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jingyaogong / minimind

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Code

How jingyaogong/minimind is laid out, and where to start for common changes.

There is no import map for this report yet.

Where to start reading

  1. Repository root
  2. Likely public API
  3. Likely public API
  4. Suggested starting pointAI
  5. Suggested starting pointAI
  6. Suggested starting pointAI
  7. Suggested starting pointAI
  8. Suggested starting pointAI

Find files for a task

AI

Add a new training pipeline

  • Import shared utilities and model from trainer_utils.py and model_minimind.py
  • Define training loop using dataset from lm_dataset.py with appropriate loss computation
  • Add wandb/swanlab logging following patterns in existing train_*.py files

Add model evaluation benchmark

  • Extend eval_llm.py with new benchmark dataset loader and scoring function
  • Reference model architecture from model_minimind.py for inference setup
  • Integrate tokenizer.json for consistent encoding across evaluations

Deploy model as API service

  • Use scripts/serve_openai_api.py as base template or extend scripts/chat_api.py
  • Load trained model checkpoint using model_minimind.py initialization
  • Configure tokenizer from model/tokenizer_config.json for request encoding
  • Reference existing FastAPI/Flask patterns from chat_api.py for endpoint design

Change the public API

RepoPilot lists up to 500 files, so a file missing here may still exist.

  • Files marked “likely” are guessed from their names.

Code quality and hotspots

Largest source files

  • Branch points13 per file on average
  • Code issues0 errors, 54 warnings
  • Largest file486 lines
  • Hotspot load1 high-risk file

Static checks on the 15 largest source files.

Tests, CI and tooling

  • CINot detected
  • TestsNot detected0.00 test files per source file (file count, not coverage)
  • Dependency updatesNo config detected

Hotspots

Files that change often and branch heavily, highest first. Branch points count if, loop, case, catch, ternary, && and || in the file.