* 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
* Make it easier to know which file we have bad JSON data in.
* Use a loop rather than repeat code sections.
I previously had these as separate cut-n-drooled sections of code. This change makes it a clean loop.
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Small fix to previous code suggestion to restore a missing variable.
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
Mixtral models throw the following exception
```
Traceback (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/generate.py", line 119, in <module>
main(args)
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/generate.py", line 96, in main
model, tokenizer = load(args.model, tokenizer_config=tokenizer_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 278, in load
model = load_model(model_path)
^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 221, in load_model
model_class, model_args_class = _get_classes(config=config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/utils.py", line 46, in _get_classes
arch = importlib.import_module(f"mlx_lm.models.{model_type}")
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/importlib/__init__.py", line 126, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "<frozen importlib._bootstrap>", line 1204, in _gcd_import
File "<frozen importlib._bootstrap>", line 1176, in _find_and_load
File "<frozen importlib._bootstrap>", line 1147, in _find_and_load_unlocked
File "<frozen importlib._bootstrap>", line 690, in _load_unlocked
File "<frozen importlib._bootstrap_external>", line 940, in exec_module
File "<frozen importlib._bootstrap>", line 241, in _call_with_frames_removed
File "/opt/homebrew/anaconda3/lib/python3.11/site-packages/mlx_lm/models/mixtral.py", line 11, in <module>
@dataclass
^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1230, in dataclass
return wrap(cls)
^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1220, in wrap
return _process_class(cls, init, repr, eq, order, unsafe_hash,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 1027, in _process_class
_init_fn(all_init_fields,
File "/opt/homebrew/anaconda3/lib/python3.11/dataclasses.py", line 545, in _init_fn
raise TypeError(f'non-default argument {f.name!r} '
TypeError: non-default argument 'model_type' follows default argument
```
* LoRA: Improve validation error for LoRA layer count exceeding model layer
This commit enhances the error handling when the specified LoRA layer count exceeds the total number of layers in the model. It clarifies the error message to provide actionable feedback for users, guiding them to adjust their input parameters accordingly.
* format + nits
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* lazy model import in mlx_lm
* change lora loading
* fix olmo lora
* remove a bunch of unused stuff from plamo
* move phixtral to mlx-lm and out of llms/
* Add checkpoints directory for adapter weights
The code was modified to create a checkpoints directory if it doesn't exist yet. Adapter weights are now saved to this checkpoints directory during the training iterations.
Corrected indentation of Save adapter weights code because it was part of "if eval"
* Fixing a blank added by mistake
* update a few examples to use compile
* update mnist
* add compile to vae and rename some stuff for simplicity
* update reqs
* use state in eval
* GCN example with RNG + dropout
* add a bit of prefetching
* initial commit
* style fixes
* update of ACKNOWLEDGMENTS
* fixed comment
* minor refactoring; removed unused imports
* added cifar and cvae to top-level README.md
* removed mention of cuda/mps in argparse
* fixed training status output
* load_weights() with strict=True
* pretrained model update
* fixed imports and style
* requires mlx>=0.0.9
* updated with results using mlx 0.0.9
* removed mention of private repo
* simplify and combine to one file, more consistency with other exmaples
* few more nits
* nits
* spell
* format
---------
Co-authored-by: Awni Hannun <awni@apple.com>
* chore(mlx-lm): add model weight index in save_weights
* Update llms/mlx_lm/utils.py
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* Update llms/mlx_lm/utils.py
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
* chore: save total siZe as param size isntead of file size
* chore: clean up format
---------
Co-authored-by: Awni Hannun <awni.hannun@gmail.com>
A new argument "--max_seq_length" has been added to the command-line parser and passed as a parameter to the main function of the lora.py script. This allows users to specify and control the maximum sequence length during training.
* Add grad checkpointing and PE in the transformer example
* Remove other frameworks from LM example
* Remove the other frameworks from MNIST example
* Improve the transformer LM example
* Fix black and change LR
* Decoding results in garbled text when multiple tokens represent a single character (e.g., Chinese).
* Decoding results in garbled text when multiple tokens represent a single character (e.g., Chinese).
* LoRA: Remove unnecessary model type judgments
1. Supported models are already checked in the load_model function in utils, no need to repeat the check in lora
2. The checks in lora are not synchronized with those in utils
* LoRA: add LoRA supported models in mlx_lm utils
