mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-25 01:41:19 +08:00
50e5ca81a8
* Adding full model weights finetuning * Updating the LORA.md and ACKNOWLEDGMENTS.md files. * removing --use-dora and --fulll-training and adding --fine-tune-type * some clean up * reformating and fixing dora training * updated CONFIG_DEFAULTS * update config example * update in the config example fie * Update LORA.md * merge and commit * adding argument for dora linear layer * clean up * clean up in the example yaml file * fix * final fix before sending * small addition to re md file * fix for loading the fully trained model by saving all the files and configs correctly * clean up * removing the unnesesairy files * changing lora layers back to 16 * removed max file size * nits * resolve merge * some consistency changes --------- Co-authored-by: Awni Hannun <awni@apple.com>
131 lines
3.6 KiB
Python
131 lines
3.6 KiB
Python
import argparse
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import glob
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import shutil
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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 .tuner.dora import DoRAEmbedding, DoRALinear
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from .tuner.lora import LoRAEmbedding, LoRALinear, LoRASwitchLinear
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from .tuner.utils import dequantize, load_adapters
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from .utils import (
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fetch_from_hub,
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get_model_path,
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save_config,
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save_weights,
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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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"--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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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_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 = load_adapters(model, args.adapter_path)
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fused_linears = [
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(n, m.fuse()) for n, m in model.named_modules() 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.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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save_config(config, config_path=save_path / "config.json")
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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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convert_to_gguf(model_path, weights, config, str(save_path / args.gguf_path))
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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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