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* examples : add model conversion tool/example This commit adds an "example/tool" that is intended to help in the process of converting models to GGUF. Currently it supports normal causal models and embedding models. The readme contains instructions and command to guide through the process. The motivation for this to have a structured and repeatable process for model conversions and hopefully with time improve upon it to make the process easier and more reliable. We have started to use this for new model conversions internally and will continue doing so and improve it as we go along. Perhaps with time this should be placed in a different directory than the examples directory, but for now it seems like a good place to keep it while we are still developing it. * squash! examples : add model conversion tool/example Remove dependency on scikit-learn in model conversion example. * squash! examples : add model conversion tool/example Update transformer dep to use non-dev version. And also import `AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility with the latest version. * squash! examples : add model conversion tool/example Remove the logits requirements file from the all requirements file.
67 lines
2.3 KiB
Python
Executable file
67 lines
2.3 KiB
Python
Executable file
#!/usr/bin/env python3
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import argparse
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import os
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import json
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from safetensors import safe_open
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from collections import defaultdict
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parser = argparse.ArgumentParser(description='Process model with specified path')
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parser.add_argument('--model-path', '-m', help='Path to the model')
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args = parser.parse_args()
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model_path = os.environ.get('MODEL_PATH', args.model_path)
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if model_path is None:
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parser.error("Model path must be specified either via --model-path argument or MODEL_PATH environment variable")
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# Check if there's an index file (multi-file model)
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index_path = os.path.join(model_path, "model.safetensors.index.json")
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single_file_path = os.path.join(model_path, "model.safetensors")
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if os.path.exists(index_path):
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# Multi-file model
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print("Multi-file model detected")
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with open(index_path, 'r') as f:
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index_data = json.load(f)
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# Get the weight map (tensor_name -> file_name)
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weight_map = index_data.get("weight_map", {})
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# Group tensors by file for efficient processing
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file_tensors = defaultdict(list)
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for tensor_name, file_name in weight_map.items():
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file_tensors[file_name].append(tensor_name)
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print("Tensors in model:")
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# Process each shard file
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for file_name, tensor_names in file_tensors.items():
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file_path = os.path.join(model_path, file_name)
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print(f"\n--- From {file_name} ---")
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with safe_open(file_path, framework="pt") as f:
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for tensor_name in sorted(tensor_names):
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tensor = f.get_tensor(tensor_name)
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print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
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elif os.path.exists(single_file_path):
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# Single file model (original behavior)
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print("Single-file model detected")
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with safe_open(single_file_path, framework="pt") as f:
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keys = f.keys()
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print("Tensors in model:")
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for key in sorted(keys):
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tensor = f.get_tensor(key)
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print(f"- {key} : shape = {tensor.shape}, dtype = {tensor.dtype}")
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else:
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print(f"Error: Neither 'model.safetensors.index.json' nor 'model.safetensors' found in {model_path}")
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print("Available files:")
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if os.path.exists(model_path):
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for item in sorted(os.listdir(model_path)):
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print(f" {item}")
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else:
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print(f" Directory {model_path} does not exist")
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exit(1)
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