Awni Hannun
112 lines
2.9 KiB
Python
112 lines
2.9 KiB
Python
import argparse
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from pathlib import Path
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from mlx.utils import tree_flatten, tree_unflatten
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from .gguf import convert_to_gguf
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from .utils import (
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dequantize_model,
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load,
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save,
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upload_to_hub,
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)
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def parse_arguments() -> argparse.Namespace:
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parser = argparse.ArgumentParser(
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description="Fuse fine-tuned adapters into the base model."
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)
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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="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-path",
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type=str,
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default="adapters",
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help="Path to the trained adapter weights and config.",
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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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"--dequantize",
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help="Generate a dequantized model.",
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action="store_true",
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)
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parser.add_argument(
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"--export-gguf",
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help="Export model weights in GGUF format.",
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action="store_true",
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)
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parser.add_argument(
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"--gguf-path",
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help="Path to save the exported GGUF format model weights. Default is ggml-model-f16.gguf.",
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default="ggml-model-f16.gguf",
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type=str,
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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, tokenizer, config = load(
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args.model, adapter_path=args.adapter_path, return_config=True
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)
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fused_linears = [
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(n, m.fuse(dequantize=args.dequantize))
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for n, m in model.named_modules()
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if hasattr(m, "fuse")
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]
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if fused_linears:
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model.update_modules(tree_unflatten(fused_linears))
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if args.dequantize:
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print("Dequantizing model")
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model = dequantize_model(model)
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config.pop("quantization", None)
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config.pop("quantization_config", None)
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save_path = Path(args.save_path)
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save(
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save_path,
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args.model,
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model,
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tokenizer,
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config,
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donate_model=False,
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)
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if args.export_gguf:
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model_type = config["model_type"]
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if model_type not in ["llama", "mixtral", "mistral"]:
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raise ValueError(
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f"Model type {model_type} not supported for GGUF conversion."
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)
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weights = dict(tree_flatten(model.parameters()))
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convert_to_gguf(save_path, weights, config, str(save_path / args.gguf_path))
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if args.upload_repo is not None:
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upload_to_hub(args.save_path, args.upload_repo)
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if __name__ == "__main__":
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print(
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"Calling `python -m mlx_lm.fuse...` directly is deprecated."
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" Use `mlx_lm.fuse...` or `python -m mlx_lm fuse ...` instead."
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)
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main()
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