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* LoRA：Refactor TrainingCallback to enhance flexibility and extensibility This commit refactors the TrainingCallback class to accept a dictionary parameter for both on_train_loss_report and on_val_loss_report methods. By switching from multiple parameters to a single dict parameter, this change significantly improves the class's flexibility and makes it easier to extend with new training or validation metrics in the future without altering the method signatures. This approach simplifies the addition of new information to be logged or processed and aligns with best practices for scalable and maintainable code design. * LoRA: Add printing and callbacks for learning rate during training |
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| .. | ||
| examples | ||
| models | ||
| tuner | ||
| __init__.py | ||
| convert.py | ||
| fuse.py | ||
| generate.py | ||
| LORA.md | ||
| lora.py | ||
| MERGE.md | ||
| merge.py | ||
| py.typed | ||
| README.md | ||
| requirements.txt | ||
| SERVER.md | ||
| server.py | ||
| UPLOAD.md | ||
| utils.py | ||
Generate Text with MLX and 🤗 Hugging Face
This an example of large language model text generation that can pull models from the Hugging Face Hub.
For more information on this example, see the README in the parent directory.
This package also supports fine tuning with LoRA or QLoRA. For more information see the LoRA documentation.