel_size = sum(t.numel() for t in model.parameters())\nprint(f\"Model size: {model_size/1000**2:.1f}M parameters\")\n\n### SET UP DATA COLLATOR\nfrom transformers import DataCollatorForLanguageModeling\n\ntokenizer.pad_token = \"\"\ntokenizer.bos_token = \"\"\ntokenizer.eos_token = \"\"\ntokenizer.unk_token = \"\"\ndata_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)\n\n\n\n### TRAINING ARGS\n# 0.1s to initialize training args\n\nfrom transformers import Trainer, TrainingArguments\n\nargs = TrainingArguments(\n output_dir=\"reverse-model-2B\",\n \n # Batch size settings - LEDOM uses global batch size of 1024 sequences\n per_device_train_batch_size=1, # Micro-batch size per GPU\n per_device_eval_batch_size=1, # Used in their fine-tuning setup\n gradient_accumulation_steps=32, # To achieve global batch size (adjust based on GPU count)\n\n eval_strategy=\"steps\", # Evaluate every N steps\n eval_steps=5000, # Eval every N steps \n logging_steps=1, # More frequent logging to match their monitoring\n \n # Training duration - LEDOM trained for ~51,900 iterations for 7B model\n num_train_epochs=1, # Keep as 1 epoch since they trained on 435B tokens once\n \n # Optimizer settings - match LEDOM exactly\n optim=\"adamw_torch\",\n learning_rate=2e-5, # Peak learning rate: 2×10⁻⁴ \n weight_decay=0.1, # Matches their setting\n adam_beta1=0.9, # Adam β₁\n adam_beta2=0.95, # Adam β₂ \n adam_epsilon=1e-8, # Adam ε\n \n # Learning rate schedule - LEDOM uses cosine with specific warmup\n lr_scheduler_type=\"cosine\",\n warmup_steps=10,\n\n # Gradient settings\n max_grad_norm=100.0, # Gradient clipping norm\n \n # Precision - LEDOM uses BF16, not FP16\n bf16=True, # Use BF16 instead of FP16\n fp16=False, # Disable FP16\n \n # Checkpointing\n save_steps=200,\n save_total_limit=3, # Reasonable limit for storage\n save_only_model=True,\n \n # Additional LEDOM-specific settings\n dataloader_num_workers=2, # For efficiency\n remove_unused_columns=False, # Keep all data columns\n \n # Disable features not used in LEDOM training\n load_best_model_at_end=False,\n)\n\ntrainer = Trainer(\n model=model,\n tokenizer=tokenizer,\n args=args,\n data_collator=data_collator,\n train_dataset=tokenized_dataset,\n eval_dataset=tokenized_dataset_valid,\n)\n\n\n\n### TRAINING\ntorch.cuda.empty_cache()\n\n# 1m for 1k samples (2.2M tokens)\ntrainer.train()\n\n\n\n### EVALUATE\n\n# import torch\n# from transformers import pipeline\n\n# device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n# pipe = pipeline(\n# \"text-generation\", model=\"./reverse-model/checkpoint-9\", device=device, \n# )\n\nimport os\nimport torch\nfrom transformers import pipeline\n\n# Base model directory\nbase_dir = \"./reverse-model-2B\"\n\n# Find the first subdirectory (sorted for consistency)\nsubdirs = [d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d))]\nif not subdirs:\n raise FileNotFoundError(f\"No subdirectories found in {base_dir}\")\nfirst_checkpoint = os.path.join(base_dir, sorted(subdirs)[0])\n\nprint(f\"Using model from: {first_checkpoint}\")\n\n# Device selection\ndevice = 0 if torch.cuda.is_available() else -1\n\n# Load the pipeline\npipe = pipeline(\n \"text-generation\",\n model=first_checkpoint,\n device=device\n)\n\n\ntext = pipe(\".olleh\", num_return_sequences=1)[0][\"generated_text\"]\n\nprint(\"generated text:\\n\", text)\nprint()\nprint(\"generated text (reversed):\\n\", text[::-1])\n\nExit code: 0","created_at":"2026-07-18T07:21:48.933701Z","finish_reason":null,"metrics":null},{"message_id":"26ca41fa-d5b8-4396-bced-4a04dd16ce54","node_id":55,"role":"tool","content":"Output from command in shell af9cb1:\ncommit 142b6669d687e7494750c1f271d0cbadf7ddbf95\nAuthor: William Y. Feng \nDate: Sat Aug 16 11:49:49 2025 -0400\n\n Tokenize in the right direction\n\ndiff --git a/.gitignore b/.gitignore\nindex 34824be..fa8c2ae 100644\n--- a/.gitignore\n+++ b/.gitignore\n@@ -1,4 +1,4 @@\n-**/__pycache_/\n+__pycache__/\n \n data/\n reverse-model*/\ndiff --git a/train/tokenize_data.py b/train/tokenize_data.py\nindex a148417..363f48b 100644\n--- a/train/tokenize_data.py\n+++ b/train/tokenize_data.py\n@@ -23,46 +23,50 @@ split_datasets = DatasetDict({\n })\n \n # Load pretrained tokenizer\n+tokenizer_path = f\"{TOKENIZER_DIR}/fineweb_spm_1M\"\n+print(f\"Loading pretrained tokenizer from {tokenizer_path}...\")\n from transformers import PreTrainedTokenizerFast\n-tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_1M\")\n+tokenizer = PreTrainedTokenizerFast.from_pretrained(tokenizer_path)\n \n # USES CONTEXT LENGTH\n print(f\"Context length: {context_length}\")\n+print(f\"Sample untokenized data: {split_datasets['train'][0]['text'][:100]}\")\n \n def tokenize(element):\n outputs = tokenizer(\n- element[\"text\"][::-1],\n+ # REVERSING because dataset was not reversed when saved\n+ [x[::-1] for x in element[\"text\"]],\n truncation=True,\n max_length=context_length,\n- return_overflowing_tokens=True,\n+ return_overflowing_tokens=False,\n return_length=True,\n )\n input_batch = []\n for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n- if length == context_length:\n- input_batch.append(input_ids)\n+ input_batch.append(input_ids)\n return {\"input_ids\": input_batch}\n \n # 25s to parse 1k examples\n # 4m 40s to parse 10k examples\n # 7m 50s to parse 200k examples\n-tokenized_dataset_train = split_datasets[\"train\"].map(\n- tokenize, batched=True, remove_columns=[\"text\"], batch_size=32)\n-tokenized_dataset_valid = split_datasets[\"valid\"].map(\n- tokenize, batched=True, remove_columns=[\"text\"], batch_size=32)\n+tokenized_dataset = {}\n+for split in [\"train\", \"valid\"]:\n+ print(f\"Tokenizing split {split}...\")\n+ tokenized_dataset[split] = split_datasets[split] \\\n+ .select_columns(\"text\") \\\n+ .map(tokenize, batched=True, batch_size=32)\n \n+print(f\"Sample data: {tokenizer.decode(tokenized_dataset['train'].take(0)['input_ids'])[:100]}\")\n \n # Takes a hot minute to save\n # 30min for 2M fineweb examples\n # 3m for 1/9 of that\n print(\"Saving training dataset...\")\n-tokenized_dataset_train.to_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_train.parquet\")\n-print(\"Saving valid dataset...\")\n-tokenized_dataset_valid.to_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_valid.parquet\")\n+for split in [\"train\", \"valid\"]:\n+ print(f\"Saving {split} dataset...\")\n+ tokenized_dataset[split].to_parquet(f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}.parquet\")\n \n \n-print(tokenized_dataset_train)\n-print(f\"Produced dataset of {tokenized_dataset_train.num_rows:,} rows, {context_length} tokens each\")\n-print(f\"Total tokens: {tokenized_dataset_train.num_rows * context_length:,}\")\n+print(tokenized_dataset['train'])\n+print(f\"Produced dataset of {tokenized_dataset['train'].num_rows:,} rows, {context_length} tokens each\")\n+print(f\"Total tokens: {tokenized_dataset['train'].num_rows * context_length:,}\")\ndiff --git a/train/train.sh b/train/train.sh\nindex 55b274c..522de09 100644\n--- a/train/train.sh\n+++ b/train/train.sh\n@@ -4,7 +4,7 @@\n #SBATCH --gres=gpu:h200:2\n #SBATCH --mincpus 32\n #SBATCH --mem 64000\n-#SBATCH --output runs/fineweb_2_000_000_run_014.log\n+#SBATCH --output runs/fineweb-10BT/003.log\n #SBATCH --time 360 \n \n /home/wyf/.conda/envs/torch/bin/accelerate launch train_fineweb.py\ndiff --git a/train/train_fineweb.ipynb b/train/train_fineweb.ipynb\nindex 9963ae8..2e98d3b 100644\n--- a/train/train_fineweb.ipynb\n+++ b/train/train_fineweb.ipynb\n@@ -2,7 +2,7 @@\n \"cells\": [\n {\n \"cell_type\": \"code\",\n- \"execution_count\": 4,\n+ \"execution_count\": 2,\n \"id\": \"b39863ca\",\n \"metadata\": {},\n \"outputs\": [],\n@@ -27,61 +27,19 @@\n },\n {\n \"cell_type\": \"code\",\n- \"execution_count\": null,\n+ \"execution_count\": 2,\n \"id\": \"be830d06\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n- \"model_id\": \"8a01eafa46764e4eb75ad8b7b253ce18\",\n- \"version_major\": 2,\n- \"version_minor\": 0\n- },\n- \"text/plain\": [\n- \"Resolving data files: 0%| | 0/2410 [00:00\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/66447ea0/content.txt\n","created_at":"2026-07-18T07:21:48.933718Z","finish_reason":null,"metrics":null},{"message_id":"9ad3e25d-e5db-42b0-a003-a781beb70fff","node_id":56,"role":"tool","content":"Output from command in shell dc7bc3:\ncommit 2d3c969ff8eb8d66439ccf8ddb9681348e90921e\nAuthor: William Y. Feng \nDate: Sat Aug 16 18:37:38 2025 -0400\n\n Highkey go back to 1024\n\ndiff --git a/load_data/load_data.py b/load_data/load_data.py\nindex 65f6480..0638c36 100644\n--- a/load_data/load_data.py\n+++ b/load_data/load_data.py\n@@ -39,9 +39,16 @@ def main():\n \n for split in split_datasets:\n save_path = f\"{DATA_DIR}/{dataset_name}/{split}\"\n- split_datasets[split].select_columns([\"text\"]).map(\n- lambda x: { \"text\": x[\"text\"][::-1] },\n- remove_columns=[\"text\"],\n- ).save_to_disk(save_path)\n+ # split_datasets[split].select_columns([\"text\"]).map(\n+ # lambda batch: { \"text\": [text[::-1] for text in batch[\"text\"]] },\n+ # batched=True,\n+ # batch_size=1_000,\n+ # num_proc=int(os.cpu_count() * 1.5),\n+ # remove_columns=[\"text\"],\n+ # ).save_to_disk(save_path)\n+\n+ split_datasets[split].select_columns([\"text\"]).save_to_disk(save_path)\n+\n+ print(f\"Saved split {split} to {save_path}\")\n \n main()\ndiff --git a/modal_runner.py b/modal_runner.py\nindex 59eef31..ac93325 100644\n--- a/modal_runner.py\n+++ b/modal_runner.py\n@@ -26,7 +26,7 @@ volumes = {\n app = modal.App(image=image, volumes=volumes)\n \n @app.function(\n- cpu=8,\n+ cpu=64,\n # memory=512,\n memory=1024*84,\n volumes=volumes,\n@@ -52,8 +52,8 @@ def train_tokenizer():\n \n \n @app.function(\n- cpu=24,\n- memory=1024*4,\n+ cpu=32,\n+ memory=1024*32,\n volumes=volumes,\n timeout=60*60*24,\n )\n@@ -65,7 +65,7 @@ def tokenize_data():\n \n n_gpus = 8\n @app.function(\n- cpu=10,\n+ cpu=12,\n gpu=f\"h200:{n_gpus}\",\n memory=1024*48,\n volumes=volumes,\ndiff --git a/tokenize/tokenize_data.py b/tokenize/tokenize_data.py\nindex a48cbb9..3889b8f 100644\n--- a/tokenize/tokenize_data.py\n+++ b/tokenize/tokenize_data.py\n@@ -19,7 +19,7 @@ def main():\n \n print(split_datasets)\n \n- print(\"=== BEGIN EXAMPLE DATA (REVERSED) ===\")\n+ print(\"=== BEGIN EXAMPLE DATA (SHOULD READ FORWARDS) ===\")\n print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n print()\n \n@@ -33,7 +33,7 @@ def main():\n \n def tokenize(element):\n outputs = tokenizer(\n- element[\"text\"],\n+ [t[-1] for t in element[\"text\"]],\n truncation=True,\n max_length=context_length,\n # padding=\"max_length\",\n@@ -62,7 +62,9 @@ def main():\n desc=f\"Tokenizing {split}...\",\n )\n \n- print(f\"Sample data: {tokenizer.decode(tokenized_datasets['train'].take(0)['input_ids'])[:100]}\")\n+ print(\"=== SAMPLE DATA (SHOULD READ BACKWARDS) ===\")\n+ print(f\"{tokenizer.decode(tokenized_datasets['train'].take(0)['input_ids'])[:100]}\")\n+ print(tokenized_datasets[\"train\"])\n \n # Takes a hot minute to save\n # 30min for 2M fineweb examples\ndiff --git a/train/train.py b/train/train.py\nindex c04f673..9dc1623 100644\n--- a/train/train.py\n+++ b/train/train.py\n@@ -24,9 +24,9 @@ import os\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n-context_length = 4096 \n+context_length = 1024\n dataset = \"fineweb-10BT\"\n-batch_size = 5\n+batch_size = 20\n \n \n model_name = f\"reverse-model-2B-{dataset}-ctx-{context_length}-batchsize-{batch_size}\"\n@@ -172,8 +172,8 @@ trainer = Trainer(\n ### TRAINING\n torch.cuda.empty_cache()\n \n-checkpoint = f\"{MODEL_DIR}/{model_name}/checkpoint-1000\"\n-# checkpoint = None\n+# checkpoint = f\"{MODEL_DIR}/{model_name}/checkpoint-9000\"\n+checkpoint = None\n trainer.train(resume_from_checkpoint=checkpoint)\n \n ### EVALUATE\n\n\nExit code: 0","created_at":"2026-07-18T07:21:48.933729Z","finish_reason":null,"metrics":null},{"message_id":"26ff5995-2e3b-4c13-8845-eda0b7f6d8b9","node_id":58,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:19","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show cdaf3a6 --patch | head -450","timeout":10000}},{"id":"functions.exec:20","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 36da088 --patch | head -350","timeout":10000}},{"id":"functions.exec:21","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git diff 2d3c969..36da088 -- train/train.py tokenize/tokenize_data.py | head -400","timeout":10000}}],"thinking":"I have a lot of information now. Let me also check the cdaf3a6 commit to understand the architecture shift from Llama to GPT2.","created_at":"2026-07-18T07:21:50.132230Z","finish_reason":"tool_calls","metrics":{"ttft_ms":973,"total_time_ms":1197,"input_tokens":6027,"output_tokens":188,"cache_read_tokens":47104,"cache_creation_tokens":null,"tpot_ms":1.1914893617021276,"tokens_per_sec":839.2857142857143}},{"message_id":"a23b1beb-1f72-4549-8dc8-75718e2f13e0","node_id":59,"role":"tool","content":"Output from command in shell 841ef6:\ncommit cdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf\nAuthor: William Y. Feng \nDate: Sat Aug 16 14:50:40 2025 -0400\n\n Organize for training\n\ndiff --git a/load_data/load_data.py b/load_data/load_data.py\nindex 0bc40ff..65f6480 100644\n--- a/load_data/load_data.py\n+++ b/load_data/load_data.py\n@@ -1,4 +1,4 @@\n-from datasets import DatasetDict, Dataset, load_dataset\n+from datasets import DatasetDict, load_dataset\n import time\n import os\n \n@@ -15,36 +15,33 @@ TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n \n ### CONFIG ###\n \n-if __name__ == \"__main__\":\n+def main():\n print(f\"Downloading dataset {dataset_name}...\")\n time.sleep(1)\n \n- # raw_dataset = load_dataset(\n- # \"HuggingFaceFW/fineweb-edu\",\n- # split=\"train\",\n- # name=\"sample-10BT\",\n- # cache_dir=\"/mnt/william/.cache\",\n- # )\n+ raw_dataset = load_dataset(\n+ \"HuggingFaceFW/fineweb-edu\",\n+ split=\"train\",\n+ name=\"sample-10BT\",\n+ cache_dir=\"/mnt/william/.cache\",\n+ )\n \n print(\"Generating split datasets...\")\n- # split_datasets = raw_dataset.train_test_split(test_size=0.005, seed=0)\n- # split_datasets = DatasetDict({\n- # \"train\": split_datasets[\"train\"],\n- # \"valid\": split_datasets[\"test\"],\n- # })\n-\n+ split_datasets = raw_dataset.train_test_split(test_size=0.005, seed=0)\n split_datasets = DatasetDict({\n- \"train\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\"),\n- \"valid\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/valid\"),\n+ \"train\": split_datasets[\"train\"],\n+ \"valid\": split_datasets[\"test\"],\n })\n \n- print(\"=== SAMPLE DATA ===\")\n+ print(\"=== SAMPLE DATA (REVERSED) ===\")\n print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n print()\n \n for split in split_datasets:\n- print(f\"Saving {split} to disk...\")\n- split_datasets[split].map(\n+ save_path = f\"{DATA_DIR}/{dataset_name}/{split}\"\n+ split_datasets[split].select_columns([\"text\"]).map(\n lambda x: { \"text\": x[\"text\"][::-1] },\n remove_columns=[\"text\"],\n- ).to_parquet(f\"{DATA_DIR}/{dataset_name}/{split}_10k_batch.parquet\", batch_size=10_000)\n+ ).save_to_disk(save_path)\n+\n+main()\ndiff --git a/modal_runner.py b/modal_runner.py\nindex 914ccd4..59eef31 100644\n--- a/modal_runner.py\n+++ b/modal_runner.py\n@@ -12,7 +12,10 @@ sys.stderr.isatty = lambda: True\n \n image = (\n modal.Image.debian_slim()\n- .uv_pip_install([\"datasets<4.0.0\", \"tokenizers\", \"tqdm\", \"numpy\", \"torch\", \"accelerate\"])\n+ .uv_pip_install([\n+ \"datasets<4.0.0\", \"tokenizers\", \"tqdm\", \"numpy\",\n+ \"torch\", \"accelerate\", \"transformers\", \"wandb\",\n+ ])\n .add_local_dir(os.path.dirname(__file__), \"/root\", copy=True)\n )\n \n@@ -23,14 +26,16 @@ volumes = {\n app = modal.App(image=image, volumes=volumes)\n \n @app.function(\n- cpu=2,\n- memory=512,\n+ cpu=8,\n+ # memory=512,\n+ memory=1024*84,\n volumes=volumes,\n timeout=60*60*24,\n )\n def load_data():\n print(\"Starting load_data.py...\")\n- subprocess.run([\"python\", \"load_data/load_data.py\"])\n+ exec(open(\"load_data/load_data.py\").read(), globals())\n+ volumes[\"/mnt/william\"].commit()\n \n @app.function(\n cpu=10,\n@@ -43,3 +48,38 @@ def train_tokenizer():\n print(\"Starting train_tokenizer.py...\")\n # 7 min to load data, ~20 min to train tokenizer\n exec(open(\"tokenize/train_tokenizer.py\").read(), globals())\n+ volumes[\"/mnt/william\"].commit()\n+\n+\n+@app.function(\n+ cpu=24,\n+ memory=1024*4,\n+ volumes=volumes,\n+ timeout=60*60*24,\n+)\n+def tokenize_data():\n+ print(\"Starting tokenize_data.py...\")\n+ exec(open(\"tokenize/tokenize_data.py\").read(), globals())\n+ volumes[\"/mnt/william\"].commit()\n+\n+\n+n_gpus = 8\n+@app.function(\n+ cpu=10,\n+ gpu=f\"h200:{n_gpus}\",\n+ memory=1024*48,\n+ volumes=volumes,\n+ timeout=60*60*24,\n+ secrets=[modal.Secret.from_name(\"william-wandb-key\")]\n+)\n+def train_model():\n+ print(\"Starting train_model.py...\")\n+ subprocess.run([\n+ \"accelerate\", \"launch\",\n+ \"--num_processes\", str(n_gpus),\n+ \"--num_machines\", \"1\",\n+ \"--mixed_precision\", \"no\",\n+ \"--dynamo_backend\", \"no\",\n+ \"train/train.py\",\n+ ])\n+ volumes[\"/mnt/william\"].commit()\ndiff --git a/train/train_dclm.ipynb b/notebooks/train_dclm.ipynb\nsimilarity index 100%\nrename from train/train_dclm.ipynb\nrename to notebooks/train_dclm.ipynb\ndiff --git a/train/train_fineweb.ipynb b/notebooks/train_fineweb.ipynb\nsimilarity index 100%\nrename from train/train_fineweb.ipynb\nrename to notebooks/train_fineweb.ipynb\ndiff --git a/tokenize/tokenize_data.py b/tokenize/tokenize_data.py\nnew file mode 100644\nindex 0000000..fa0db9e\n--- /dev/null\n+++ b/tokenize/tokenize_data.py\n@@ -0,0 +1,77 @@\n+from datasets import Dataset, DatasetDict\n+\n+import os\n+os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n+os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n+\n+context_length = 4096\n+\n+DATA_DIR = \"/mnt/william/reverse-llm/data\"\n+TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n+\n+dataset_name = \"fineweb-10BT\"\n+\n+def main():\n+ split_datasets = DatasetDict({\n+ \"train\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\"),\n+ \"valid\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/valid\"),\n+ })\n+ # split_datasets = DatasetDict({\n+ # \"train\": Dataset.from_parquet(f\"{DATA_DIR}/{dataset_name}/train.parquet\"),\n+ # \"valid\": Dataset.from_parquet(f\"{DATA_DIR}/{dataset_name}/valid.parquet\"),\n+ # })\n+\n+ print(split_datasets)\n+\n+ print(\"=== BEGIN EXAMPLE DATA (REVERSED) ===\")\n+ print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n+ print()\n+\n+ # Load pretrained tokenizer\n+ from transformers import PreTrainedTokenizerFast\n+ tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+\n+ # USES CONTEXT LENGTH\n+ print(f\"Context length: {context_length}\")\n+ print(split_datasets)\n+\n+ def tokenize(element):\n+ outputs = tokenizer(\n+ element[\"text\"],\n+ truncation=True,\n+ max_length=context_length,\n+ # padding=\"max_length\",\n+ return_overflowing_tokens=False,\n+ # return_overflowing_tokens=True,\n+ return_length=True,\n+ )\n+ input_batch = []\n+ for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n+ input_batch.append(input_ids)\n+ return {\"input_ids\": input_batch}\n+\n+ # 25s to parse 1k examples\n+ # 4m 40s to parse 10k examples\n+ # 7m 50s to parse 200k examples\n+ tokenized_dataset_train = split_datasets[\"train\"].map(\n+ tokenize, batched=True, remove_columns=[\"text\"])\n+ tokenized_dataset_valid = split_datasets[\"valid\"].map(\n+ tokenize, batched=True, remove_columns=[\"text\"])\n+\n+\n+ # Takes a hot minute to save\n+ # 30min for 2M fineweb examples\n+ # 3m for 1/9 of that\n+ print(\"Saving training dataset...\")\n+ tokenized_dataset_train.to_parquet(\n+ f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_train.parquet\")\n+ print(\"Saving valid dataset...\")\n+ tokenized_dataset_valid.to_parquet(\n+ f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_valid.parquet\")\n+\n+\n+ print(tokenized_dataset_train)\n+ print(f\"Produced dataset of {tokenized_dataset_train.num_rows:,} rows, {context_length} tokens each\")\n+ print(f\"Total tokens: {tokenized_dataset_train.num_rows * context_length:,}\")\n+\n+main()\ndiff --git a/tokenize/train_tokenizer.py b/tokenize/train_tokenizer.py\nindex b854621..61b18df 100644\n--- a/tokenize/train_tokenizer.py\n+++ b/tokenize/train_tokenizer.py\n@@ -21,6 +21,7 @@ def main():\n from datasets import Dataset\n \n # Dataset is too big to fit into memory so we stream\n+ # train_dataset = Dataset.from_parquet(f\"{DATA_DIR}/{dataset_name}/train.parquet\")\n train_dataset = Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\")\n \n # Train tokenizer (7.4s on 1k examples)\n@@ -59,9 +60,9 @@ def main():\n pad_token=\"\", # Used for padding shorter sequences\n )\n \n- tokenizer_path = f\"{TOKENIZER_DIR}/{dataset_name}\"\n+ tokenizer_path = f\"{TOKENIZER_DIR}/fineweb_spm_200k\"\n print(f\"Saving tokenizer to {tokenizer_path}\")\n os.makedirs(TOKENIZER_DIR, exist_ok=True)\n- tokenizer.save(tokenizer_path)\n+ tokenizer.save_pretrained(tokenizer_path)\n \n main()\ndiff --git a/train/.gitignore b/train/.gitignore\ndeleted file mode 100644\nindex 691fdcc..0000000\n--- a/train/.gitignore\n+++ /dev/null\n@@ -1,2 +0,0 @@\n-wandb/\n-runs/\ndiff --git a/train/tokenize.sh b/train/tokenize.sh\ndeleted file mode 100644\nindex 3f96985..0000000\n--- a/train/tokenize.sh\n+++ /dev/null\n@@ -1,11 +0,0 @@\n-#!/bin/bash\n-\n-#SBATCH -p mit_normal_gpu\n-#SBATCH --mincpus 32\n-#SBATCH --mem 64000\n-#SBATCH --output runs/tokenize.log\n-#SBATCH --time 360 \n-\n-#/home/wyf/.conda/envs/torch/bin/accelerate launch train_fineweb.py\n-/home/wyf/.conda/envs/torch/bin/python tokenize_data.py\n-\ndiff --git a/train/tokenize_data.py b/train/tokenize_data.py\ndeleted file mode 100644\nindex 5f7f2ba..0000000\n--- a/train/tokenize_data.py\n+++ /dev/null\n@@ -1,68 +0,0 @@\n-from datasets import Dataset, DatasetDict\n-\n-import sys\n-\n-import os\n-os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n-os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n-\n-n_samples = 10_000_000_000\n-context_length = 4096\n-\n-# DATA_DIR = \"/home/wyf/ai/causal-llm/data\"\n-DATA_DIR = \"/home/wyf/orcd/pool/causal-llm/data\"\n-TOKENIZER_DIR = \"/home/wyf/ai/causal-llm/tokenizers\"\n-MODEL_DIR = \"/home/wyf/ai/causal-llm/models\"\n-\n-dataset = \"fineweb-10BT\"\n-model_name = f\"reverse-{dataset}-ctx-{context_length}-2B\"\n-\n-split_datasets = DatasetDict({\n- \"train\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset}/train\"),\n- \"valid\": Dataset.load_from_disk(f\"{DATA_DIR}/{dataset}/val\")\n-})\n-\n-# Load pretrained tokenizer\n-from transformers import PreTrainedTokenizerFast\n-tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_1M\")\n-\n-# USES CONTEXT LENGTH\n-print(f\"Context length: {context_length}\")\n-\n-def tokenize(element):\n- outputs = tokenizer(\n- element[\"text\"],\n- truncation=True,\n- max_length=context_length,\n- return_overflowing_tokens=True,\n- return_length=True,\n- )\n- input_batch = []\n- for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n- if length == context_length:\n- input_batch.append(input_ids)\n- return {\"input_ids\": input_batch}\n-\n-# 25s to parse 1k examples\n-# 4m 40s to parse 10k examples\n-# 7m 50s to parse 200k examples\n-tokenized_dataset_train = split_datasets[\"train\"].map(\n- tokenize, batched=True, remove_columns=[\"text\"], batch_size=32)\n-tokenized_dataset_valid = split_datasets[\"valid\"].map(\n- tokenize, batched=True, remove_columns=[\"text\"], batch_size=32)\n-\n-\n-# Takes a hot minute to save\n-# 30min for 2M fineweb examples\n-# 3m for 1/9 of that\n-print(\"Saving training dataset...\")\n-tokenized_dataset_train.to_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_train.parquet\")\n-print(\"Saving valid dataset...\")\n-tokenized_dataset_valid.to_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_valid.parquet\")\n-\n-\n-print(tokenized_dataset_train)\n-print(f\"Produced dataset of {tokenized_dataset_train.num_rows:,} rows, {context_length} tokens each\")\n-print(f\"Total tokens: {tokenized_dataset_train.num_rows * context_length:,}\")\ndiff --git a/train/train.py b/train/train.py\nnew file mode 100644\nindex 0000000..c04f673\n--- /dev/null\n+++ b/train/train.py\n@@ -0,0 +1,198 @@\n+### THINGS TO CHANGE IN THIS FILE (fix to not hardcode later)\n+# - model name (be descriptive)\n+# - wandb config values for logging\n+# - checkpoint\n+\n+\n+### ACCELERATE CONFIG\n+\n+from accelerate import Accelerator\n+import os\n+import math\n+\n+accelerator = Accelerator()\n+\n+if not accelerator.is_main_process:\n+ os.environ[\"WANDB_MODE\"] = \"disabled\"\n+ print = lambda *args: None\n+\n+\n+### IMPORTS\n+from datasets import Dataset\n+\n+import os\n+os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n+os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n+\n+context_length = 4096 \n+dataset = \"fineweb-10BT\"\n+batch_size = 5\n+\n+\n+model_name = f\"reverse-model-2B-{dataset}-ctx-{context_length}-batchsize-{batch_size}\"\n+\n+DATA_DIR = \"/mnt/william/reverse-llm/data\"\n+TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n+MODEL_DIR = \"/mnt/william/reverse-llm/models\"\n+\n+\n+### LOAD TOKENIZER\n+from transformers import PreTrainedTokenizerFast\n+tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+\n+\n+### LOAD TOKENIZED DATASETS\n+tokenized_datasets = {}\n+for split in [\"train\", \"valid\"]:\n+ tokenized_datasets[split] = (\n+ Dataset.from_parquet(\n+ f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}.parquet\")\n+ .select_columns([\"input_ids\"])\n+ )\n+\n+print(f\"Dataset size: {tokenized_datasets['train'].num_rows * context_length:,} tokens\")\n+\n+print(tokenized_datasets['train'])\n+print(f\"Produced dataset of {tokenized_datasets['train'].num_rows:,} rows, {context_length} tokens each\")\n+print(f\"Total tokens: {tokenized_datasets['train'].num_rows * context_length:,}\")\n+\n+print(f\"Tokenizer vocab size: {len(tokenizer)}\")\n+print(f\"Model config vocab size: {tokenizer.vocab_size}\")\n+print(f\"BOS token ID: {tokenizer.bos_token_id}\")\n+print(f\"EOS token ID: {tokenizer.eos_token_id}\")\n+print(f\"PAD token ID: {tokenizer.pad_token_id}\")\n+\n+\n+# Check sample data\n+print(f\"=== SAMPLE DATA ===\")\n+print(tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100])\n+\n+\n+### INITIALIZE MODEL\n+\n+# 3.2s to initialize model\n+from transformers import LlamaConfig, LlamaForCausalLM\n+import torch\n+\n+model_size = \"2B\"\n+\n+config = LlamaConfig(\n+ vocab_size=len(tokenizer),\n+ max_position_embeddings=8192,\n+ hidden_size=2048 if model_size == \"2B\" else 3072,\n+ intermediate_size=16384 if model_size == \"2B\" else 24576,\n+ num_hidden_layers=18 if model_size == \"2B\" else 28,\n+ num_attention_heads=8 if model_size == \"2B\" else 16,\n+ num_key_value_heads=1 if model_size == \"2B\" else 16,\n… (433 lines truncated)\n\nExit code: 0\n\n`| head -450` was parsed out (450 of 883 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/955f7efa/content.txt\n","created_at":"2026-07-18T07:21:50.215148Z","finish_reason":null,"metrics":null},{"message_id":"e214d645-8eac-479a-ae2a-784bf57eabf1","node_id":60,"role":"tool","content":"Output from command in shell 8a6ad2:\ncommit 36da088be82ef9446ee73f2b540fd9998d59309f\nAuthor: William Y. Feng \nDate: Sun Aug 17 22:01:51 2025 -0400\n\n Tighten up gpt2 architecture\n\ndiff --git a/.gitignore b/.gitignore\nindex fa8c2ae..3ba6566 100644\n--- a/.gitignore\n+++ b/.gitignore\n@@ -4,3 +4,7 @@ data/\n reverse-model*/\n tokenizers/\n *.log\n+\n+train/wandb/\n+\n+train/runs_*/\ndiff --git a/tokenize/tokenize_data.py b/tokenize/tokenize_data.py\nindex 3889b8f..81e6afd 100644\n--- a/tokenize/tokenize_data.py\n+++ b/tokenize/tokenize_data.py\n@@ -6,8 +6,8 @@ os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n context_length = 1024\n \n-DATA_DIR = \"/mnt/william/reverse-llm/data\"\n-TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n+DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n+TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n \n dataset_name = \"fineweb-10BT\"\n \n@@ -20,50 +20,65 @@ def main():\n print(split_datasets)\n \n print(\"=== BEGIN EXAMPLE DATA (SHOULD READ FORWARDS) ===\")\n- print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n+ print(list(split_datasets[\"train\"].take(1))[0][\"text\"][:100])\n print()\n \n # Load pretrained tokenizer\n from transformers import PreTrainedTokenizerFast\n- tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+ tokenizer = PreTrainedTokenizerFast(\n+ tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n+ bos_token=\"\",\n+ eos_token=\"\",\n+ unk_token=\"\",\n+ pad_token=\"\",\n+ mask_token=\"\",\n+ )\n+ print(tokenizer.special_tokens_map)\n \n # USES CONTEXT LENGTH\n print(f\"Context length: {context_length}\")\n print(split_datasets)\n \n- def tokenize(element):\n- outputs = tokenizer(\n- [t[-1] for t in element[\"text\"]],\n- truncation=True,\n- max_length=context_length,\n- # padding=\"max_length\",\n- return_overflowing_tokens=False,\n- # return_overflowing_tokens=True,\n- return_length=True,\n- )\n- input_batch = []\n- for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n- input_batch.append(input_ids)\n- return {\"input_ids\": input_batch}\n-\n- # 25s to parse 1k examples\n- # 4m 40s to parse 10k examples\n- # 7m 50s to parse 200k examples\n+ def concatenate_and_chunk(element):\n+ all_token_ids = []\n+ for text in element[\"text\"]:\n+ token_ids = tokenizer.encode(text[::-1], add_special_tokens=False)\n+ all_token_ids.extend(token_ids)\n+ all_token_ids.append(tokenizer.eos_token_id)\n+\n+ total_length = len(all_token_ids)\n+\n+ if total_length < context_length:\n+ return { \"input_ids\": [] }\n+\n+ total_length = (total_length // context_length) * context_length\n+\n+ # Split into chunks\n+ input_ids = [\n+ all_token_ids[i : i + context_length]\n+ for i in range(0, total_length, context_length)\n+ ]\n+\n+ return { \"input_ids\": input_ids }\n+\n tokenized_datasets = {}\n for split in [\"train\", \"valid\"]:\n tokenized_datasets[split] = split_datasets[split] \\\n .select_columns(\"text\") \\\n .map(\n- tokenize,\n+ concatenate_and_chunk,\n batched=True,\n remove_columns=[\"text\"],\n batch_size=1_000,\n- num_proc=os.cpu_count(),\n+ num_proc=(os.cpu_count() - 1),\n desc=f\"Tokenizing {split}...\",\n )\n \n+ print(type(tokenized_datasets[\"train\"][0][\"input_ids\"]))\n+ print(tokenized_datasets[\"train\"][0][\"input_ids\"][:5])\n+\n print(\"=== SAMPLE DATA (SHOULD READ BACKWARDS) ===\")\n- print(f\"{tokenizer.decode(tokenized_datasets['train'].take(0)['input_ids'])[:100]}\")\n+ print(f\"{tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100]}\")\n print(tokenized_datasets[\"train\"])\n \n # Takes a hot minute to save\n@@ -72,7 +87,7 @@ def main():\n print(\"Saving training dataset...\")\n for split in [\"train\", \"valid\"]:\n print(f\"Saving {split} dataset...\")\n- tokenized_datasets[split].to_parquet(f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}.parquet\")\n+ tokenized_datasets[split].save_to_disk(f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}\")\n \n print(tokenized_datasets)\n print(f\"Produced dataset of {tokenized_datasets['train'].num_rows:,} rows, {context_length} tokens each\")\ndiff --git a/tokenize/tokenize_data.sh b/tokenize/tokenize_data.sh\nnew file mode 100644\nindex 0000000..72a8f41\n--- /dev/null\n+++ b/tokenize/tokenize_data.sh\n@@ -0,0 +1,9 @@\n+#!/bin/bash\n+\n+#SBATCH -p mit_normal\n+#SBATCH --mincpus 96\n+#SBATCH --mem 90000\n+#SBATCH --time 360\n+#SBATCH --output runs/tokenize_data.log\n+\n+/home/wyf/.conda/envs/torch/bin/python tokenize_data.py\ndiff --git a/tokenize/train_tokenizer.py b/tokenize/train_tokenizer.py\nindex 61b18df..a98fb63 100644\n--- a/tokenize/train_tokenizer.py\n+++ b/tokenize/train_tokenizer.py\n@@ -3,7 +3,7 @@ from tqdm import tqdm\n \n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n-os.environ[\"HF_DATASETS_CACHE\"] = \"/mnt/william/.cache\"\n+os.environ[\"HF_DATASETS_CACHE\"] = \"~/orcd/pool/.cache\"\n \n os.environ[\"PYTHONUNBUFFERED\"] = \"1\"\n \n@@ -11,8 +11,8 @@ os.environ[\"PYTHONUNBUFFERED\"] = \"1\"\n \n dataset_name = \"fineweb-10BT\"\n \n-DATA_DIR = \"/mnt/william/reverse-llm/data\"\n-TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n+DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n+TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n \n ### CONFIG ###\n def main():\n@@ -20,49 +20,38 @@ def main():\n \n from datasets import Dataset\n \n- # Dataset is too big to fit into memory so we stream\n- # train_dataset = Dataset.from_parquet(f\"{DATA_DIR}/{dataset_name}/train.parquet\")\n train_dataset = Dataset.load_from_disk(f\"{DATA_DIR}/{dataset_name}/train\")\n+ print(train_dataset)\n+ print(f\"=== SAMPLE DATA (SHOULD READ FORWARDS) ===\")\n+ print(train_dataset[0][\"text\"])\n \n- # Train tokenizer (7.4s on 1k examples)\n- # 3m 30s on 200k examples\n- # 20m on 900k examples (fineweb-10BT)\n-\n- from tokenizers import SentencePieceBPETokenizer\n+ from tokenizers import ByteLevelBPETokenizer\n \n def text_iterator():\n- fraction = 0.02\n- n_samples = int(train_dataset.num_rows * fraction)\n- print(f\"Using {n_samples:,} samples\")\n+ n_samples = 200_000\n+ print(f\"Using {n_samples:,} samples ({n_samples / len(train_dataset):.2%} of dataset)\")\n \n print(\"Shuffling dataset and selecting samples...\")\n filtered_dataset = train_dataset.shuffle(seed=0).select(range(n_samples))\n print(\"Generating text iterator...\")\n for x in tqdm(filtered_dataset[\"text\"]):\n- yield x\n+ yield x[::-1]\n \n print(\"Training tokenizer...\")\n- spm_tokenizer = SentencePieceBPETokenizer()\n- spm_tokenizer.train_from_iterator(\n+ bpe_tokenizer = ByteLevelBPETokenizer()\n+ bpe_tokenizer.train_from_iterator(\n text_iterator(),\n vocab_size=52_000,\n min_frequency=5,\n show_progress=True,\n+ special_tokens = [\"\", \"\", \"\", \"\", \"\"]\n )\n \n- from transformers import PreTrainedTokenizerFast\n-\n- tokenizer = PreTrainedTokenizerFast(\n- tokenizer_object=spm_tokenizer,\n- bos_token=\"\", # Always added at start\n- eos_token=\"\", # Always added at end \n- unk_token=\"\", # Replaces unknown words\n- pad_token=\"\", # Used for padding shorter sequences\n- )\n-\n- tokenizer_path = f\"{TOKENIZER_DIR}/fineweb_spm_200k\"\n+ tokenizer_path = f\"{TOKENIZER_DIR}/fineweb_bpe_200k\"\n print(f\"Saving tokenizer to {tokenizer_path}\")\n os.makedirs(TOKENIZER_DIR, exist_ok=True)\n- tokenizer.save_pretrained(tokenizer_path)\n+ bpe_tokenizer.save(tokenizer_path)\n+\n+ print(f\"Done.\")\n \n main()\ndiff --git a/tokenize/train_tokenizer.sh b/tokenize/train_tokenizer.sh\nnew file mode 100644\nindex 0000000..cba40d5\n--- /dev/null\n+++ b/tokenize/train_tokenizer.sh\n@@ -0,0 +1,8 @@\n+#!/bin/bash\n+\n+#SBATCH --mincpus 10\n+#SBATCH --mem 84000\n+#SBATCH --time 360\n+#SBATCH --output runs/train_tokenizer.log\n+\n+/home/wyf/.conda/envs/torch/bin/python train_tokenizer.py\ndiff --git a/train/test_h200.out b/train/test_h200.out\nnew file mode 100644\nindex 0000000..7675b25\n--- /dev/null\n+++ b/train/test_h200.out\n@@ -0,0 +1,21 @@\n+node3005\n+Sun Aug 17 21:16:28 2025 \n++-----------------------------------------------------------------------------------------+\n+| NVIDIA-SMI 550.54.14 Driver Version: 550.54.14 CUDA Version: 12.4 |\n+|-----------------------------------------+------------------------+----------------------+\n+| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n+| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n+| | | MIG M. |\n+|=========================================+========================+======================|\n+| 0 NVIDIA L40S On | 00000000:CA:00.0 Off | 0 |\n+| N/A 29C P8 32W / 350W | 0MiB / 46068MiB | 0% Default |\n+| | | N/A |\n++-----------------------------------------+------------------------+----------------------+\n+ \n++-----------------------------------------------------------------------------------------+\n+| Processes: |\n+| GPU GI CI PID Type Process name GPU Memory |\n+| ID ID Usage |\n+|=========================================================================================|\n+| No running processes found |\n++-----------------------------------------------------------------------------------------+\ndiff --git a/train/train.py b/train/train.py\nindex 9dc1623..dfd2386 100644\n--- a/train/train.py\n+++ b/train/train.py\n@@ -1,14 +1,9 @@\n-### THINGS TO CHANGE IN THIS FILE (fix to not hardcode later)\n-# - model name (be descriptive)\n-# - wandb config values for logging\n-# - checkpoint\n-\n-\n ### ACCELERATE CONFIG\n \n from accelerate import Accelerator\n import os\n import math\n+import torch\n \n accelerator = Accelerator()\n \n@@ -16,6 +11,7 @@ if not accelerator.is_main_process:\n os.environ[\"WANDB_MODE\"] = \"disabled\"\n print = lambda *args: None\n \n+n_gpus = accelerator.num_processes\n \n ### IMPORTS\n from datasets import Dataset\n@@ -26,27 +22,31 @@ os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n context_length = 1024\n dataset = \"fineweb-10BT\"\n-batch_size = 20\n-\n \n-model_name = f\"reverse-model-2B-{dataset}-ctx-{context_length}-batchsize-{batch_size}\"\n+model_name = f\"reverse-gpt2-0.35B-{dataset}-ctx-{context_length}\"\n \n-DATA_DIR = \"/mnt/william/reverse-llm/data\"\n-TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n-MODEL_DIR = \"/mnt/william/reverse-llm/models\"\n+DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n+TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n+MODEL_DIR = \"/home/wyf/orcd/pool/reverse-llm/models\"\n \n \n ### LOAD TOKENIZER\n from transformers import PreTrainedTokenizerFast\n-tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+tokenizer = PreTrainedTokenizerFast(\n+ tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n+ bos_token=\"\",\n+ eos_token=\"\",\n+ unk_token=\"\",\n+ pad_token=\"\",\n+ mask_token=\"\",\n+)\n \n \n ### LOAD TOKENIZED DATASETS\n tokenized_datasets = {}\n for split in [\"train\", \"valid\"]:\n tokenized_datasets[split] = (\n- Dataset.from_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}.parquet\")\n+ Dataset.load_from_disk(f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}\")\n .select_columns([\"input_ids\"])\n )\n \n@@ -69,32 +69,25 @@ print(tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100])\n \n \n ### INITIALIZE MODEL\n-\n-# 3.2s to initialize model\n… (138 lines truncated)\n\nExit code: 0\n\n`| head -350` was parsed out (350 of 488 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/b7f5c762/content.txt\n","created_at":"2026-07-18T07:21:50.215170Z","finish_reason":null,"metrics":null},{"message_id":"ba9ae00d-dfba-414c-81bc-0c977391c7f7","node_id":61,"role":"tool","content":"Output from command in shell dbd7f7:\ndiff --git a/tokenize/tokenize_data.py b/tokenize/tokenize_data.py\nindex 3889b8f..81e6afd 100644\n--- a/tokenize/tokenize_data.py\n+++ b/tokenize/tokenize_data.py\n@@ -6,8 +6,8 @@ os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n context_length = 1024\n \n-DATA_DIR = \"/mnt/william/reverse-llm/data\"\n-TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n+DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n+TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n \n dataset_name = \"fineweb-10BT\"\n \n@@ -20,50 +20,65 @@ def main():\n print(split_datasets)\n \n print(\"=== BEGIN EXAMPLE DATA (SHOULD READ FORWARDS) ===\")\n- print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n+ print(list(split_datasets[\"train\"].take(1))[0][\"text\"][:100])\n print()\n \n # Load pretrained tokenizer\n from transformers import PreTrainedTokenizerFast\n- tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+ tokenizer = PreTrainedTokenizerFast(\n+ tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n+ bos_token=\"\",\n+ eos_token=\"\",\n+ unk_token=\"\",\n+ pad_token=\"\",\n+ mask_token=\"\",\n+ )\n+ print(tokenizer.special_tokens_map)\n \n # USES CONTEXT LENGTH\n print(f\"Context length: {context_length}\")\n print(split_datasets)\n \n- def tokenize(element):\n- outputs = tokenizer(\n- [t[-1] for t in element[\"text\"]],\n- truncation=True,\n- max_length=context_length,\n- # padding=\"max_length\",\n- return_overflowing_tokens=False,\n- # return_overflowing_tokens=True,\n- return_length=True,\n- )\n- input_batch = []\n- for length, input_ids in zip(outputs[\"length\"], outputs[\"input_ids\"]):\n- input_batch.append(input_ids)\n- return {\"input_ids\": input_batch}\n-\n- # 25s to parse 1k examples\n- # 4m 40s to parse 10k examples\n- # 7m 50s to parse 200k examples\n+ def concatenate_and_chunk(element):\n+ all_token_ids = []\n+ for text in element[\"text\"]:\n+ token_ids = tokenizer.encode(text[::-1], add_special_tokens=False)\n+ all_token_ids.extend(token_ids)\n+ all_token_ids.append(tokenizer.eos_token_id)\n+\n+ total_length = len(all_token_ids)\n+\n+ if total_length < context_length:\n+ return { \"input_ids\": [] }\n+\n+ total_length = (total_length // context_length) * context_length\n+\n+ # Split into chunks\n+ input_ids = [\n+ all_token_ids[i : i + context_length]\n+ for i in range(0, total_length, context_length)\n+ ]\n+\n+ return { \"input_ids\": input_ids }\n+\n tokenized_datasets = {}\n for split in [\"train\", \"valid\"]:\n tokenized_datasets[split] = split_datasets[split] \\\n .select_columns(\"text\") \\\n .map(\n- tokenize,\n+ concatenate_and_chunk,\n batched=True,\n remove_columns=[\"text\"],\n batch_size=1_000,\n- num_proc=os.cpu_count(),\n+ num_proc=(os.cpu_count() - 1),\n desc=f\"Tokenizing {split}...\",\n )\n \n+ print(type(tokenized_datasets[\"train\"][0][\"input_ids\"]))\n+ print(tokenized_datasets[\"train\"][0][\"input_ids\"][:5])\n+\n print(\"=== SAMPLE DATA (SHOULD READ BACKWARDS) ===\")\n- print(f\"{tokenizer.decode(tokenized_datasets['train'].take(0)['input_ids'])[:100]}\")\n+ print(f\"{tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100]}\")\n print(tokenized_datasets[\"train\"])\n \n # Takes a hot minute to save\n@@ -72,7 +87,7 @@ def main():\n print(\"Saving training dataset...\")\n for split in [\"train\", \"valid\"]:\n print(f\"Saving {split} dataset...\")\n- tokenized_datasets[split].to_parquet(f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}.parquet\")\n+ tokenized_datasets[split].save_to_disk(f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}\")\n \n print(tokenized_datasets)\n print(f\"Produced dataset of {tokenized_datasets['train'].num_rows:,} rows, {context_length} tokens each\")\ndiff --git a/train/train.py b/train/train.py\nindex 9dc1623..dfd2386 100644\n--- a/train/train.py\n+++ b/train/train.py\n@@ -1,14 +1,9 @@\n-### THINGS TO CHANGE IN THIS FILE (fix to not hardcode later)\n-# - model name (be descriptive)\n-# - wandb config values for logging\n-# - checkpoint\n-\n-\n ### ACCELERATE CONFIG\n \n from accelerate import Accelerator\n import os\n import math\n+import torch\n \n accelerator = Accelerator()\n \n@@ -16,6 +11,7 @@ if not accelerator.is_main_process:\n os.environ[\"WANDB_MODE\"] = \"disabled\"\n print = lambda *args: None\n \n+n_gpus = accelerator.num_processes\n \n ### IMPORTS\n from datasets import Dataset\n@@ -26,27 +22,31 @@ os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n \n context_length = 1024\n dataset = \"fineweb-10BT\"\n-batch_size = 20\n-\n \n-model_name = f\"reverse-model-2B-{dataset}-ctx-{context_length}-batchsize-{batch_size}\"\n+model_name = f\"reverse-gpt2-0.35B-{dataset}-ctx-{context_length}\"\n \n-DATA_DIR = \"/mnt/william/reverse-llm/data\"\n-TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n-MODEL_DIR = \"/mnt/william/reverse-llm/models\"\n+DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n+TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n+MODEL_DIR = \"/home/wyf/orcd/pool/reverse-llm/models\"\n \n \n ### LOAD TOKENIZER\n from transformers import PreTrainedTokenizerFast\n-tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_spm_200k\")\n+tokenizer = PreTrainedTokenizerFast(\n+ tokenizer_file=f\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\",\n+ bos_token=\"\",\n+ eos_token=\"\",\n+ unk_token=\"\",\n+ pad_token=\"\",\n+ mask_token=\"\",\n+)\n \n \n ### LOAD TOKENIZED DATASETS\n tokenized_datasets = {}\n for split in [\"train\", \"valid\"]:\n tokenized_datasets[split] = (\n- Dataset.from_parquet(\n- f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}.parquet\")\n+ Dataset.load_from_disk(f\"{DATA_DIR}/{dataset}/tokenized_{context_length}_{split}\")\n .select_columns([\"input_ids\"])\n )\n \n@@ -69,32 +69,25 @@ print(tokenizer.decode(tokenized_datasets['train'][0]['input_ids'])[:100])\n \n \n ### INITIALIZE MODEL\n-\n-# 3.2s to initialize model\n-from transformers import LlamaConfig, LlamaForCausalLM\n-import torch\n-\n-model_size = \"2B\"\n-\n-config = LlamaConfig(\n- vocab_size=len(tokenizer),\n- max_position_embeddings=8192,\n- hidden_size=2048 if model_size == \"2B\" else 3072,\n- intermediate_size=16384 if model_size == \"2B\" else 24576,\n- num_hidden_layers=18 if model_size == \"2B\" else 28,\n- num_attention_heads=8 if model_size == \"2B\" else 16,\n- num_key_value_heads=1 if model_size == \"2B\" else 16,\n- rms_norm_eps=1e-5,\n- tie_word_embeddings=False,\n- rope_scaling=None,\n+from transformers import GPT2Config, GPT2LMHeadModel\n+\n+config = GPT2Config(\n+ vocab_size=tokenizer.vocab_size,\n+ n_positions=context_length,\n+ n_ctx=context_length,\n+ n_embd=1024,\n+ n_layer=24,\n+ n_head=16,\n bos_token_id=tokenizer.bos_token_id,\n eos_token_id=tokenizer.eos_token_id,\n+ pad_token_id=tokenizer.pad_token_id,\n )\n \n-model = LlamaForCausalLM(config).to(\"cuda\")\n+model = GPT2LMHeadModel(config).to(\"cuda\")\n+# model.gradient_checkpointing_enable()\n \n model_size = sum(t.numel() for t in model.parameters())\n-print(f\"Model size: {model_size/1000**3:.1f}B parameters\")\n+print(f\"Model size: {model_size/1000**3:.3f}B parameters\")\n \n \n ### SET UP DATA COLLATOR\n@@ -107,49 +100,34 @@ from transformers import Trainer, TrainingArguments\n \n args = TrainingArguments(\n output_dir=f\"{MODEL_DIR}/{model_name}\",\n- report_to=\"wandb\",\n- \n- per_device_train_batch_size=batch_size,\n- per_device_eval_batch_size=batch_size,\n- gradient_accumulation_steps=math.ceil(32 / batch_size / accelerator.num_processes),\n-\n- eval_strategy=\"steps\",\n- eval_steps=100,\n- logging_steps=1,\n- \n+ overwrite_output_dir=True,\n num_train_epochs=1,\n+ per_device_train_batch_size=64,\n+ gradient_accumulation_steps=((1<<20) // (64 * n_gpus) // context_length),\n \n- optim=\"adamw_torch\",\n- learning_rate=2e-4,\n- weight_decay=0.1,\n- adam_beta1=0.9,\n- adam_beta2=0.95,\n- adam_epsilon=1e-8,\n- \n+ learning_rate=1e-4,\n+ weight_decay=0.01,\n+ warmup_ratio=0.03,\n lr_scheduler_type=\"cosine\",\n- warmup_steps=2_000,\n \n max_grad_norm=1.0,\n- \n- bf16=True,\n- fp16=False,\n- \n- save_steps=100,\n+\n+ logging_steps=1,\n+ save_steps=500,\n save_total_limit=3,\n- save_only_model=False,\n- dataloader_num_workers=2,\n+ prediction_loss_only=True,\n+ bf16=True,\n \n- remove_unused_columns=False,\n- load_best_model_at_end=False,\n- \n- # Multi-gpu settings\n- ddp_find_unused_parameters=False,\n+ report_to=\"wandb\",\n+ run_name=model_name,\n+\n+ torch_compile=True,\n+ seed=0,\n )\n \n import wandb\n-wandb.login(key=os.environ[\"WANDB_API_KEY\"])\n wandb.init(\n- project=\"reverse-llm-training-modal\",\n+ project=\"reverse-llm-gpt2-0.35B\",\n name=model_name,\n entity=\"womogenes-team\",\n config=args.to_dict(),\n@@ -172,8 +150,12 @@ trainer = Trainer(\n ### TRAINING\n torch.cuda.empty_cache()\n \n-# checkpoint = f\"{MODEL_DIR}/{model_name}/checkpoint-9000\"\n-checkpoint = None\n+import torch\n+print(f\"VRAM allocated: {torch.cuda.memory_allocated()/1e9:.2f} GB\")\n+print(f\"VRAM max allocated: {torch.cuda.max_memory_allocated()/1e9:.2f} GB\")\n+\n+# checkpoint = None\n+checkpoint = True\n trainer.train(resume_from_checkpoint=checkpoint)\n \n ### EVALUATE\n\nExit code: 0","created_at":"2026-07-18T07:21:50.215176Z","finish_reason":null,"metrics":null},{"message_id":"f7ff0487-4cf7-4483-97db-dc859d020f5b","node_id":63,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:22","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/load_data/load_ultrachat_data.py"}},{"id":"functions.exec:23","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 6a4ece3 --patch | head -300","timeout":10000}},{"id":"functions.exec:24","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show f423b4c --patch | head -350","timeout":10000}}],"thinking":"Good. Let me also check the train_chat file and load_ultrachat_data to understand the instruct fine-tuning.","created_at":"2026-07-18T07:21:51.526757Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1105,"total_time_ms":1310,"input_tokens":10627,"output_tokens":158,"cache_read_tokens":53120,"cache_creation_tokens":null,"tpot_ms":1.2974683544303798,"tokens_per_sec":770.7317073170732}},{"message_id":"c9a5aebf-6329-40e1-b5cb-c9403c61a739","node_id":64,"role":"tool","content":"\n 1|from transformers import PreTrainedTokenizerFast\n 2|from datasets import Dataset, load_dataset\n 3|from tqdm import tqdm\n 4|import os\n 5|\n 6|DATA_DIR = \"/home/wyf/orcd/pool/reverse-llm/data\"\n 7|TOKENIZER_DIR = \"/home/wyf/orcd/pool/reverse-llm/tokenizers\"\n 8|\n 9|USER_ROLE_NAME = \"user\"[::-1]\n 10|ASSISTANT_ROLE_NAME = \"assistant\"[::-1]\n 11|\n 12|dataset_name = \"ultrachat\"\n 13|context_length = 1024\n 14|\n 15|# Load tokenizer\n 16|tokenizer = PreTrainedTokenizerFast.from_pretrained(f\"{TOKENIZER_DIR}/fineweb_bpe_200k\")\n 17|tokenizer.add_special_tokens({\"additional_special_tokens\": [\"\", \"\"]})\n 18|\n 19|tokenizer.chat_template = \"\"\"{% for message in messages -%}\n 20|{{ message['role'] }}\n 21|{{ message['content'] }}\n 22|{%- endfor -%}\n 23|{% if add_generation_prompt and messages[-1]['role'] != 'assistant' -%}\n 24|assistant\n 25|{%- endif %}\"\"\"\n 26|\n 27|# Load dataset\n 28|raw_dataset = load_dataset(\"HuggingFaceH4/ultrachat_200k\", split=[\"train_sft\", \"test_sft\"])\n 29|\n 30|def process_ultrachat_data(ds_split: Dataset):\n 31| all_convos = []\n 32| \n 33| for ex in tqdm(ds_split, desc=\"Processing conversations\"):\n 34| messages = ex[\"messages\"]\n 35| \n 36| # Filter: must start with user and end with assistant\n 37| if not (len(messages) >= 2 and \n 38| messages[0][\"role\"] == \"user\" and \n 39| messages[-1][\"role\"] == \"assistant\"):\n 40| continue\n 41| \n 42| # Filter: no user query longer than 500 characters\n 43| if any(msg[\"role\"] == \"user\" and len(msg[\"content\"]) > 500 for msg in messages):\n 44| continue\n 45| \n 46| # Reverse content and role names, pre-tokenize each message\n 47| tokenized_messages = []\n 48| for msg in messages:\n 49| role = USER_ROLE_NAME if msg[\"role\"] == \"user\" else ASSISTANT_ROLE_NAME\n 50| content = msg[\"content\"].strip()[::-1]\n 51| \n 52| # Pre-tokenize the formatted message\n 53| formatted_msg = f\"{role}\\n{content}\"\n 54| tokens = tokenizer.encode(formatted_msg, add_special_tokens=False)\n 55| \n 56| tokenized_messages.append({\n 57| \"role\": role,\n 58| \"content\": content,\n 59| \"tokens\": tokens,\n 60| \"token_count\": len(tokens)\n 61| })\n 62| \n 63| # Create sliding windows\n 64| windows = create_sliding_windows(tokenized_messages, context_length)\n 65| all_convos.extend(windows)\n 66| \n 67| return all_convos\n 68|\n 69|def create_sliding_windows(tokenized_messages, max_len):\n 70| windows = []\n 71| start_idx = 0\n 72| \n 73| while start_idx < len(tokenized_messages):\n 74| window = []\n 75| current_tokens = 0\n 76| i = start_idx\n 77| \n 78| # Build window ensuring it starts with user and ends with assistant\n 79| while i < len(tokenized_messages):\n 80| msg = tokenized_messages[i]\n 81| \n 82| # Check if adding this message exceeds limit\n 83| if current_tokens + msg[\"token_count\"] > max_len:\n 84| # If window is empty or doesn't end with assistant, try to find valid ending\n 85| if not window:\n 86| i += 1\n 87| start_idx = i\n 88| break\n 89| elif window[-1][\"role\"] != ASSISTANT_ROLE_NAME:\n 90| # Remove messages from end until we have assistant response\n 91| while window and window[-1][\"role\"] != ASSISTANT_ROLE_NAME:\n 92| removed = window.pop()\n 93| current_tokens -= removed[\"token_count\"]\n 94| break\n 95| \n 96| window.append(msg)\n 97| current_tokens += msg[\"token_count\"]\n 98| i += 1\n 99| \n100| # Only add windows that start with user and end with assistant\n101| if (window and len(window) >= 2 and \n102| window[0][\"role\"] == USER_ROLE_NAME and \n103| window[-1][\"role\"] == ASSISTANT_ROLE_NAME):\n104| \n105| formatted_window = [{\"role\": m[\"role\"], \"content\": m[\"content\"]} for m in window]\n106| windows.append(formatted_window)\n107| \n108| # Start next window from halfway through current window\n109| if len(window) >= 4:\n110| start_idx = start_idx + len(window) // 2\n111| else:\n112| start_idx = i\n113| else:\n114| start_idx = max(start_idx + 1, i)\n115| \n116| # Prevent infinite loops\n117| if start_idx >= len(tokenized_messages):\n118| break\n119| \n120| return windows\n121|\n122|def formatting_func_batch(examples):\n123| batch_input_ids = []\n124| batch_attention_mask = []\n125| batch_labels = []\n126| \n127| im_start_token_id = 52000\n128| im_end_token_id = 52001\n129| \n130| for conversation in examples[\"conversations\"]:\n131| prompt_text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=False)\n132| \n133| tokenized_inputs = tokenizer(\n134| prompt_text,\n135| truncation=True,\n136| max_length=context_length,\n137| return_attention_mask=True,\n138| padding=\"max_length\",\n139| )\n140| input_ids = tokenized_inputs[\"input_ids\"]\n141| labels = [-100] * len(input_ids)\n142| \n143| # Find and unmask all assistant responses\n144| current_token_idx = 0\n145| for turn in conversation:\n146| role = turn[\"role\"]\n147| \n148| try:\n149| start_of_turn_bos_idx = input_ids.index(im_start_token_id, current_token_idx)\n150| except ValueError:\n151| break\n152| \n153| try:\n154| end_of_turn_eos_idx = input_ids.index(im_end_token_id, start_of_turn_bos_idx + 1)\n155| except ValueError:\n156| break\n157| \n158| if role == ASSISTANT_ROLE_NAME:\n159| role_and_newline_text = f\"{role}\\n\"\n160| role_and_newline_tokens = tokenizer.encode(role_and_newline_text, add_special_tokens=False)\n161| start_of_content_idx = start_of_turn_bos_idx + 1 + len(role_and_newline_tokens)\n162| \n163| # Unmask assistant response including token\n164| for k_label in range(start_of_content_idx, end_of_turn_eos_idx + 1):\n165| if 0 <= k_label < len(labels):\n166| labels[k_label] = input_ids[k_label]\n167| \n168| current_token_idx = end_of_turn_eos_idx + 1\n169| \n170| batch_input_ids.append(input_ids)\n171| batch_attention_mask.append(tokenized_inputs[\"attention_mask\"])\n172| batch_labels.append(labels)\n173| \n174| return {\n175| \"input_ids\": batch_input_ids,\n176| \"attention_mask\": batch_attention_mask,\n177| \"labels\": batch_labels,\n178| }\n179|\n180|# Process datasets\n181|print(\"Processing train split...\")\n182|train_convos = process_ultrachat_data(raw_dataset[0])\n183|print(\"Processing valid split...\")\n184|valid_convos = process_ultrachat_data(raw_dataset[1])\n185|\n186|print(f\"Train conversations: {len(train_convos)}\")\n187|print(f\"Valid conversations: {len(valid_convos)}\")\n188|\n189|# Create datasets and tokenize in batches\n190|print(\"Creating and tokenizing datasets...\")\n191|train_tokens = 0\n192|valid_tokens = 0\n193|\n194|for split, convos in [(\"train\", train_convos), (\"valid\", valid_convos)]:\n195| dataset = Dataset.from_dict({\"conversations\": convos})\n196| \n197| # Process in batches for efficiency\n198| tokenized_split = dataset.map(\n199| formatting_func_batch,\n200| batched=True,\n201| batch_size=1000,\n202| remove_columns=[\"conversations\"],\n203| desc=f\"Tokenizing {split}\"\n204| )\n205| \n206| # Count total tokens\n207| total_tokens = sum(sum(1 for token in seq if token != -100) for seq in tokenized_split[\"labels\"])\n208| if split == \"train\":\n209| train_tokens = total_tokens\n210| else:\n211| valid_tokens = total_tokens\n212| \n213| save_path = f\"{DATA_DIR}/{dataset_name}/tokenized_{context_length}_{split}\"\n214| tokenized_split.save_to_disk(save_path)\n215| print(f\"Saved {split} to: {save_path}\")\n216|\n217|print(f\"Total train tokens: {train_tokens:,}\")\n218|print(f\"Total valid tokens: {valid_tokens:,}\")\n219|print(\"Tokenization complete!\")\n","created_at":"2026-07-18T07:21:51.615410Z","finish_reason":null,"metrics":null},{"message_id":"e36ca248-2196-42cf-a707-706bc5556b59","node_id":65,"role":"tool","content":"Output from command in shell c12d39:\ncommit 6a4ece3c43b392e4796f95d0b19f75ba45910ac1\nAuthor: William Y. Feng \nDate: Tue Aug 19 11:06:27 2025 -0400\n\n Train on alpaca successfully\n\ndiff --git a/eval/eval_alpaca.ipynb b/eval/eval_alpaca.ipynb\nnew file mode 100644\nindex 0000000..a533eea\n--- /dev/null\n+++ b/eval/eval_alpaca.ipynb\n@@ -0,0 +1,225 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 2,\n+ \"id\": \"978c1e6e\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast\\n\",\n+ \"\\n\",\n+ \"model_name = \\\"reverse-gpt2-0.35B-fineweb-10BT-ctx-1024-chat-v2\\\"\\n\",\n+ \"\\n\",\n+ \"MODEL_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/models\\\"\\n\",\n+ \"TOKENIZER_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/tokenizers\\\"\\n\",\n+ \"\\n\",\n+ \"model = GPT2LMHeadModel.from_pretrained(f\\\"{MODEL_DIR}/{model_name}/checkpoint-105\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 3,\n+ \"id\": \"28387677\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"USER_ROLE_NAME = \\\"user\\\"[::-1]\\n\",\n+ \"ASSISTANT_ROLE_NAME = \\\"assistant\\\"[::-1]\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 4,\n+ \"id\": \"42f1e1df\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"tokenizer = PreTrainedTokenizerFast.from_pretrained(f\\\"{TOKENIZER_DIR}/fineweb_bpe_200k\\\")\\n\",\n+ \"tokenizer.add_special_tokens({ \\\"additional_special_tokens\\\": [\\\"\\\", \\\"\\\"] })\\n\",\n+ \"\\n\",\n+ \"tokenizer.chat_template = \\\"\\\"\\\"{% for message in messages -%}\\n\",\n+ \"{{ message['role'] }}\\n\",\n+ \"{{ message['content'] }}\\n\",\n+ \"{%- endfor -%}\\n\",\n+ \"{% if add_generation_prompt and messages[-1]['role'] != 'assistant' -%}\\n\",\n+ \"tnatsissa{{ '\\\\n' }}\\n\",\n+ \"{%- endif %}\\\"\\\"\\\"\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 5,\n+ \"id\": \"848eddd8\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"name\": \"stderr\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"Device set to use cuda:0\\n\"\n+ ]\n+ }\n+ ],\n+ \"source\": [\n+ \"from transformers import pipeline\\n\",\n+ \"\\n\",\n+ \"pipe = pipeline(\\n\",\n+ \" \\\"text-generation\\\",\\n\",\n+ \" model=model,\\n\",\n+ \" tokenizer=tokenizer,\\n\",\n+ \" # max_new_tokens=128,\\n\",\n+ \" do_sample=True,\\n\",\n+ \" temperature=1.0,\\n\",\n+ \" num_return_sequences=1,\\n\",\n+ \" # pad_token_id=tokenizer.eos_token_id,\\n\",\n+ \" # eos_token_id=tokenizer.eos_token_id,\\n\",\n+ \" # bos_token_id=tokenizer.bos_token_id,\\n\",\n+ \" clean_up_tokenization_spaces=False,\\n\",\n+ \")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 6,\n+ \"id\": \"1d7c07ce\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"data\": {\n+ \"text/plain\": [\n+ \"{'bos_token': '',\\n\",\n+ \" 'eos_token': '',\\n\",\n+ \" 'unk_token': '',\\n\",\n+ \" 'pad_token': '',\\n\",\n+ \" 'mask_token': '',\\n\",\n+ \" 'additional_special_tokens': ['', '']}\"\n+ ]\n+ },\n+ \"execution_count\": 6,\n+ \"metadata\": {},\n+ \"output_type\": \"execute_result\"\n+ }\n+ ],\n+ \"source\": [\n+ \"tokenizer.special_tokens_map\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 7,\n+ \"id\": \"5d7981ef\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"data\": {\n+ \"text/plain\": [\n+ \"{'input_ids': [52001], 'token_type_ids': [0], 'attention_mask': [1]}\"\n+ ]\n+ },\n+ \"execution_count\": 7,\n+ \"metadata\": {},\n+ \"output_type\": \"execute_result\"\n+ }\n+ ],\n+ \"source\": [\n+ \"tokenizer(\\\"\\\")\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 30,\n+ \"id\": \"69a219ff\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"name\": \"stderr\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"Device set to use cuda:0\\n\"\n+ ]\n+ },\n+ {\n+ \"name\": \"stdout\",\n+ \"output_type\": \"stream\",\n+ \"text\": [\n+ \"Tokenized prompt: resu\\n\",\n+ \"ma itnatsissa\\n\",\n+ \"\\n\",\n+ \"=== RAW OUTPUT TEXT ===\\n\",\n+ \"resu\\n\",\n+ \"ma itnatsissa\\n\",\n+ \".suoiretsym dna suoiretsym gnihtemos htiw gnilaed m'I nehw dnim ym hserfer ot ecnahc a rof gnikool m'I\\n\",\n+ \"\\n\",\n+ \"=== QUERY ===\\n\",\n+ \"i am\\n\",\n+ \"\\n\",\n+ \"=== RESPONSE ===\\n\",\n+ \"I'm looking for a chance to refresh my mind when I'm dealing with something mysterious and mysterious.\\n\",\n+ \"\\n\"\n+ ]\n+ }\n+ ],\n+ \"source\": [\n+ \"from transformers import pipeline\\n\",\n+ \"\\n\",\n+ \"pipe = pipeline(\\n\",\n+ \" \\\"text-generation\\\",\\n\",\n+ \" model=model,\\n\",\n+ \" tokenizer=tokenizer,\\n\",\n+ \" clean_up_tokenization_spaces=False,\\n\",\n+ \")\\n\",\n+ \"\\n\",\n+ \"query = \\\"i am\\\"\\n\",\n+ \"\\n\",\n+ \"messages = [\\n\",\n+ \" {\\\"role\\\": USER_ROLE_NAME, \\\"content\\\": query[::-1]},\\n\",\n+ \"]\\n\",\n+ \"# For generation, apply template with add_generation_prompt=True\\n\",\n+ \"prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\\n\",\n+ \"\\n\",\n+ \"print(f\\\"Tokenized prompt: {prompt}\\\")\\n\",\n+ \"\\n\",\n+ \"outputs = pipe(\\n\",\n+ \" prompt,\\n\",\n+ \" num_return_sequences=1,\\n\",\n+ \" # eos_token_id=tokenizer.eos_token_id,\\n\",\n+ \" max_new_tokens=128,\\n\",\n+ \" eos_token_id=52001,\\n\",\n+ \" pad_token_id=tokenizer.pad_token_id,\\n\",\n+ \" repetition_penalty=1.1,\\n\",\n+ \" temperature=0.5,\\n\",\n+ \")\\n\",\n+ \"out_text = outputs[0][\\\"generated_text\\\"]\\n\",\n+ \"\\n\",\n+ \"print(f\\\"=== RAW OUTPUT TEXT ===\\\\n{out_text}\\\")\\n\",\n+ \"\\n\",\n+ \"response = out_text.split(\\\"tnatsissa\\\")[1]\\n\",\n+ \"\\n\",\n+ \"print(f\\\"\\\\n=== QUERY ===\\\\n{query}\\\\n\\\")\\n\",\n+ \"print(f\\\"=== RESPONSE ===\\\\n{response[::-1]}\\\")\"\n+ ]\n+ }\n+ ],\n+ \"metadata\": {\n+ \"kernelspec\": {\n+ \"display_name\": \"torch\",\n+ \"language\": \"python\",\n+ \"name\": \"python3\"\n+ },\n+ \"language_info\": {\n+ \"codemirror_mode\": {\n+ \"name\": \"ipython\",\n+ \"version\": 3\n+ },\n+ \"file_extension\": \".py\",\n+ \"mimetype\": \"text/x-python\",\n+ \"name\": \"python\",\n+ \"nbconvert_exporter\": \"python\",\n+ \"pygments_lexer\": \"ipython3\",\n+ \"version\": \"3.12.11\"\n+ }\n+ },\n+ \"nbformat\": 4,\n+ \"nbformat_minor\": 5\n+}\ndiff --git a/notebooks/load_alpaca_data.ipynb b/notebooks/load_alpaca_data.ipynb\nnew file mode 100644\nindex 0000000..30c1b3e\n--- /dev/null\n+++ b/notebooks/load_alpaca_data.ipynb\n@@ -0,0 +1,421 @@\n+{\n+ \"cells\": [\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 1,\n+ \"id\": \"f3a4884c\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from transformers import PreTrainedTokenizerFast\\n\",\n+ \"import os\\n\",\n+ \"\\n\",\n+ \"DATA_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/data\\\"\\n\",\n+ \"TOKENIZER_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/tokenizers\\\"\\n\",\n+ \"MODEL_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/models\\\"\\n\",\n+ \"\\n\",\n+ \"model_name = f\\\"reverse-gpt2-0.35B-fineweb-10BT-ctx-1024\\\"\\n\",\n+ \"\\n\",\n+ \"USER_ROLE_NAME = \\\"user\\\"[::-1]\\n\",\n+ \"ASSISTANT_ROLE_NAME = \\\"assistant\\\"[::-1]\\n\",\n+ \"\\n\",\n+ \"dataset_name = \\\"alpaca\\\"\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 2,\n+ \"id\": \"a95e8bba\",\n+ \"metadata\": {},\n+ \"outputs\": [\n+ {\n+ \"data\": {\n+ \"text/plain\": [\n+ \"2\"\n+ ]\n+ },\n+ \"execution_count\": 2,\n+ \"metadata\": {},\n+ \"output_type\": \"execute_result\"\n+ }\n+ ],\n+ \"source\": [\n+ \"tokenizer = PreTrainedTokenizerFast.from_pretrained(f\\\"{TOKENIZER_DIR}/fineweb_bpe_200k\\\")\\n\",\n+ \"tokenizer.add_special_tokens({ \\\"additional_special_tokens\\\": [\\\"\\\", \\\"\\\"] })\"\n+ ]\n+ },\n+ {\n+ \"cell_type\": \"code\",\n+ \"execution_count\": 3,\n+ \"id\": \"6ee7930a\",\n+ \"metadata\": {},\n+ \"outputs\": [],\n+ \"source\": [\n+ \"from datasets import Dataset, load_dataset\\n\",\n+ \"raw_dataset = load_dataset(\\\"tatsu-lab/alpaca\\\", split=\\\"train\\\")\\n\",\n+ \"split_datasets = raw_dataset.train_test_split(test_size=0.1, seed=0)\"\n+ ]\n… (4241 lines truncated)\n\nExit code: 0\n\n`| head -300` was parsed out (300 of 4541 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/fc82226e/content.txt\n","created_at":"2026-07-18T07:21:51.615418Z","finish_reason":null,"metrics":null},{"message_id":"e814cce6-ece0-40ed-900a-14271f2b36c9","node_id":66,"role":"tool","content":"Output from command in shell 3b0b05:\ncommit f423b4ca162bdde6caaa555ea0b7592795f62ae1\nAuthor: William Y. Feng \nDate: Mon Sep 8 16:28:11 2025 -0400\n\n Integrate ultrachat data for instruct fine-tuning\n\ndiff --git a/.gitignore b/.gitignore\nindex 3ba6566..fcc78f7 100644\n--- a/.gitignore\n+++ b/.gitignore\n@@ -4,7 +4,9 @@ data/\n reverse-model*/\n tokenizers/\n *.log\n+*.out\n \n train/wandb/\n+train_chat/wandb/\n \n train/runs_*/\ndiff --git a/.gitignore.swp b/.gitignore.swp\nnew file mode 100644\nindex 0000000..7ac0be8\nBinary files /dev/null and b/.gitignore.swp differ\ndiff --git a/eval/eval.ipynb b/eval/eval.ipynb\ndeleted file mode 100644\nindex 642bb23..0000000\n--- a/eval/eval.ipynb\n+++ /dev/null\n@@ -1,278 +0,0 @@\n-{\n- \"cells\": [\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 1,\n- \"id\": \"71dc6362\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"Available checkpoints:\\n\",\n- \"checkpoint-3500\\n\",\n- \"checkpoint-4000\\n\",\n- \"checkpoint-4500\\n\",\n- \"checkpoint-5000\\n\",\n- \"checkpoint-5500\\n\",\n- \"checkpoint-6000\\n\",\n- \"checkpoint-6500\\n\",\n- \"checkpoint-7000\\n\",\n- \"checkpoint-7500\\n\",\n- \"checkpoint-8000\\n\",\n- \"checkpoint-8500\\n\",\n- \"checkpoint-9000\\n\"\n- ]\n- }\n- ],\n- \"source\": [\n- \"# Load checkpoints\\n\",\n- \"\\n\",\n- \"MODEL_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/models\\\"\\n\",\n- \"TOKENIZER_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/tokenizers\\\"\\n\",\n- \"DATA_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/data\\\"\\n\",\n- \"\\n\",\n- \"model_name = \\\"reverse-gpt2-0.35B-fineweb-10BT-ctx-1024\\\"\\n\",\n- \"\\n\",\n- \"from transformers import PreTrainedTokenizerFast\\n\",\n- \"from transformers import GPT2LMHeadModel\\n\",\n- \"from datasets import Dataset\\n\",\n- \"import torch\\n\",\n- \"import numpy as np\\n\",\n- \"from torch.utils.data import DataLoader\\n\",\n- \"\\n\",\n- \"import os\\n\",\n- \"\\n\",\n- \"avail_checkpoints = sorted(os.listdir(f\\\"{MODEL_DIR}/{model_name}\\\"))\\n\",\n- \"print(\\\"Available checkpoints:\\\")\\n\",\n- \"print(\\\"\\\\n\\\".join(avail_checkpoints))\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 2,\n- \"id\": \"86ed3da1\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"(4614, 1)\\n\"\n- ]\n- }\n- ],\n- \"source\": [\n- \"# Load dataset\\n\",\n- \"val_dataset = Dataset.load_from_disk(f\\\"{DATA_DIR}/fineweb-10BT/tokenized_1024_valid\\\")\\n\",\n- \"print(val_dataset.shape)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 3,\n- \"id\": \"a0178255\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stderr\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \" 0%| | 0/145 [00:00 \\u001b[39m\\u001b[32m23\\u001b[39m \\u001b[38;5;28mprint\\u001b[39m(checkpoint, \\u001b[33m\\\"\\u001b[39m\\u001b[38;5;130;01m\\\\t\\u001b[39;00m\\u001b[33m\\\"\\u001b[39m, \\u001b[43mget_loss\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43mcheckpoint\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n- \"\\u001b[36mCell\\u001b[39m\\u001b[36m \\u001b[39m\\u001b[32mIn[3]\\u001b[39m\\u001b[32m, line 18\\u001b[39m, in \\u001b[36mget_loss\\u001b[39m\\u001b[34m(checkpoint)\\u001b[39m\\n\\u001b[32m 16\\u001b[39m batch = batch.to(\\u001b[33m\\\"\\u001b[39m\\u001b[33mcuda\\u001b[39m\\u001b[33m\\\"\\u001b[39m)\\n\\u001b[32m 17\\u001b[39m loss = model(batch, labels=batch).loss\\n\\u001b[32m---> \\u001b[39m\\u001b[32m18\\u001b[39m total_loss += \\u001b[43mloss\\u001b[49m\\u001b[43m.\\u001b[49m\\u001b[43mitem\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\\u001b[32m 20\\u001b[39m \\u001b[38;5;28;01mreturn\\u001b[39;00m total_loss / \\u001b[38;5;28mlen\\u001b[39m(dataloader)\\n\",\n- \"\\u001b[31mKeyboardInterrupt\\u001b[39m: \"\n- ]\n- }\n- ],\n- \"source\": [\n- \"from tqdm import tqdm\\n\",\n- \"\\n\",\n- \"def get_loss(checkpoint):\\n\",\n- \" model_dir = f\\\"{MODEL_DIR}/{model_name}/{checkpoint}\\\"\\n\",\n- \" model = GPT2LMHeadModel.from_pretrained(model_dir)\\n\",\n- \" model.to(\\\"cuda\\\")\\n\",\n- \" model.eval()\\n\",\n- \" \\n\",\n- \" # Convert to tensor once\\n\",\n- \" val_tensor = torch.tensor(val_dataset['input_ids']).long()\\n\",\n- \" dataloader = DataLoader(val_tensor, batch_size=32, shuffle=False)\\n\",\n- \" \\n\",\n- \" total_loss = 0\\n\",\n- \" with torch.no_grad():\\n\",\n- \" for batch in tqdm(dataloader):\\n\",\n- \" batch = batch.to(\\\"cuda\\\")\\n\",\n- \" loss = model(batch, labels=batch).loss\\n\",\n- \" total_loss += loss.item()\\n\",\n- \" \\n\",\n- \" return total_loss / len(dataloader)\\n\",\n- \"\\n\",\n- \"for checkpoint in avail_checkpoints:\\n\",\n- \" print(checkpoint, \\\"\\\\t\\\", get_loss(checkpoint))\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 4,\n- \"id\": \"bfea5f2a\",\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"from transformers import pipeline\\n\",\n- \"\\n\",\n- \"# Load tokenizer\\n\",\n- \"tokenizer = PreTrainedTokenizerFast(\\n\",\n- \" tokenizer_file=f\\\"{TOKENIZER_DIR}/fineweb_bpe_200k.json\\\",\\n\",\n- \" bos_token=\\\"\\\",\\n\",\n- \" eos_token=\\\"\\\",\\n\",\n- \" unk_token=\\\"\\\",\\n\",\n- \" pad_token=\\\"\\\",\\n\",\n- \" mask_token=\\\"\\\",\\n\",\n- \")\\n\",\n- \"\\n\",\n- \"# Load model\\n\",\n- \"def generate_sample(checkpoint_no, input_text, **pipe_kwargs):\\n\",\n- \" model = GPT2LMHeadModel.from_pretrained(f\\\"{MODEL_DIR}/{model_name}/checkpoint-{checkpoint_no}\\\")\\n\",\n- \"\\n\",\n- \" pipe = pipeline(\\n\",\n- \" \\\"text-generation\\\",\\n\",\n- \" model=model,\\n\",\n- \" tokenizer=tokenizer,\\n\",\n- \" clean_up_tokenization_spaces=False,\\n\",\n- \" **pipe_kwargs,\\n\",\n- \" )\\n\",\n- \" text = pipe(input_text)[0][\\\"generated_text\\\"]\\n\",\n- \" return text\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 7,\n- \"id\": \"e6cdde9b\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stderr\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"Device set to use cuda:0\\n\"\n- ]\n- },\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"that a special character does not appear in the beginning of the book.\\n\",\n- \"The character that appears in the beginning of the book shows that a special character appears in the beginning of the book. The character that appears in the beginning of the book shows that there is a special character in this book.\\n\",\n- \"The character that appears in the beginning of the book shows that there is a special character in the book. The character that appears in the beginning of the book also shows that there is a special character. It also shows that there is a special character in this book. There is a special character in the book, which can be found near the end of the book.\\n\",\n- \"* Harry Potter is a special character in this book. The character that appears in the beginning of the novel appears in this book, but it also appears in a later chapter of his life. This not only proves to have an interesting and interesting character, but it also shows that he had had some success. Harry Potter must have had some success at some time in his life. He deserves some credit for his success until near the end of his life. After all, he was proud of his claim to fame, both for life in the 1930s, and for helping to change the world.\\n\",\n- \"* Harry Potter is the author of Harry Potter.\\n\"\n- ]\n- }\n- ],\n- \"source\": [\n- \"print(generate_sample(9000, \\\"is the author of Harry Potter.\\\"[::-1])[::-1])\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 22,\n- \"id\": \"d00d2206\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"Generated 289 tokens\\n\",\n- \"EOS in output: True\\n\",\n- \"Stopped because: EOS generated\\n\"\n- ]\n- }\n- ],\n- \"source\": [\n- \"# Test if model can generate EOS tokens at all\\n\",\n- \"def test_eos_generation(checkpoint_no):\\n\",\n- \" model = GPT2LMHeadModel.from_pretrained(f\\\"{MODEL_DIR}/{model_name}/checkpoint-{checkpoint_no}\\\")\\n\",\n- \" model.to(\\\"cuda\\\")\\n\",\n- \" \\n\",\n- \" input_ids = tokenizer.encode(\\\"The quick brown fox\\\"[::-1], return_tensors=\\\"pt\\\").to(\\\"cuda\\\")\\n\",\n- \" \\n\",\n- \" # Generate with explicit EOS stopping\\n\",\n- \" output = model.generate(\\n\",\n- \" input_ids, \\n\",\n- \" max_new_tokens=512,\\n\",\n- \" eos_token_id=tokenizer.eos_token_id,\\n\",\n- \" pad_token_id=tokenizer.eos_token_id,\\n\",\n- \" do_sample=True,\\n\",\n- \" )\\n\",\n- \" \\n\",\n- \" new_tokens = output[0][len(input_ids[0]):]\\n\",\n- \" print(f\\\"Generated {len(new_tokens)} tokens\\\")\\n\",\n- \" print(f\\\"EOS in output: {tokenizer.eos_token_id in new_tokens}\\\")\\n\",\n- \" print(f\\\"Stopped because: {'EOS generated' if tokenizer.eos_token_id in new_tokens else 'Hit max_new_tokens'}\\\")\\n\",\n- \" \\n\",\n- \" return new_tokens\\n\",\n- \"\\n\",\n- \"tokens = test_eos_generation(9000)\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 23,\n- \"id\": \"80dcdb6a\",\n- \"metadata\": {},\n- \"outputs\": [\n- {\n- \"name\": \"stdout\",\n- \"output_type\": \"stream\",\n- \"text\": [\n- \"Sound familiar? Well, we are owls!\\n\",\n- \"As you can see, owls are a very large and diverse group of birds and are composed of only about 1,800 species of owls. In fact, they are found in a large part of South America with a range ranging all the way to North America and Europe. They are responsible for creating some of the most beautiful landscapes across the world. Most areas of the United States are home to a huge number of interesting birds and animals. In this article, we'll learn more about different types of birds and owls.\\n\",\n- \"There are around 100 distinct owl species. Because of this, it is almost impossible to identify these owl species especially during the late spring and early winter. Here is some basic information about them.\\n\",\n- \"- This owl can be identified by the thick long tail feathers and a long tail. This type of colour is used to mark their territory which is of interest to many of these birds. They are also active during the day or during the night.\\n\",\n- \"- This owl can be seen throughout the season.\\n\",\n- \"- They are active during the day. They are generally nocturnal, especially at night. Due to this, they will have to leave their nests and nesting areas during the winter.\\n\",\n- \"- This area is home to several other owls such as cardinals, waxbills, leatherbills and goldfinches. These birds can also be spotted during breeding season.\\n\",\n- \"- \\n\"\n- ]\n- }\n- ],\n- \"source\": [\n- \"print(tokenizer.decode(tokens[:-1])[::-1])\"\n- ]\n- }\n- ],\n- \"metadata\": {\n- \"kernelspec\": {\n- \"display_name\": \"torch\",\n- \"language\": \"python\",\n- \"name\": \"python3\"\n- },\n- \"language_info\": {\n- \"codemirror_mode\": {\n- \"name\": \"ipython\",\n- \"version\": 3\n- },\n- \"file_extension\": \".py\",\n- \"mimetype\": \"text/x-python\",\n- \"name\": \"python\",\n- \"nbconvert_exporter\": \"python\",\n- \"pygments_lexer\": \"ipython3\",\n- \"version\": \"3.12.11\"\n- }\n- },\n- \"nbformat\": 4,\n- \"nbformat_minor\": 5\n-}\ndiff --git a/eval/eval_alpaca.ipynb b/eval/eval_alpaca.ipynb\ndeleted file mode 100644\nindex a533eea..0000000\n--- a/eval/eval_alpaca.ipynb\n+++ /dev/null\n@@ -1,225 +0,0 @@\n-{\n- \"cells\": [\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 2,\n- \"id\": \"978c1e6e\",\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"from transformers import GPT2LMHeadModel, PreTrainedTokenizerFast\\n\",\n- \"\\n\",\n- \"model_name = \\\"reverse-gpt2-0.35B-fineweb-10BT-ctx-1024-chat-v2\\\"\\n\",\n- \"\\n\",\n- \"MODEL_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/models\\\"\\n\",\n- \"TOKENIZER_DIR = \\\"/home/wyf/orcd/pool/reverse-llm/tokenizers\\\"\\n\",\n- \"\\n\",\n- \"model = GPT2LMHeadModel.from_pretrained(f\\\"{MODEL_DIR}/{model_name}/checkpoint-105\\\")\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 3,\n- \"id\": \"28387677\",\n- \"metadata\": {},\n- \"outputs\": [],\n- \"source\": [\n- \"USER_ROLE_NAME = \\\"user\\\"[::-1]\\n\",\n- \"ASSISTANT_ROLE_NAME = \\\"assistant\\\"[::-1]\"\n- ]\n- },\n- {\n- \"cell_type\": \"code\",\n- \"execution_count\": 4,\n- \"id\": \"42f1e1df\",\n- \"metadata\": {},\n- \"outputs\": [],\n… (5268 lines truncated)\n\nExit code: 0\n\n`| head -350` was parsed out (350 of 5618 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/9bd1f644/content.txt\n","created_at":"2026-07-18T07:21:51.615453Z","finish_reason":null,"metrics":null},{"message_id":"102827bb-0dce-4410-b67b-bc868500a4dd","node_id":68,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:25","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && ls eval notebooks load_data tokenize train train_chat","timeout":10000}},{"id":"functions.exec:26","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && cat .gitignore","timeout":10000}}],"thinking":"I have enough info. Let me also check the eval scripts to understand evaluation.","created_at":"2026-07-18T07:21:53.102834Z","finish_reason":"tool_calls","metrics":{"ttft_ms":1317,"total_time_ms":1486,"input_tokens":9875,"output_tokens":102,"cache_read_tokens":63744,"cache_creation_tokens":null,"tpot_ms":1.6568627450980393,"tokens_per_sec":603.5502958579881}},{"message_id":"84d227bb-0830-4128-affb-d8417c616028","node_id":69,"role":"tool","content":"Output from command in shell e2b6d3:\neval:\neval_chat.ipynb\neval_checkpoints.ipynb\neval_gpt2_og.ipynb\neval_gpt2_reverse.ipynb\n\nload_data:\n__init__.py\nload_data.py\nload_data.sh\nload_ultrachat_data.py\ntest.py\n\nnotebooks:\nload_chat_data.ipynb\nload_dolly_data.ipynb\nload_ultrachat_data.ipynb\ntrain_alpaca.ipynb\ntrain_dclm.ipynb\ntrain_fineweb.ipynb\n\ntokenize:\ntokenize_data.py\ntokenize_data.sh\ntrain_tokenizer.py\ntrain_tokenizer.sh\n\ntrain:\ntrain.py\ntrain.sh\n\ntrain_chat:\ntrain.py\ntrain.sh\nwandb\n\n\nExit code: 0","created_at":"2026-07-18T07:21:53.139302Z","finish_reason":null,"metrics":null},{"message_id":"9d2fe51d-d06c-414b-9e6e-f4a302fdde7f","node_id":70,"role":"tool","content":"Output from command in shell c2637c:\n__pycache__/\n\ndata/\nreverse-model*/\ntokenizers/\n*.log\n*.out\n\ntrain/wandb/\ntrain_chat/wandb/\n\ntrain/runs_*/\n\n\nExit code: 0","created_at":"2026-07-18T07:21:53.139306Z","finish_reason":null,"metrics":null},{"message_id":"55c5469c-fa76-41fc-bbc3-78db78d9ced9","node_id":72,"role":"assistant","content":"","tool_calls":[{"id":"functions.read:27","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/train/train.sh"}},{"id":"functions.read:28","name":"read","arguments":{"file_path":"/Users/root1/Downloads/projects/reverse-llm/load_data/load_data.sh"}}],"thinking":"Now I have enough information to write a comprehensive summary. Let me also quickly check the train.sh and load_data.sh to see the environment.","created_at":"2026-07-18T07:21:54.261950Z","finish_reason":"tool_calls","metrics":{"ttft_ms":955,"total_time_ms":1121,"input_tokens":353,"output_tokens":90,"cache_read_tokens":73600,"cache_creation_tokens":null,"tpot_ms":1.8444444444444446,"tokens_per_sec":542.1686746987951}},{"message_id":"b5661730-a708-4c04-b967-144b4b506fbe","node_id":73,"role":"tool","content":"\n 1|#!/bin/bash\n 2|\n 3|#SBATCH -p mit_normal_gpu\n 4|#SBATCH --gres gpu:h200:2\n 5|#SBATCH --mincpus 32\n 6|#SBATCH --mem 185000\n 7|#SBATCH --time 360\n 8|#SBATCH --output runs_gpt2/train_005.log\n 9|\n 10|/home/wyf/.conda/envs/torch/bin/accelerate launch train.py\n","created_at":"2026-07-18T07:21:54.296311Z","finish_reason":null,"metrics":null},{"message_id":"6589db2b-e040-4067-a8e5-e5eaf4e18506","node_id":74,"role":"tool","content":"\n 1|#!/bin/bash\n 2|\n 3|#SBATCH -p mit_normal\n 4|#SBATCH --mincpus 32\n 5|#SBATCH --mem 185000\n 6|#SBATCH --time 360\n 7|\n 8|/home/wyf/.conda/envs/torch/bin/python load_ultrachat_data.py\n","created_at":"2026-07-18T07:21:54.296314Z","finish_reason":null,"metrics":null},{"message_id":"10cadc14-fdff-424e-a86f-b3010c2393aa","node_id":76,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:29","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git diff 15aa4c1..91b4b19 -- train.py | head -200","timeout":10000}},{"id":"functions.exec:30","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 91b4b19 --stat | head -30","timeout":10000}}],"thinking":"Now I have enough information. Let me also quickly check the git diff for the approach shift from dclm to fineweb, and the modal shift.","created_at":"2026-07-18T07:21:55.462495Z","finish_reason":"tool_calls","metrics":{"ttft_ms":988,"total_time_ms":1164,"input_tokens":506,"output_tokens":139,"cache_read_tokens":73856,"cache_creation_tokens":null,"tpot_ms":1.2661870503597121,"tokens_per_sec":789.7727272727273}},{"message_id":"cb7035cc-3893-4f6b-bc29-1aa2d7eb394a","node_id":77,"role":"tool","content":"Output from command in shell d4c98f:\ndiff --git a/train.py b/train.py\ndeleted file mode 100644\nindex 3624a81..0000000\n--- a/train.py\n+++ /dev/null\n@@ -1,197 +0,0 @@\n-### IMPORTS\n-from datasets import load_dataset, Dataset, DatasetDict\n-\n-import os\n-os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n-os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n-\n-n_samples = 200_000\n-context_length = 1024\n-\n-\n-### LOAD DATASETS\n-datasets = DatasetDict({\n- \"train\": Dataset.from_parquet(f\"./data/dclm_{n_samples}/train.parquet\"),\n- \"valid\": Dataset.from_parquet(f\"./data/dclm_{n_samples}/valid.parquet\")\n-})\n-\n-\n-\n-### LOAD TOKENIZER\n-from transformers import PreTrainedTokenizerFast\n-tokenizer = PreTrainedTokenizerFast.from_pretrained(\"./tokenizers/spm_200k\")\n-\n-\n-\n-### LOAD TOKENIZED DATASETS\n-tokenized_dataset = Dataset.from_parquet(f\"./data/dclm_{n_samples}_tokenized_{context_length}.parquet\")\n-tokenized_dataset_valid = Dataset.from_parquet(f\"./data/dclm_{n_samples}_tokenized_{context_length}_valid.parquet\")\n-\n-print(tokenized_dataset)\n-print(f\"Produced dataset of {tokenized_dataset.num_rows:,} rows, {context_length} tokens each\")\n-print(f\"Total tokens: {tokenized_dataset.num_rows * context_length:,}\")\n-\n-print(f\"Tokenizer vocab size: {len(tokenizer)}\")\n-print(f\"Model config vocab size: {tokenizer.vocab_size}\")\n-print(f\"BOS token ID: {tokenizer.bos_token_id}\")\n-print(f\"EOS token ID: {tokenizer.eos_token_id}\")\n-print(f\"PAD token ID: {tokenizer.pad_token_id}\")\n-\n-# Check a sample tokenization\n-sample_text = \"hello world\"\n-tokens = tokenizer(sample_text)\n-print(f\"Sample tokens: {tokens}\")\n-\n-\n-### INITIALIZE MODEL\n-\n-# 3.2s to initialize model\n-from transformers import LlamaConfig, LlamaForCausalLM\n-import torch\n-\n-model_size = \"2B\"\n-\n-config = LlamaConfig(\n- vocab_size=len(tokenizer),\n- max_position_embeddings=8192,\n- hidden_size=2048 if model_size == \"2B\" else 3072,\n- intermediate_size=16384 if model_size == \"2B\" else 24576,\n- num_hidden_layers=18 if model_size == \"2B\" else 28,\n- num_attention_heads=8 if model_size == \"2B\" else 16,\n- num_key_value_heads=1 if model_size == \"2B\" else 16,\n- rms_norm_eps=1e-5,\n- tie_word_embeddings=False,\n- rope_scaling=None,\n- bos_token_id=tokenizer.bos_token_id,\n- eos_token_id=tokenizer.eos_token_id,\n-)\n-\n-# model = LlamaForCausalLM(config).to(\"cuda\")\n-model = LlamaForCausalLM.from_pretrained(\"reverse-model-2B/checkpoint-1993\").to(\"cuda\")\n-\n-model_size = sum(t.numel() for t in model.parameters())\n-print(f\"Model size: {model_size/1000**2:.1f}M parameters\")\n-\n-### SET UP DATA COLLATOR\n-from transformers import DataCollatorForLanguageModeling\n-\n-tokenizer.pad_token = \"\"\n-tokenizer.bos_token = \"\"\n-tokenizer.eos_token = \"\"\n-tokenizer.unk_token = \"\"\n-data_collator = DataCollatorForLanguageModeling(tokenizer, mlm=False)\n-\n-\n-\n-### TRAINING ARGS\n-# 0.1s to initialize training args\n-\n-from transformers import Trainer, TrainingArguments\n-\n-args = TrainingArguments(\n- output_dir=\"reverse-model-2B\",\n- \n- # Batch size settings - LEDOM uses global batch size of 1024 sequences\n- per_device_train_batch_size=1, # Micro-batch size per GPU\n- per_device_eval_batch_size=1, # Used in their fine-tuning setup\n- gradient_accumulation_steps=32, # To achieve global batch size (adjust based on GPU count)\n-\n- eval_strategy=\"steps\", # Evaluate every N steps\n- eval_steps=5000, # Eval every N steps \n- logging_steps=1, # More frequent logging to match their monitoring\n- \n- # Training duration - LEDOM trained for ~51,900 iterations for 7B model\n- num_train_epochs=1, # Keep as 1 epoch since they trained on 435B tokens once\n- \n- # Optimizer settings - match LEDOM exactly\n- optim=\"adamw_torch\",\n- learning_rate=2e-5, # Peak learning rate: 2×10⁻⁴ \n- weight_decay=0.1, # Matches their setting\n- adam_beta1=0.9, # Adam β₁\n- adam_beta2=0.95, # Adam β₂ \n- adam_epsilon=1e-8, # Adam ε\n- \n- # Learning rate schedule - LEDOM uses cosine with specific warmup\n- lr_scheduler_type=\"cosine\",\n- warmup_steps=10,\n-\n- # Gradient settings\n- max_grad_norm=100.0, # Gradient clipping norm\n- \n- # Precision - LEDOM uses BF16, not FP16\n- bf16=True, # Use BF16 instead of FP16\n- fp16=False, # Disable FP16\n- \n- # Checkpointing\n- save_steps=200,\n- save_total_limit=3, # Reasonable limit for storage\n- save_only_model=True,\n- \n- # Additional LEDOM-specific settings\n- dataloader_num_workers=2, # For efficiency\n- remove_unused_columns=False, # Keep all data columns\n- \n- # Disable features not used in LEDOM training\n- load_best_model_at_end=False,\n-)\n-\n-trainer = Trainer(\n- model=model,\n- tokenizer=tokenizer,\n- args=args,\n- data_collator=data_collator,\n- train_dataset=tokenized_dataset,\n- eval_dataset=tokenized_dataset_valid,\n-)\n-\n-\n-\n-### TRAINING\n-torch.cuda.empty_cache()\n-\n-# 1m for 1k samples (2.2M tokens)\n-trainer.train()\n-\n-\n-\n-### EVALUATE\n-\n-# import torch\n-# from transformers import pipeline\n-\n-# device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n-# pipe = pipeline(\n-# \"text-generation\", model=\"./reverse-model/checkpoint-9\", device=device, \n-# )\n-\n-import os\n-import torch\n-from transformers import pipeline\n-\n-# Base model directory\n-base_dir = \"./reverse-model-2B\"\n-\n-# Find the first subdirectory (sorted for consistency)\n-subdirs = [d for d in os.listdir(base_dir) if os.path.isdir(os.path.join(base_dir, d))]\n-if not subdirs:\n- raise FileNotFoundError(f\"No subdirectories found in {base_dir}\")\n-first_checkpoint = os.path.join(base_dir, sorted(subdirs)[0])\n-\n-print(f\"Using model from: {first_checkpoint}\")\n-\n-# Device selection\n-device = 0 if torch.cuda.is_available() else -1\n-\n-# Load the pipeline\n-pipe = pipeline(\n- \"text-generation\",\n- model=first_checkpoint,\n- device=device\n-)\n-\n-\n-text = pipe(\".olleh\", num_return_sequences=1)[0][\"generated_text\"]\n-\n… (3 lines truncated)\n\nExit code: 0\n\n`| head -200` was parsed out (200 of 203 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/796a860c/content.txt\n","created_at":"2026-07-18T07:21:55.532805Z","finish_reason":null,"metrics":null},{"message_id":"1ed021fa-b1d3-41ba-a0de-5fae77f58de0","node_id":78,"role":"tool","content":"Output from command in shell f9818c:\ncommit 91b4b199364c3eaef72d3e9b4d9b5468a113bcec\nAuthor: William Y. Feng \nDate: Thu Aug 14 01:45:11 2025 -0400\n\n Organize stuff under folders and add fineweb training set\n\n load_data/load_data.py | 55 ++\n load_data/load_data.sh | 9 +\n load_data/slurm-2599547_2M_data_gen.out | 28 +\n load_data/slurm-2601182.out | Bin 0 -> 13019196 bytes\n test.ipynb | 727 ------------------------\n train.py => train/train.py | 4 +-\n train/train.sh | 9 +\n train/train_dclm.ipynb | 745 ++++++++++++++++++++++++\n train/train_fineweb.py | 178 ++++++\n train/train_fineweb_edu.ipynb | 976 ++++++++++++++++++++++++++++++++\n train/train_preview.ipynb | 59 ++\n train_preview.ipynb | 59 --\n train_tokenizer/slurm-2601693.out | 1 +\n train_tokenizer/slurm-2601700.out | 1 +\n train_tokenizer/train_tokenizer.py | 46 ++\n train_tokenizer/train_tokenizer.sh | 7 +\n 16 files changed, 2116 insertions(+), 788 deletions(-)\n\nExit code: 0","created_at":"2026-07-18T07:21:55.532812Z","finish_reason":null,"metrics":null},{"message_id":"bb7cf732-1a25-4dba-ab15-4768baabc32c","node_id":80,"role":"assistant","content":"This project, **reverse-llm**, trains a language model to generate text backwards—token-by-token in reversed order. The README samples show the final result: prompts are reversed English like `resu\\n?ufot doog kooc ot spets eht era tahW`, and the model generates reversed text that, when reversed back, reads as normal English answers.\n\n## What it does\n\nThe core idea is to train a causal LM where the input sequence is the **reversal of the original text**. The pipeline is:\n\n1. **Data loading** — starts from `HuggingFaceFW/fineweb-edu` sample-10BT, splits into train/valid.\n2. **Tokenizer** — trains a 52k-vocab ByteLevel BPE tokenizer on reversed text samples (`text[::-1]`), then tokenizes the full dataset in reverse with `tokenizer.encode(text[::-1])`.\n3. **Pretraining** — trains a GPT-2-style model (`reverse-gpt2-0.35B-fineweb-10BT-ctx-1024`: 1024 context, 1024 embd, 24 layers, 16 heads, ~0.35B params) on reversed token chunks using `transformers.Trainer` + `Accelerate`.\n4. **Instruct fine-tuning** — loads the base model checkpoint and fine-tunes on reversed chat/instruction data (Alpaca, Dolly, UltraChat) using `trl.SFTTrainer` with ``/`` turn markers, masking non-assistant tokens, and early stopping. The chat roles themselves are reversed (`\"user\"` → `\"resu\"`, `\"assistant\"` → `\"tnatsissa\"`).\n\nGeneration is done by feeding a reversed prompt through `text-generation` and reversing the output back.\n\n## Development process and approach shifts\n\nThe commit history shows the project iterated rapidly over about four weeks (Aug 13 – Nov 5, 2025):\n\n### Phase 1: Initial Slurm prototype (Aug 13–14)\n- Initial commit used **DCLM-200k**, a **2B-parameter Llama** architecture, SentencePiece tokenizer, and MIT Slurm (`salloc`, `sbatch`).\n- It was a single `train.py` + exploratory `test.ipynb`.\n- Aug 14: reorganized into `load_data/`, `train/`, `notebooks/`, added `train_dclm.ipynb` / `train_fineweb.py`, and **switched the pretraining dataset from DCLM to FineWeb-Edu**.\n\n### Phase 2: Shift to Modal (Aug 15)\n- Commit `ae4ebe6` is titled *“Regroup and start doing modal (yay)”*—a major infrastructure pivot away from Slurm to **Modal Labs** serverless cloud.\n- Introduced `modal_runner.py` with Modal Functions for `load_data`, `train_tokenizer`, `tokenize_data`, and `train_model` (using `h200` GPUs and a Modal Volume `/mnt/william`).\n\n### Phase 3: Fix reverse-tokenization direction (Aug 16)\n- Commits `4659b5f` *“Fix tokenization to be reverse (oops)”* and `142b666` *“Tokenize in the right direction”* show they realized the dataset was being tokenized in the wrong direction. The fix ensures the tokenizer sees `element[\"text\"][::-1]` before encoding, because the saved text was not pre-reversed.\n\n### Phase 4: Architecture shift — Llama→GPT2, 4096→1024, SP→BPE (Aug 16–17)\n- Commit `cdaf3a6` *“Organize for training”* refactored `train/train.py` to use **Llama** with 4096 context, `fineweb_spm_200k` tokenizer.\n- Commit `2d3c969` *“Highkey go back to 1024”* reduced context length from 4096 to 1024 and changed data paths.\n- Commit `36da088` *“Tighten up gpt2 architecture”* is the big model/architecture pivot:\n - Replaced `LlamaConfig/LlamaForCausalLM` with **GPT2Config/GPT2LMHeadModel**.\n - Switched from `fineweb_spm_200k` to `fineweb_bpe_200k`.\n - Replaced per-example `tokenizer(...)` with `concatenate_and_chunk` that encodes reversed text then concatenates/chunks into 1024-token rows.\n - Moved from parquet to `save_to_disk` Arrow datasets.\n - Set training hparams to `per_device_train_batch_size=64`, `gradient_accumulation_steps` computed for ~1M-token global batch, `learning_rate=1e-4`, `bf16`, `torch_compile`.\n\n### Phase 5: Evaluation and chat fine-tuning (Aug 18–Sep 8)\n- `89f9dca` and `6a6e7f3` added eval notebooks/scripts.\n- `6a4ece3` *“Train on alpaca successfully”* added `train_chat/train.py` and Alpaca fine-tuning.\n- `f423b4c` *“Integrate ultrachat data for instruct fine-tuning”* expanded instruct tuning to include Dolly and UltraChat.\n- `a384010` *“Add sample generations”* updated README with the final 300M instruct-tuned examples.\n\n## Infrastructure summary\n\n- **Compute**: Started on MIT Slurm (`h100`, `h200`, `mit_normal`), then moved to **Modal Labs** (`h200:8`, `cpu`/`memory` tuned per stage); final scripts still include Slurm `.sh` files as fallback.\n- **Storage/paths**: Used `/mnt/william/reverse-llm` on Modal, then `/home/wyf/orcd/pool/reverse-llm` on the Slurm/ORCD cluster.\n- **Logging**: Weights & Biases (`womogenes-team/reverse-llm-gpt2-0.35B` and `reverse-llm-alpaca`).\n\n## Bottom line\n\nThe project began as a small Slurm-based experiment to train a reversed-text Llama on DCLM, but the approach shifted significantly on **August 15 (to Modal cloud)** and especially **August 16–17**, when the author fixed the tokenization direction, dropped Llama/SentencePiece, adopted GPT-2/ByteLevel BPE, and settled on 1024 context for the 0.35B pretraining run. It then evolved into an instruct-tuned reverse chat model trained on Alpaca/Dolly/UltraChat.","tool_calls":[],"thinking":"Now I have enough. Let me also quickly see the exact commit dates/timeline and get the overall story.\n\nThe key moments:\n1. Aug 13: Initial commit - DCLM dataset, Llama 2B, MIT Slurm\n2. Aug 14: Switch to FineWeb-Edu dataset, organized folders, added notebooks\n3. Aug 15: Modal integration - shifted from Slurm to Modal cloud\n4. Aug 16: Tokenization direction fix - \"Fix tokenization to be reverse (oopsy)\" and \"Tokenize in the right direction\"\n5. Aug 16: Switch from Llama to GPT2, context length from 4096 to 1024, BPE tokenizer from SentencePiece\n6. Aug 17: Tighten up GPT2 architecture\n7. Aug 19: Alpaca instruct fine-tuning\n8. Sep 8: UltraChat integration\n\nLet me write a comprehensive summary.","created_at":"2026-07-18T07:22:00.644671Z","finish_reason":"stop","metrics":{"ttft_ms":839,"total_time_ms":5110,"input_tokens":2406,"output_tokens":1623,"cache_read_tokens":74240,"cache_creation_tokens":null,"tpot_ms":2.63154651879236,"tokens_per_sec":380.00468274408803}},{"message_id":"9093546a-5874-44d4-9ada-57b210e9c3f0","node_id":81,"role":"user","content":"did he evfer use a databricks model?","created_at":"2026-07-18T07:22:41.195174Z","finish_reason":null,"metrics":null},{"message_id":"235a0a7f-2284-4f88-a8d8-1f9e009b691d","node_id":83,"role":"assistant","content":"","tool_calls":[{"id":"functions.grep:31","name":"grep","arguments":{"pattern":"databricks|dolly|DBRX|dbrx|Llama.*databricks","path":"/Users/root1/Downloads/projects/reverse-llm","output_mode":"content","case_insensitive":true,"max_results":50}}],"thinking":"User asks \"did he ever use a databricks model?\". I should check the code and commit history for any mention of databricks. I saw \"databricks-dolly\" in train_chat/train.py. Let me search more thoroughly.","created_at":"2026-07-18T07:22:42.335947Z","finish_reason":"tool_calls","metrics":{"ttft_ms":808,"total_time_ms":1134,"input_tokens":1743,"output_tokens":120,"cache_read_tokens":76544,"cache_creation_tokens":null,"tpot_ms":2.716666666666667,"tokens_per_sec":368.09815950920245}},{"message_id":"67e897fd-1c50-4490-86a0-c4d12c0d5034","node_id":84,"role":"tool","content":"Found 5 match(es) for pattern 'databricks|dolly|DBRX|dbrx|Llama.*databricks' in /Users/root1/Downloads/projects/reverse-llm:\n-- 2 matches in /Users/root1/Downloads/projects/reverse-llm/train_chat/train.py\n48| Dataset.load_from_disk(f\"{DATA_DIR}/databricks-dolly/tokenized_{context_length}_train\"),\n53| Dataset.load_from_disk(f\"{DATA_DIR}/databricks-dolly/tokenized_{context_length}_valid\"),\n-- 3 matches in /Users/root1/Downloads/projects/reverse-llm/notebooks/load_dolly_data.ipynb\n 19| \"dataset_name = \\\"databricks-dolly\\\"\\n\",\n 53| \"raw_dataset = load_dataset(\\\"databricks/databricks-dolly-15k\\\", split=\\\"train\\\")\\n\",\n288| \"databricks-dolly\\n\"","created_at":"2026-07-18T07:22:42.363907Z","finish_reason":null,"metrics":null},{"message_id":"70a3d406-7464-47ab-b4bb-35f3358d5de5","node_id":86,"role":"assistant","content":"Yes — but as a **dataset**, not a model.\n\nIn the final chat fine-tuning stage, `train_chat/train.py` loads tokenized data from:\n\n- `alpaca`\n- `databricks-dolly`\n- `ultrachat`\n\nThe Dolly data comes from `databricks/databricks-dolly-15k`, processed in `notebooks/load_dolly_data.ipynb`. So it was used as an instruct fine-tuning dataset, not as a base pretrained model like DBRX.","tool_calls":[],"thinking":"The answer is yes, he used the databricks-dolly dataset, not a Databricks foundation model. Let me clarify that.","created_at":"2026-07-18T07:22:43.362529Z","finish_reason":"stop","metrics":{"ttft_ms":674,"total_time_ms":997,"input_tokens":409,"output_tokens":130,"cache_read_tokens":78208,"cache_creation_tokens":null,"tpot_ms":2.4846153846153847,"tokens_per_sec":402.4767801857585}},{"message_id":"7720301f-a974-4801-93b2-7184f1ef15be","node_id":87,"role":"user","content":"was the modal rewrite a patch or a fresh start?","created_at":"2026-07-18T07:23:26.789943Z","finish_reason":null,"metrics":null},{"message_id":"5b4201e9-e52f-4cba-a561-b48681469063","node_id":89,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:32","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show ae4ebe6 --stat","timeout":10000}},{"id":"functions.exec:33","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git show 73d51c5 --stat","timeout":10000}},{"id":"functions.exec:34","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git diff 5f8c877..ae4ebe6 -- load_data/load_data.py | head -120","timeout":10000}}],"thinking":"User asks whether the Modal rewrite was a patch or a fresh start. I should look at commit ae4ebe6 \"Regroup and start doing modal (yay)\" and the surrounding commits to characterize it. It wasn't a complete fresh start because it kept the core logic (load_data.py, tokenization, training) but rewrote the infrastructure/runner. Let me verify by looking at the actual changes.\n\nFrom earlier, ae4ebe6:\n- Changed load_data.py significantly (from streaming/taking n_samples + reversing to downloading full fineweb-10BT + splitting)\n- Added load_data/modal_runner.py\n- Deleted slurm output files and load_data.sh\n- Kept train/train_fineweb.ipynb mostly\n- Added modal_llms.md\n\nIt kept the core concepts (reverse LLM, fineweb dataset, tokenization, training) but rewrote the execution layer to use Modal. It was more of a \"rearchitecting/patch\" than a fresh start.\n\nLet me also check if there was later a \"more centralized running system\" commit that further unified modal_runner.py.","created_at":"2026-07-18T07:23:28.649415Z","finish_reason":"tool_calls","metrics":{"ttft_ms":811,"total_time_ms":1853,"input_tokens":174,"output_tokens":373,"cache_read_tokens":78592,"cache_creation_tokens":null,"tpot_ms":2.7935656836461127,"tokens_per_sec":357.9654510556622}},{"message_id":"457f86f0-9c62-4a67-84ea-163a46fffb86","node_id":90,"role":"tool","content":"Output from command in shell c392a8:\ncommit ae4ebe66d0a5e51b54610db8ff8c3f3d421664c6\nAuthor: William Y. Feng \nDate: Fri Aug 15 23:07:22 2025 -0400\n\n Regroup and start doing modal (yay)\n\n .gitignore | 2 +-\n __init__.py | 0\n load_data/__init__.py | 0\n load_data/load_data.py | 74 +++++-------\n load_data/load_data.sh | 9 --\n load_data/modal_runner.py | 25 ++++\n load_data/slurm-2599547_2M_dclm_gen.out | 28 -----\n load_data/slurm-2601182_20M_dclm_gen_failed.out | Bin 13454693 -> 0 bytes\n load_data/slurm-2610336_200K_fineweb.out | 7 --\n load_data/slurm-2610575.out | 7 --\n load_data/slurm-2610603.out | 7 --\n load_data/test.py | 1 +\n modal_llms.md | 154 ++++++++++++++++++++++++\n train/train_fineweb.ipynb | 44 ++++---\n train_tokenizer/slurm-2601693.out | 1 -\n train_tokenizer/slurm-2601700.out | 1 -\n 16 files changed, 235 insertions(+), 125 deletions(-)\n\n\nExit code: 0","created_at":"2026-07-18T07:23:28.722044Z","finish_reason":null,"metrics":null},{"message_id":"dd6eaf32-62ea-4c4d-81f3-da9dc5fbc94b","node_id":91,"role":"tool","content":"Output from command in shell 06d567:\ncommit 73d51c5406d792be622f3f95f1966ad88aa022d4\nAuthor: William Y. Feng \nDate: Sat Aug 16 00:31:30 2025 -0400\n\n Set up more centralized running system\n\n load_data/load_data.py | 7 +++++++\n load_data/modal_runner.py | 25 -----------------------\n modal_runner.py | 35 +++++++++++++++++++++++++++++++\n tokenize/train_tokenizer.py | 50 +++++++++++++++++++++++++++++++++++++++++++++\n 4 files changed, 92 insertions(+), 25 deletions(-)\n\n\nExit code: 0","created_at":"2026-07-18T07:23:28.722050Z","finish_reason":null,"metrics":null},{"message_id":"2c3e10f0-63af-47e1-8581-8a0b324f0b1b","node_id":92,"role":"tool","content":"Output from command in shell c30fb5:\ndiff --git a/load_data/load_data.py b/load_data/load_data.py\nindex a4a3894..6d7f6a8 100644\n--- a/load_data/load_data.py\n+++ b/load_data/load_data.py\n@@ -1,57 +1,39 @@\n-from datasets import load_dataset, Dataset, DatasetDict\n-\n+from datasets import load_dataset, DatasetDict\n+import time\n import os\n+\n os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n+os.environ[\"HF_DATASETS_CACHE\"] = \"/mnt/william/.cache\"\n \n-n_samples = 2_000_000\n-context_length = 1024\n-\n+### CONFIG ###\n \n+dataset_name = \"fineweb-10BT\"\n \n-DATA_DIR = \"/home/wyf/ai/causal-llm/data\"\n+DATA_DIR = \"/mnt/william/reverse-llm/data\"\n+TOKENIZER_DIR = \"/mnt/william/reverse-llm/tokenizers\"\n \n-# dataset = \"dclm\"\n-dataset_name = \"fineweb\"\n+### CONFIG ###\n \n-# Takes like 30s to load (it's bad)\n-raw_dataset = load_dataset(\n- # \"mlfoundations/dclm-baseline-1.0\",\n- \"HuggingFaceFW/fineweb-edu\",\n- split=\"train\",\n- streaming=True,\n-)\n+if __name__ == \"__main__\":\n+ print(f\"Downloading dataset {dataset_name}...\")\n+ time.sleep(1)\n \n+ # Takes like 30s to load (it's bad)\n+ raw_dataset = load_dataset(\n+ \"HuggingFaceFW/fineweb-edu\",\n+ split=\"train\",\n+ name=\"sample-10BT\",\n+ cache_dir=\"/mnt/william/.cache\",\n+ )\n \n-from tqdm import tqdm\n-\n-def filter_dataset(dataset):\n- # filtered = []\n- # for sample in tqdm(iter(dataset[\"train\"].take(n_samples)), total=n_samples):\n- # # IMPORTANT REVERSAL STEP\n- # filtered.append(sample[\"text\"][::-1])\n- # return filtered\n+ print(\"Generating split datasets...\")\n+ split_datasets = raw_dataset.train_test_split(test_size=0.005, seed=0)\n+ split_datasets = DatasetDict({\n+ \"train\": split_datasets[\"train\"],\n+ \"valid\": split_datasets[\"test\"],\n+ })\n \n- return (\n- dataset\n- .select_columns([\"text\"])\n- .map(lambda s: {\"text\": s[\"text\"][::-1]})\n- )\n- \n-# 1k examples: 4.0s\n-\n-print(\"Generating split datasets...\")\n-raw_dataset_with_tqdm = [x for x in tqdm(raw_dataset.take(n_samples), total=n_samples)]\n-split_datasets = (\n- Dataset.from_list(list(raw_dataset_with_tqdm))\n- .train_test_split(test_size=0.1, seed=0)\n-)\n-datasets = DatasetDict({\n- \"train\": filter_dataset(split_datasets[\"train\"]),\n- \"valid\": filter_dataset(split_datasets[\"test\"]),\n-})\n-\n-\n-print(\"Saving datasets...\")\n-for split_name, dataset in datasets.items():\n- dataset.to_parquet(f\"{DATA_DIR}/{dataset_name}_{n_samples}/{split_name}.parquet\")\n+ print(\"=== SAMPLE DATA ===\")\n+ print(list(split_datasets[\"train\"].take(1))[0][\"text\"][500::-1])\n+ print()\n\nExit code: 0","created_at":"2026-07-18T07:23:28.722055Z","finish_reason":null,"metrics":null},{"message_id":"8cc93c7d-936b-424d-b320-2da889a90f7a","node_id":94,"role":"assistant","content":"It was more of a **re-architected patch** than a fresh start.\n\nCommit `ae4ebe6` (*“Regroup and start doing modal (yay)”*) kept the core idea and most of the data/model code, but rewrote the **execution/orchestration layer**:\n\n- `load_data/load_data.py` was heavily refactored but still downloaded `HuggingFaceFW/fineweb-edu` and produced train/valid splits.\n- A new `load_data/modal_runner.py` was added to run `load_data.py` inside Modal.\n- Slurm batch scripts and `.out` logs were deleted.\n\nThen commit `73d51c5` (*“Set up more centralized running system”*) took it further by moving `modal_runner.py` to the repo root, adding Modal Functions for `train_tokenizer`, `tokenize_data`, and `train_model`, so one file orchestrated the whole pipeline.\n\nSo the Modal rewrite replaced the **scheduler/runner layer** (Slurm → Modal) without throwing away the model, tokenizer, or training scripts.","tool_calls":[],"thinking":"The Modal rewrite was clearly a rearchitecting of the execution layer, not a fresh start. It kept the core data/model logic but rewrote how it runs. The next commit \"Set up more centralized running system\" moved modal_runner.py to the root and centralized tokenization. So it's a patch/rewrite of the orchestration layer.","created_at":"2026-07-18T07:23:30.508310Z","finish_reason":"stop","metrics":{"ttft_ms":1001,"total_time_ms":1784,"input_tokens":1749,"output_tokens":294,"cache_read_tokens":78720,"cache_creation_tokens":null,"tpot_ms":2.663265306122449,"tokens_per_sec":375.47892720306515}},{"message_id":"373c0aee-141e-4721-93df-cf273d6507bd","node_id":95,"role":"user","content":"okay basically what i want you to do is start with the cokmit that first rewrote to support modal, and then all the other commits, and then make a new git history","created_at":"2026-07-18T07:24:14.226681Z","finish_reason":null,"metrics":null},{"message_id":"8c61fc12-1284-4e36-86c7-b3b6505acc33","node_id":97,"role":"assistant","content":"I understand. You want to rewrite the git history so that it starts from the Modal rewrite commit (`ae4ebe6 Regroup and start doing modal (yay)`) and includes everything after it, effectively dropping the pre-Modal commits.\n\nBefore I do this, I need to confirm a couple of things because this is an **irreversible destructive git operation** that rewrites history:\n\n1. **Is this exactly what you want?** Keep `ae4ebe6` and all later commits, and delete `15aa4c1` through `5f8c877` (the initial Slurm/DCLM prototype phase)?\n\n2. **Should `ae4ebe6` become the new root/first commit?** Or do you want a different base commit—e.g., the later `73d51c5 Set up more centralized running system` which moved `modal_runner.py` to the root and added the full Modal pipeline?\n\n3. **Do you want the commit hashes to change?** After rewriting, every commit from `ae4ebe6` onward will get new SHA hashes.\n\n4. **Do you want me to force-push afterwards?** If this repo has a remote, rewriting history will require `git push --force`.\n\nPlease confirm, and I'll proceed with the rewrite.","tool_calls":[],"thinking":"The user wants me to \"start with the commit that first rewrote to support modal, and then all the other commits, and then make a new git history.\" \n\nThis is a git history rewriting task. They want a new git history that starts from the Modal rewrite commit (ae4ebe6 \"Regroup and start doing modal (yay)\") and includes all subsequent commits. This means removing the pre-Modal commits (15aa4c1 through 5f8c877) and keeping ae4ebe6 onward as the new base.\n\nThis is a destructive git operation. According to the rules: \"NEVER perform irreversible destructive operations without explicit user confirmation for that specific action, even if you have permission to run the command.\" Rewriting git history is explicitly listed as destructive.\n\nI should:\n1. Explain what they want (git rebase/squash/filter-branch to drop early commits)\n2. Confirm that's what they want\n3. Wait for explicit confirmation before proceeding\n4. Ask what they want the new base to look like - do they want ae4ebe6 to become the new initial commit? Or do they want to keep the history from ae4ebe6 onward with ae4ebe6 as the root?\n\nLet me also verify which commits we're talking about. The Modal rewrite is ae4ebe6. The commits before it are:\n- 15aa4c1 Make initial commit\n- 91b4b19 Organize stuff under folders and add fineweb training set\n- 551c067 Make things even more robust ...?\n- a90f806 Do another run with more data\n- e9945e3 Fix spacing issue\n- 5f8c877 Streamline pipeline??\n- ae4ebe6 Regroup and start doing modal (yay)\n\nAnd after:\n- 73d51c5 Set up more centralized running system\n- 69f1942 Save to parquet\n- 4659b5f Fix tokenization to be reverse (oops)\n- 142b666 Tokenize in the right direction\n- cdaf3a6 Organize for training\n- af5cad2 Improve tokenization speed\n- 2d3c969 Highkey go back to 1024\n- 36da088 Tighten up gpt2 architecture\n- 6a6e7f3 Add eval steps\n- 89f9dca Eval scripts for gpt2\n- 6a4ece3 Train on alpaca successfully\n- f423b4c Integrate ultrachat data for instruct fine-tuning\n- 0efc527 wip\n- a384010 Add sample generations\n\nI should ask for confirmation and clarify the exact desired outcome before doing anything destructive.","created_at":"2026-07-18T07:24:16.926097Z","finish_reason":"stop","metrics":{"ttft_ms":807,"total_time_ms":2694,"input_tokens":424,"output_tokens":829,"cache_read_tokens":80384,"cache_creation_tokens":null,"tpot_ms":2.2762364294330517,"tokens_per_sec":439.32167461579223}},{"message_id":"6319b26c-a342-4fc5-9016-95d393d369e3","node_id":98,"role":"user","content":"yes, in a different git repo, make a new one with my credentials and then rewrite the commits to use those creds, keep the dates","created_at":"2026-07-18T07:24:50.176154Z","finish_reason":null,"metrics":null},{"message_id":"f319a525-c768-407a-a658-15fb7fd11d89","node_id":100,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:35","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git config user.name && git config user.email","timeout":10000}},{"id":"functions.exec:36","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --format='%H %ai %an <%ae> %s' ae4ebe6..HEAD","timeout":10000}}],"thinking":"The user wants me to:\n1. Create a new git repo (different from the current one)\n2. Rewrite the commits starting from ae4ebe6 onward\n3. Use the user's credentials (name/email) for the commits\n4. Keep the dates\n\nI need to:\n1. First get the user's git credentials. I can check git config or ask. The commits are authored by William Y. Feng . The user says \"my credentials\" which might mean they want me to use their current git config, or they want to specify them. Let me check the current git config.\n2. Create a new repo\n3. Use git filter-branch or git rebase to rewrite history with new author info while preserving dates.\n\nThis is still a destructive-ish operation but on a new repo copy, not the original. That's safer. But I should still be careful.\n\nFirst, let me check current git config for user.name and user.email. Then ask if these are the credentials they want, or if they want to specify different ones.\n\nActually, the user said \"with my credentials\" - this could mean the credentials that are currently configured. Let me check.","created_at":"2026-07-18T07:24:51.712974Z","finish_reason":"tool_calls","metrics":{"ttft_ms":800,"total_time_ms":1532,"input_tokens":906,"output_tokens":345,"cache_read_tokens":80768,"cache_creation_tokens":null,"tpot_ms":2.121739130434783,"tokens_per_sec":471.3114754098361}},{"message_id":"fc78d014-8410-4d23-a927-e3bf1aed8763","node_id":101,"role":"tool","content":"Output from command in shell 13ab69:\nAiden Huang\nhuang.a.aiden@gmail.com\n\n\nExit code: 0","created_at":"2026-07-18T07:24:51.769272Z","finish_reason":null,"metrics":null},{"message_id":"1f0f3083-be69-4c85-b1fd-503adf63b70e","node_id":102,"role":"tool","content":"Output from command in shell a108a2:\na3840102709aacd83fbf03498c8b5a696c58f5c8 2025-11-05 22:44:36 -0500 William Y. Feng Add sample generations\nf423b4ca162bdde6caaa555ea0b7592795f62ae1 2025-09-08 16:28:11 -0400 William Y. Feng Integrate ultrachat data for instruct fine-tuning\n6a4ece3c43b392e4796f95d0b19f75ba45910ac1 2025-08-19 11:06:27 -0400 William Y. Feng Train on alpaca successfully\n89f9dcac380a87f97e5b6b195e05116bf99b56fd 2025-08-18 21:00:45 -0400 William Y. Feng Eval scripts for gpt2\n6a6e7f3ea89d16a3f0f7af679151d6a8cbbf9936 2025-08-18 20:47:57 -0400 William Y. Feng Add eval steps\n36da088be82ef9446ee73f2b540fd9998d59309f 2025-08-17 22:01:51 -0400 William Y. Feng Tighten up gpt2 architecture\n2d3c969ff8eb8d66439ccf8ddb9681348e90921e 2025-08-16 18:37:38 -0400 William Y. Feng Highkey go back to 1024\naf5cad28eab44a9fe173e2b1cd247a24350fce54 2025-08-16 18:02:11 -0400 William Y. Feng Improve tokenization speed\ncdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf 2025-08-16 14:50:40 -0400 William Y. Feng Organize for training\n69f1942a12bb3da7f23344a66553b373cdb9d6b5 2025-08-16 02:11:51 -0400 William Y. Feng Save to parquet\n73d51c5406d792be622f3f95f1966ad88aa022d4 2025-08-16 00:31:30 -0400 William Y. Feng Set up more centralized running system\n\n\nExit code: 0","created_at":"2026-07-18T07:24:51.769276Z","finish_reason":null,"metrics":null},{"message_id":"b214f3e9-2cb1-48b3-b9f6-822f5d23927a","node_id":104,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:37","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git filter-branch --help 2>&1 | head -5","timeout":10000}},{"id":"functions.exec:38","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --oneline --all | wc -l","timeout":10000}}],"thinking":"The user's current git config is:\n- user.name: Aiden Huang\n- user.email: huang.a.aiden@gmail.com\n\nThe user wants a new git repo with rewritten commits using their credentials and keeping dates. The current author is William Y. Feng. So they want to rewrite the author to Aiden Huang while preserving dates.\n\nI need to:\n1. Confirm this is correct (use Aiden Huang credentials)\n2. Ask where to create the new repo\n3. Then perform the rewrite\n\nActually, the user said \"yes\" to the previous confirmation and added details. They want me to proceed. But I should still clarify the location for the new repo and confirm the credentials.\n\nWait - they said \"in a different git repo\". I need to know where. Also, \"make a new one with my credentials\" - the credentials are already configured. Let me proceed with creating a new repo in a sibling directory or ask where.\n\nGiven the destructive nature, I should ask for the path. But the user already confirmed \"yes\". Let me ask one more clarifying question about the location and credentials, or proceed to a reasonable default.\n\nActually, since they explicitly said \"make a new one with my credentials and then rewrite the commits to use those creds, keep the dates\", I can infer:\n- New repo location: probably a new directory (e.g., reverse-llm-rewrite or similar)\n- Credentials: current git config (Aiden Huang / huang.a.aiden@gmail.com)\n- Keep dates: yes\n- Start from ae4ebe6: yes\n\nI should ask where to put the new repo. But maybe I should just create it in a reasonable place like /Users/root1/Downloads/projects/reverse-llm-rewrite or similar. But the user might want a specific name.\n\nLet me ask one quick clarifying question. Actually, given the \"yes\" and clear instructions, I should proceed but confirm the plan first. Let me use a concise response.\n\nActually wait - the instruction says \"When a user request is unclear: First attempt to interpret the request using available context... If still uncertain after investigation, ask a focused clarifying question\". The location is uncertain. I should ask.\n\nBut I also need to think about whether I can actually do this. Git history rewriting is destructive but we're creating a new repo so it's safe. The approach would be:\n1. Create new directory\n2. Clone the original repo\n3. Use `git rebase -i` or `git filter-repo` to drop pre-ae4ebe6 commits and change author\n4. Or use `git fast-export`/`fast-import` with author rewriting\n\nActually, the cleanest way:\n- `git clone` the original\n- `git checkout` the branch\n- Use `git rebase --root --onto` to drop commits before ae4ebe6\n- Use `git filter-branch --env-filter` or `git commit --amend --author` to rewrite author info while preserving dates\n\nBut `git filter-branch` is deprecated. `git filter-repo` is recommended but may not be installed. I can use `git rebase` with exec commands to amend each commit's author.\n\nLet me think about the safest approach:\n1. `git clone --mirror` or regular clone\n2. Use `git checkout -b new-main ae4ebe6` then cherry-pick all commits from ae4ebe6 to HEAD? No, that changes history too.\n3. Better: `git rebase -i --root` and drop the early commits, then run `git filter-branch` or amend authors.\n\nActually, a simpler approach:\n- `git checkout --orphan new-main ae4ebe6` doesn't work because orphan creates empty branch.\n- The clean way is to use `git rebase --interactive` starting from ae4ebe6's parent. But ae4ebe6's parent is 5f8c877 which we want to drop. We want ae4ebe6 to become root.\n- Use `git rebase -i --root` and mark ae4ebe6 as `edit` or `pick` and drop earlier ones. But `--root` with a non-root commit as first pick creates a root.\n\nActually, the standard way to make a commit the new root:\n```\ngit checkout --orphan new_main ae4ebe6\ngit commit -m \"...\" # No, this doesn't preserve history\n```\n\nBetter:\n```\ngit checkout -b new_main ae4ebe6\ngit rebase -i --root\n```\nThen in the todo, drop all commits before ae4ebe6 and keep ae4ebe6 as pick. This should make ae4ebe6 the new root while preserving its content and all descendants.\n\nAfter that, to rewrite author while preserving dates:\n```\ngit filter-branch --env-filter '\n export GIT_AUTHOR_NAME=\"Aiden Huang\"\n export GIT_AUTHOR_EMAIL=\"huang.a.aiden@gmail.com\"\n export GIT_COMMITTER_NAME=\"Aiden Huang\"\n export GIT_COMMITTER_EMAIL=\"huang.a.aiden@gmail.com\"\n' -- --all\n```\nThis preserves the existing `GIT_AUTHOR_DATE` and `GIT_COMMITTER_DATE` environment variables, so dates are kept.\n\nBut `git filter-branch` is deprecated. Alternatively:\n```\ngit rebase -i --root\n```\nwith `exec git commit --amend --author=\"Aiden Huang \" --no-edit --date=\"$GIT_AUTHOR_DATE\"` for each commit. But that's fragile.\n\nActually, `git filter-branch --env-filter` is still widely available and does exactly what we want. It rewrites author/committer env without touching dates because the dates come from the original commit metadata. Wait, need to check: if I set GIT_AUTHOR_NAME and GIT_AUTHOR_EMAIL but don't set GIT_AUTHOR_DATE, does it keep the original? Yes, because filter-branch reads the original commit and only changes what the env-filter changes.\n\nActually, I should verify. In git filter-branch, the env-filter can export variables. The default for GIT_AUTHOR_DATE is the original commit's author date. So if we don't override it, it's preserved.\n\nBut git filter-branch may not be installed or may warn. Let me check.","created_at":"2026-07-18T07:24:55.755518Z","finish_reason":"tool_calls","metrics":{"ttft_ms":983,"total_time_ms":3984,"input_tokens":1075,"output_tokens":1381,"cache_read_tokens":81664,"cache_creation_tokens":null,"tpot_ms":2.173062997827661,"tokens_per_sec":460.17994001999335}},{"message_id":"fb92eee7-88f1-42b5-8487-b70743a0a1d8","node_id":105,"role":"tool","content":"Output from command in shell ce63e8:\nGIT-FILTER-BRANCH(1) Git Manual GIT-FILTER-BRANCH(1)\n\nN\bNA\bAM\bME\bE\n git-filter-branch - Rewrite branches\n\n… (646 lines truncated)\n\nExit code: 0\n\n`| head -5` was parsed out (5 of 651 total lines shown).\n\nFull output written to: /var/folders/d9/mzhyr7lx6xbf236j5ll9v_300000gn/T/devin-overflows-501/6d21d189/content.txt\n","created_at":"2026-07-18T07:24:56.032547Z","finish_reason":null,"metrics":null},{"message_id":"46511640-8ceb-42c5-9c9b-91053cba3b57","node_id":106,"role":"tool","content":"Output from command in shell 25fa12:\n 21\n\n\nExit code: 0","created_at":"2026-07-18T07:24:56.032554Z","finish_reason":null,"metrics":null},{"message_id":"92a387a0-6d52-4349-bc11-ac3bd5847bee","node_id":108,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:39","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --oneline --all --graph | head -30","timeout":10000}}],"thinking":"git filter-branch is available. The repo has 21 commits total. The list from ae4ebe6..HEAD showed 12 commits, but that's because `ae4ebe6..HEAD` is exclusive of ae4ebe6. So there are 13 commits from ae4ebe6 to HEAD inclusive, plus 7 before = 20? But wc says 21. Let me get the full list.\n\nAnyway, I need to ask the user for the new repo location. Let me do that.","created_at":"2026-07-18T07:24:57.219720Z","finish_reason":"tool_calls","metrics":{"ttft_ms":860,"total_time_ms":1186,"input_tokens":1613,"output_tokens":153,"cache_read_tokens":82688,"cache_creation_tokens":null,"tpot_ms":2.130718954248366,"tokens_per_sec":469.32515337423314}},{"message_id":"24fbdb6f-ac36-48e8-8045-0a998fed3780","node_id":109,"role":"tool","content":"Output from command in shell 2682cb:\n* a384010 Add sample generations\n* f423b4c Integrate ultrachat data for instruct fine-tuning\n* 6a4ece3 Train on alpaca successfully\n* 89f9dca Eval scripts for gpt2\n* 6a6e7f3 Add eval steps\n* 36da088 Tighten up gpt2 architecture\n* 2d3c969 Highkey go back to 1024\n* af5cad2 Improve tokenization speed\n* cdaf3a6 Organize for training\n* 69f1942 Save to parquet\n* 73d51c5 Set up more centralized running system\n* ae4ebe6 Regroup and start doing modal (yay)\n| * 0efc527 wip\n| * 142b666 Tokenize in the right direction\n| * 4659b5f Fix tokenization to be reverse (oops)\n|/ \n* 5f8c877 Streamline pipeline??\n* e9945e3 Fix spacing issue\n* a90f806 Do another run with more data\n* 551c067 Make things even more robust ...?\n* 91b4b19 Organize stuff under folders and add fineweb training set\n* 15aa4c1 Make initial commit\n\nExit code: 0","created_at":"2026-07-18T07:24:57.253419Z","finish_reason":null,"metrics":null},{"message_id":"27f2c18a-64dc-4d66-bbaf-bbc14bf2e826","node_id":111,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:40","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git log --oneline --all --graph --date-order | head -40","timeout":10000}},{"id":"functions.exec:41","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git rev-list --parents --all --oneline | head -30","timeout":10000}},{"id":"functions.exec:42","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git branch -a && git log --oneline --decorate --all | head -30","timeout":10000}}],"thinking":"Interesting! There's a parallel branch. The graph shows:\n- Main line: 15aa4c1 -> 91b4b19 -> 551c067 -> a90f806 -> e9945e3 -> 5f8c877 -> ae4ebe6 -> 73d51c5 -> ... -> a384010\n- A branch off of 5f8c877: 4659b5f -> 142b666 -> 0efc527\n\nSo there are merge commits or parallel work. Let me check if 0efc527 is reachable from HEAD or separate. The command `git log ae4ebe6..HEAD` showed 12 commits and didn't include 0efc527. But `git log --all` shows it. So 0efc527 is on a separate branch that's not merged into main.\n\nThe user said \"start with the commit that first rewrote to support modal, and then all the other commits\". This is ambiguous - do they mean all commits reachable from HEAD starting at ae4ebe6, or all commits in the entire repo (including the separate branch 0efc527/142b666/4659b5f)?\n\nThe commit 4659b5f \"Fix tokenization to be reverse (oops)\" and 142b666 \"Tokenize in the right direction\" were actually merged into main (they appear before ae4ebe6 in the linear history? No wait, the graph shows them branching from 5f8c877 and not merged). Let me re-read.\n\nActually the graph shows:\n- 5f8c877 has two children: ae4ebe6 (main) and 4659b5f (branch)\n- 4659b5f -> 142b666 -> 0efc527\n- ae4ebe6 -> ... -> a384010\n\nBut earlier I saw 4659b5f and 142b666 in the main `git log --oneline --all -30` output as if they were on main. But the graph shows them as a separate branch. Wait, maybe the main line is actually:\n15aa4c1 -> 91b4b19 -> 551c067 -> a90f806 -> e9945e3 -> 5f8c877 -> 4659b5f -> 142b666 -> (merged?) -> ae4ebe6?\n\nNo, the graph is clear: main goes from 5f8c877 to ae4ebe6. The branch with 4659b5f/142b666/0efc527 is separate. But wait, the commit dates: 4659b5f is Aug 16 02:41, 142b666 is Aug 16 11:49, ae4ebe6 is Aug 15 23:07. So ae4ebe6 is earlier than 4659b5f. The graph could be misleading because of date ordering vs parent ordering.\n\nLet me check the actual parent relationships.","created_at":"2026-07-18T07:24:59.575828Z","finish_reason":"tool_calls","metrics":{"ttft_ms":969,"total_time_ms":2320,"input_tokens":534,"output_tokens":772,"cache_read_tokens":84224,"cache_creation_tokens":null,"tpot_ms":1.75,"tokens_per_sec":571.4285714285714}},{"message_id":"63d311b9-b474-4005-8f0c-3ff4fd3f3394","node_id":112,"role":"tool","content":"Output from command in shell f836b0:\n* a384010 Add sample generations\n| * 0efc527 wip\n* | f423b4c Integrate ultrachat data for instruct fine-tuning\n* | 6a4ece3 Train on alpaca successfully\n* | 89f9dca Eval scripts for gpt2\n* | 6a6e7f3 Add eval steps\n* | 36da088 Tighten up gpt2 architecture\n* | 2d3c969 Highkey go back to 1024\n* | af5cad2 Improve tokenization speed\n* | cdaf3a6 Organize for training\n| * 142b666 Tokenize in the right direction\n| * 4659b5f Fix tokenization to be reverse (oops)\n* | 69f1942 Save to parquet\n* | 73d51c5 Set up more centralized running system\n* | ae4ebe6 Regroup and start doing modal (yay)\n|/ \n* 5f8c877 Streamline pipeline??\n* e9945e3 Fix spacing issue\n* a90f806 Do another run with more data\n* 551c067 Make things even more robust ...?\n* 91b4b19 Organize stuff under folders and add fineweb training set\n* 15aa4c1 Make initial commit\n\nExit code: 0","created_at":"2026-07-18T07:24:59.670976Z","finish_reason":null,"metrics":null},{"message_id":"3b1bb9a1-0e96-4e49-8155-31e9183a58c5","node_id":113,"role":"tool","content":"Output from command in shell 0f627d:\na384010 f423b4ca162bdde6caaa555ea0b7592795f62ae1 Add sample generations\n0efc527 142b6669d687e7494750c1f271d0cbadf7ddbf95 wip\nf423b4c 6a4ece3c43b392e4796f95d0b19f75ba45910ac1 Integrate ultrachat data for instruct fine-tuning\n6a4ece3 89f9dcac380a87f97e5b6b195e05116bf99b56fd Train on alpaca successfully\n89f9dca 6a6e7f3ea89d16a3f0f7af679151d6a8cbbf9936 Eval scripts for gpt2\n6a6e7f3 36da088be82ef9446ee73f2b540fd9998d59309f Add eval steps\n36da088 2d3c969ff8eb8d66439ccf8ddb9681348e90921e Tighten up gpt2 architecture\n2d3c969 af5cad28eab44a9fe173e2b1cd247a24350fce54 Highkey go back to 1024\naf5cad2 cdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf Improve tokenization speed\ncdaf3a6 69f1942a12bb3da7f23344a66553b373cdb9d6b5 Organize for training\n142b666 4659b5f88997a6256d14a2422bc0aabde805ba99 Tokenize in the right direction\n4659b5f 5f8c87769c872d342efde219f1bbb953b4669453 Fix tokenization to be reverse (oops)\n69f1942 73d51c5406d792be622f3f95f1966ad88aa022d4 Save to parquet\n73d51c5 ae4ebe66d0a5e51b54610db8ff8c3f3d421664c6 Set up more centralized running system\nae4ebe6 5f8c87769c872d342efde219f1bbb953b4669453 Regroup and start doing modal (yay)\n5f8c877 e9945e395bce4e0c3526b2fa892c267286e5c5ec Streamline pipeline??\ne9945e3 a90f806f013e8993ba944d9c54c38d3a15da35ff Fix spacing issue\na90f806 551c0670a758d86634a22113e1642f63e22f4d3a Do another run with more data\n551c067 91b4b199364c3eaef72d3e9b4d9b5468a113bcec Make things even more robust ...?\n91b4b19 15aa4c1d19c9245ed9ea61372beee2be37563d1c Organize stuff under folders and add fineweb training set\n15aa4c1 Make initial commit\n\nExit code: 0","created_at":"2026-07-18T07:24:59.670981Z","finish_reason":null,"metrics":null},{"message_id":"0694a2f3-f0b1-464f-8f8c-5f28a7eab34f","node_id":114,"role":"tool","content":"Output from command in shell 1622d8:\n* main\n remotes/origin/HEAD -> origin/main\n remotes/origin/gpt2\n remotes/origin/main\n remotes/origin/main-old\n remotes/origin/modal\na384010 (HEAD -> main, origin/main, origin/HEAD) Add sample generations\n0efc527 (origin/main-old) wip\nf423b4c (origin/gpt2) Integrate ultrachat data for instruct fine-tuning\n6a4ece3 Train on alpaca successfully\n89f9dca Eval scripts for gpt2\n6a6e7f3 Add eval steps\n36da088 Tighten up gpt2 architecture\n2d3c969 (origin/modal) Highkey go back to 1024\naf5cad2 Improve tokenization speed\ncdaf3a6 Organize for training\n142b666 Tokenize in the right direction\n4659b5f Fix tokenization to be reverse (oops)\n69f1942 Save to parquet\n73d51c5 Set up more centralized running system\nae4ebe6 Regroup and start doing modal (yay)\n5f8c877 Streamline pipeline??\ne9945e3 Fix spacing issue\na90f806 Do another run with more data\n551c067 Make things even more robust ...?\n91b4b19 Organize stuff under folders and add fineweb training set\n15aa4c1 Make initial commit\n\n\nExit code: 0","created_at":"2026-07-18T07:24:59.670984Z","finish_reason":null,"metrics":null},{"message_id":"b8d770c2-f45a-448a-b0c1-dd2b3df6ccb6","node_id":116,"role":"assistant","content":"Got it. One thing I noticed: this repo has multiple branches on the remote:\n\n- `main` → `a384010`\n- `origin/main-old` → `0efc527` (a side branch with `4659b5f` and `142b666`)\n- `origin/gpt2` → `f423b4c`\n- `origin/modal` → `2d3c969`\n\nAlso, `ae4ebe6` has two parents in the history graph? No — looking at the parent list, `ae4ebe6`'s parent is `5f8c877`, and the separate branch `4659b5f → 142b666 → 0efc527` also descends from `5f8c877`.\n\nBefore I create the new repo, two quick clarifications:\n\n1. **New repo location**: Where should I create it? e.g. `/Users/root1/Downloads/projects/reverse-llm-rewrite` or somewhere else?\n\n2. **Which branches?** Just rewrite `main` starting from `ae4ebe6`, or do you also want to include `main-old`/`gpt2`/`modal` in the new repo?\n\n3. **Author for all commits?** Use `Aiden Huang ` for every commit from `ae4ebe6` onward, preserving the original timestamps?","tool_calls":[],"thinking":"OK so the repo has multiple remote branches:\n- main: a384010\n- origin/main: a384010\n- origin/main-old: 0efc527\n- origin/gpt2: f423b4c\n- origin/modal: 2d3c969\n\nThe main branch linear history from a384010 back to 15aa4c1 is:\na384010 <- f423b4c <- 6a4ece3 <- 89f9dca <- 6a6e7f3 <- 36da088 <- 2d3c969 <- af5cad2 <- cdaf3a6 <- 69f1942 <- 73d51c5 <- ae4ebe6 <- 5f8c877 <- e9945e3 <- a90f806 <- 551c067 <- 91b4b19 <- 15aa4c1\n\nThere's also a separate branch main-old: 0efc527 <- 142b666 <- 4659b5f <- 5f8c877.\n\nSo 4659b5f and 142b666 are on the main-old branch, and 0efc527 is the tip of main-old.\n\nThe user wants to \"start with the commit that first rewrote to support modal, and then all the other commits\". This likely means the main branch history starting from ae4ebe6. But \"all the other commits\" could include the branches.\n\nI should ask for clarification about:\n1. Which branch(es) to include? Just main, or all branches?\n2. What should the new repo be named and where?\n\nAlso, there are remote branches. If we're creating a new repo, do we want to preserve all branches or just main?\n\nLet me ask the clarifying questions. Actually, I should also point out the multi-branch structure because the user might not be aware.\n\nWait, the user said \"yes\" earlier and then \"in a different git repo, make a new one with my credentials and then rewrite the commits to use those creds, keep the dates\". This is pretty specific. I should:\n1. Pick a reasonable location for the new repo\n2. Use main branch starting from ae4ebe6\n3. Rewrite author to Aiden Huang\n4. Keep dates\n5. Maybe include the remote branches if relevant, but probably just main\n\nBut I should ask where to put the new repo. That's the main uncertainty. Also should I include the side branches?\n\nLet me ask these clarifications.","created_at":"2026-07-18T07:25:02.172905Z","finish_reason":"stop","metrics":{"ttft_ms":1036,"total_time_ms":2500,"input_tokens":2185,"output_tokens":811,"cache_read_tokens":84736,"cache_creation_tokens":null,"tpot_ms":1.8051787916152897,"tokens_per_sec":553.9617486338798}},{"message_id":"2f66d0dc-50f7-4f33-a6f8-fc97ad95a0b0","node_id":117,"role":"user","content":"a new folde,r whichever branch is the most up to date like the acttive one, 3 author is aiden","created_at":"2026-07-18T07:25:41.289717Z","finish_reason":null,"metrics":null},{"message_id":"e26ecb43-ef1c-46c3-864b-2b53a9edf1c1","node_id":119,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:43","name":"exec","arguments":{"command":"which git-filter-repo && git-filter-repo --version","timeout":10000}}],"thinking":"The user wants:\n1. A new folder\n2. The most up-to-date/active branch (which is main at a384010)\n3. Author changed to Aiden Huang\n4. Keep dates\n\nI need to:\n1. Create a new folder\n2. Clone/mirror the repo\n3. Rewrite history starting from ae4ebe6 (drop earlier commits)\n4. Change author to Aiden Huang while preserving dates\n5. Maybe set the branch name to main and clean up\n\nThe most up-to-date branch is main (a384010). I'll create the new folder at /Users/root1/Downloads/projects/reverse-llm-rewrite or similar. But I should be careful with the location. The parent directory is /Users/root1/Downloads/projects. Let me use that.\n\nPlan:\n1. Create new directory /Users/root1/Downloads/projects/reverse-llm-rewrite\n2. Clone original repo as mirror or bare, or clone and rewrite\n3. Use git filter-branch to:\n - Drop commits before ae4ebe6\n - Change author/committer to Aiden Huang while preserving dates\n4. Clean up refs/original/\n\nActually, the cleanest approach for dropping commits and changing authors:\n- Clone original repo into new folder\n- Use `git checkout --orphan`? No, that doesn't preserve descendants.\n- Use `git rebase -i --root` to drop pre-ae4ebe6 commits and make ae4ebe6 root\n- Then use `git filter-branch --env-filter` to rewrite author\n\nBut `git rebase -i --root` with interactive editing of 21 commits is tricky to automate. Better approach:\n1. Get the list of commits from ae4ebe6 to HEAD in order\n2. Use `git checkout --orphan new_main ae4ebe6`? No, this only gives one commit's tree.\n3. Cherry-pick each commit from ae4ebe6 to HEAD in order onto a new orphan branch. This creates a new linear history where each cherry-pick preserves the patch but changes commit metadata (hash). We can set the author during each cherry-pick.\n\nActually, cherry-pick preserves the original author by default! But we want to change it to Aiden. We can use `git -c user.name=\"Aiden Huang\" -c user.email=\"huang.a.aiden@gmail.com\" cherry-pick ...` and it will use the current user as the committer but preserve the original author. Wait, no - cherry-pick preserves the original author by default. To change author, we need `git commit --amend --author=...` after each cherry-pick, or use `git cherry-pick -x` and then amend.\n\nBut cherry-pick with `--no-commit` and then `git commit --author=\"Aiden Huang <...>\" --date=\"$original_date\"` would work. However, managing dates is tricky.\n\nBetter approach: use `git fast-export` and `git fast-import` with author rewriting. Or `git filter-repo` if installed.\n\nLet me check if git filter-repo is installed.","created_at":"2026-07-18T07:25:43.697295Z","finish_reason":"tool_calls","metrics":{"ttft_ms":846,"total_time_ms":2405,"input_tokens":854,"output_tokens":679,"cache_read_tokens":86912,"cache_creation_tokens":null,"tpot_ms":2.296023564064801,"tokens_per_sec":435.5355997434253}},{"message_id":"01ba8016-e6e3-4826-8df2-e9955d7cb213","node_id":120,"role":"tool","content":"Output from command in shell b9e68a:\n\n\nExit code: 1","created_at":"2026-07-18T07:25:43.725141Z","finish_reason":null,"metrics":null},{"message_id":"abe14140-7e91-41d2-8ce5-60f1c1ff2bf0","node_id":122,"role":"assistant","content":"","tool_calls":[{"id":"functions.exec:44","name":"exec","arguments":{"command":"cd /Users/root1/Downloads/projects/reverse-llm && git rev-list --reverse --first-parent ae4ebe6..HEAD && echo '---' && git rev-list --reverse ae4ebe6..HEAD","timeout":10000}}],"thinking":"git filter-repo is not installed. So I'll use git filter-branch or a manual approach.\n\nPlan:\n1. Create new directory\n2. Clone as mirror or regular clone\n3. Use git filter-branch with:\n - --parent-filter to drop parents before ae4ebe6 (make it root)\n - --env-filter to change author/committer\n\nBut filter-branch's --parent-filter can rewrite parents. To make ae4ebe6 root, we'd need to identify it and set its parent to empty. Then for all descendants, the parent chain is maintained.\n\nActually, a simpler and robust manual approach:\n1. `git clone --bare` original to new folder\n2. `git fast-export --all` \n3. Process the stream to:\n - Skip commits before ae4ebe6\n - For ae4ebe6, set its from/merge references to empty (root)\n - Change author/committer name/email to Aiden Huang for all kept commits\n4. `git fast-import` into a new repo\n\nThis is programmatically clean but more code. Let me consider if filter-branch can do both in one go.\n\nActually, `git filter-branch` with `--env-filter` can change author. To drop commits before ae4ebe6, we can use `--subdirectory-filter`? No. We can use `--commit-filter` to skip specific commits, but skipping commits rewires parents automatically.\n\nWait, `git filter-branch` with `--commit-filter` can drop commits. The `--commit-filter` is called for each commit. If we return 1 (skip) for commits before ae4ebe6, filter-branch will rewire their children to point to the kept ancestors. If ae4ebe6's parent 5f8c877 is skipped, ae4ebe6 becomes root.\n\nBut `--commit-filter` is tricky because we need to actually commit. The default commit-filter is `git commit-tree \"$@\"`. If we want to skip, we return 1? Actually the docs say: \"If the filter exits with a non-zero status, the commit is not made.\" But we need to map old commits to new commits.\n\nHmm. Actually, to drop commits, the standard way in filter-branch is `--index-filter` or `--parent-filter`. `--parent-filter` rewrites parent list. We can use `--parent-filter` to remove parents that are not in our kept set, but it's complex.\n\nSimpler approach using `git rebase`:\n1. In the new clone, create a new orphan branch at ae4ebe6's tree? No.\n\nActually, the simplest robust approach: cherry-pick all commits from ae4ebe6 to HEAD onto a new orphan branch, using a script that preserves dates and sets author.\n\nSteps:\n1. Clone original repo to new folder (non-bare)\n2. Create orphan branch `new_main`\n3. Get list: `git rev-list --reverse ae4ebe6..HEAD` plus ae4ebe6 itself\n4. For each commit in order:\n - `git cherry-pick --no-commit `\n - `git commit --author=\"Aiden Huang \" --date=\"$(git show -s --format=%ai )\"`\n - But this uses current date for committer, which is fine. We want to preserve author date.\n \nWait, `git commit --date` sets the author date. So:\n```\ngit -c user.name=\"Aiden Huang\" -c user.email=\"huang.a.aiden@gmail.com\" \\\n commit --date=\"$(git show -s --format=%ai )\" \\\n -m \"$(git show -s --format=%B )\"\n```\n\nBut cherry-pick --no-commit applies the patch to the index. Then we commit with the original message, original author date, and new author. This should work!\n\nHowever, cherry-pick may fail on merge conflicts. Since we're cherry-picking from the same repo linear history, there should be no conflicts (each commit is applied in order). But cherry-pick might complain about empty commits or already-applied changes. We can use `git cherry-pick --no-commit --strategy=recursive -X theirs ` or handle empty commits.\n\nActually, since these are sequential commits from the same branch, `git cherry-pick` should apply cleanly. But there's a subtle issue: if the original commits include merge commits, cherry-pick may not handle them. Looking at the history, it's linear from ae4ebe6 to a384010, so no merges.\n\nLet's verify the linearity of main from ae4ebe6 to HEAD.","created_at":"2026-07-18T07:25:47.249540Z","finish_reason":"tool_calls","metrics":{"ttft_ms":977,"total_time_ms":3521,"input_tokens":797,"output_tokens":1029,"cache_read_tokens":87680,"cache_creation_tokens":null,"tpot_ms":2.4723032069970845,"tokens_per_sec":404.4811320754717}},{"message_id":"c2b27b09-7f02-42d1-b8ed-b034303e170a","node_id":123,"role":"tool","content":"Output from command in shell 65c48e:\n73d51c5406d792be622f3f95f1966ad88aa022d4\n69f1942a12bb3da7f23344a66553b373cdb9d6b5\ncdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf\naf5cad28eab44a9fe173e2b1cd247a24350fce54\n2d3c969ff8eb8d66439ccf8ddb9681348e90921e\n36da088be82ef9446ee73f2b540fd9998d59309f\n6a6e7f3ea89d16a3f0f7af679151d6a8cbbf9936\n89f9dcac380a87f97e5b6b195e05116bf99b56fd\n6a4ece3c43b392e4796f95d0b19f75ba45910ac1\nf423b4ca162bdde6caaa555ea0b7592795f62ae1\na3840102709aacd83fbf03498c8b5a696c58f5c8\n---\n73d51c5406d792be622f3f95f1966ad88aa022d4\n69f1942a12bb3da7f23344a66553b373cdb9d6b5\ncdaf3a63cc8cdc3558f9738e5b7efbeb1cc53ebf\naf5cad28eab44a9fe173e2b1cd247a24350fce54\n2d3c969ff8eb8d66439ccf8ddb9681348e90921e\n36da088be82ef9446ee73f2b540fd9998d59309f\n6a6e7f3ea89d16a3f0f7af679151d6a8cbbf9936\n89f9dcac380a87f97e5b6b195e05116bf99b56fd\n6a4ece3c43b392e4796f95d0b19f75ba45910ac1\nf423b4ca162bdde6caaa555ea0b7592795f62ae1\na3840102709aacd83fbf03498c8b5a696c58f5c8\n\n\nExit code: 0","created_at":"2026-07-18T07:25:47.304548Z","finish_reason":null,"metrics":null},{"message_id":"87b92373-cb04-4dfe-9331-6c23bf11daaa","node_id":125,"role":"assistant","content":"","tool_calls":[{"id":"functions.todo_write:45","name":"todo_write","arguments":{"todos":[{"content":"Create new directory and clone original repo","status":"in_progress"},{"content":"Rewrite history to start at ae4ebe6 and drop earlier commits","status":"pending"},{"content":"Rewrite author/committer to Aiden Huang preserving dates","status":"pending"},{"content":"Verify new history and clean up","status":"pending"}]}}],"thinking":"The list from ae4ebe6..HEAD is 12 commits, not including ae4ebe6 itself. We need to include ae4ebe6 too. So the full list is ae4ebe6 plus those 12 = 13 commits.\n\nThe main branch from ae4ebe6 to a384010 is linear. Good.\n\nNow, plan:\n1. Create new directory /Users/root1/Downloads/projects/reverse-llm-rewrite\n2. Initialize or clone the original\n3. Create a new orphan branch\n4. Cherry-pick ae4ebe6 then the 12 commits, with author Aiden Huang and original dates\n\nApproach using git filter-branch might actually be simpler. Let me think about filter-branch:\n\n```\ngit filter-branch --env-filter '\n export GIT_AUTHOR_NAME=\"Aiden Huang\"\n export GIT_AUTHOR_EMAIL=\"huang.a.aiden@gmail.com\"\n export GIT_COMMITTER_NAME=\"Aiden Huang\"\n export GIT_COMMITTER_EMAIL=\"huang.a.aiden@gmail.com\"\n' --parent-filter '\n # drop parents before ae4ebe6\n' ...\n```\n\nThe parent-filter approach is complex. Better to use filter-branch to just change author, and use rebase to drop commits. But we can combine: first rebase to drop commits, then filter-branch to change author.\n\nActually, to make ae4ebe6 the root and keep descendants, we can do:\n```\ngit checkout --orphan new_main ae4ebe6\n# But this only has ae4ebe6's tree, no history\n```\n\nThen we need to replay commits. The cherry-pick approach is cleaner.\n\nBut wait, there's an even simpler approach using `git rebase --onto`:\n```\ngit checkout -b new_main ae4ebe6\ngit rebase --onto ~1 new_main\n```\nBut we need a new root commit. We can create an empty root commit, then rebase ae4ebe6..HEAD onto it. But that adds an extra empty commit at the root.\n\nOr:\n```\ngit checkout --orphan new_main\ngit rm -rf .\ngit commit --allow-empty -m \"root\"\ngit rebase --onto new_main ae4ebe6~1 main\n```\nThis makes an empty root, then rebases all commits from ae4ebe6 onward onto it. But the original ae4ebe6 parent becomes new_main (empty root). Then we can delete new_main label? No, the empty root remains in history.\n\nActually, we don't need an empty root. We can use `git rebase -i --root` and drop all commits before ae4ebe6. ae4ebe6 will become the new root. Then we use filter-branch to change author.\n\nInteractive rebase needs an editor. We can set GIT_SEQUENCE_EDITOR to a script that edits the todo list. That's doable.\n\nPlan:\n1. Clone original to new directory\n2. `git checkout main`\n3. Create a script that transforms the interactive rebase todo:\n - Drop all commits before ae4ebe6\n - Mark all remaining commits with `exec git commit --amend --author=\"Aiden Huang \" --no-edit`\n4. Run `GIT_SEQUENCE_EDITOR=