Code
How jingyaogong/minimind is laid out, and where to start for common changes.
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Where to start reading
- Repository root
- Likely public API
- Likely public API
- Suggested starting pointAI
- Suggested starting pointAI
- Suggested starting pointAI
- Suggested starting pointAI
- Suggested starting pointAI
Find files for a task
AIAdd 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
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- Files marked “likely” are guessed from their names.