mirror of
https://github.com/ml-explore/mlx-examples.git
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854ad8747a
* feat(mlx-lm): add de-quant for fuse * chore: disable quant in to linear when de-quant enabled * chore: add better error handling for adapter file not found
106 lines
2.8 KiB
Python
106 lines
2.8 KiB
Python
import argparse
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import glob
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import json
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import shutil
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from pathlib import Path
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from typing import Any, Dict, Union
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from mlx.utils import tree_flatten, tree_unflatten
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from .tuner.lora import LoRALinear
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from .tuner.utils import apply_lora_layers, dequantize
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from .utils import fetch_from_hub, get_model_path, save_weights, upload_to_hub
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def parse_arguments() -> argparse.Namespace:
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parser = argparse.ArgumentParser(description="LoRA or QLoRA finetuning.")
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parser.add_argument(
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"--model",
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default="mlx_model",
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help="The path to the local model directory or Hugging Face repo.",
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)
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parser.add_argument(
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"--save-path",
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default="lora_fused_model",
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help="The path to save the fused model.",
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)
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parser.add_argument(
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"--adapter-file",
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type=str,
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default="adapters.npz",
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help="Path to the trained adapter weights (npz or safetensors).",
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)
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parser.add_argument(
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"--hf-path",
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type=str,
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default=None,
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help="Path to the original Hugging Face model. Required for upload if --model is a local directory.",
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)
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parser.add_argument(
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"--upload-repo",
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help="The Hugging Face repo to upload the model to.",
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type=str,
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default=None,
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)
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parser.add_argument(
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"--de-quantize",
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help="Generate a de-quantized model.",
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action="store_true",
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)
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return parser.parse_args()
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def main() -> None:
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print("Loading pretrained model")
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args = parse_arguments()
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model_path = get_model_path(args.model)
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model, config, tokenizer = fetch_from_hub(model_path)
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model.freeze()
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model = apply_lora_layers(model, args.adapter_file)
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fused_linears = [
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(n, m.to_linear())
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for n, m in model.named_modules()
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if isinstance(m, LoRALinear)
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]
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model.update_modules(tree_unflatten(fused_linears))
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if args.de_quantize:
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print("De-quantizing model")
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model = dequantize(model)
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weights = dict(tree_flatten(model.parameters()))
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save_path = Path(args.save_path)
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save_weights(save_path, weights)
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py_files = glob.glob(str(model_path / "*.py"))
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for file in py_files:
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shutil.copy(file, save_path)
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tokenizer.save_pretrained(save_path)
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if args.de_quantize:
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config.pop("quantization", None)
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with open(save_path / "config.json", "w") as fid:
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json.dump(config, fid, indent=4)
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if args.upload_repo is not None:
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hf_path = args.hf_path or (
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args.model if not Path(args.model).exists() else None
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)
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if hf_path is None:
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raise ValueError(
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"Must provide original Hugging Face repo to upload local model."
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)
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upload_to_hub(args.save_path, args.upload_repo, hf_path)
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if __name__ == "__main__":
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main()
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