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Use config.json, add model_type (#157)
* Use config.json, add model_type * Update convert to generate config.json
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@ -1,6 +1,7 @@
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# Copyright © 2023 Apple Inc.
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import argparse
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import json
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import numpy as np
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from pathlib import Path
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import torch
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@ -22,3 +23,10 @@ if __name__ == "__main__":
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str(model_path / "weights.npz"),
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**{k: v.to(torch.float16).numpy() for k, v in state.items()}
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)
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# Save config.json with model_type
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with open(model_path / "params.json", "r") as f:
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config = json.loads(f.read())
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config["model_type"] = "mistral"
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with open(model_path / "config.json", "w") as f:
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json.dump(config, f, indent=4)
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@ -192,9 +192,10 @@ class Tokenizer:
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def load_model(folder: str, dtype=mx.float16):
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model_path = Path(folder)
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tokenizer = Tokenizer(str(model_path / "tokenizer.model"))
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with open(model_path / "params.json", "r") as f:
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with open(model_path / "config.json", "r") as f:
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config = json.loads(f.read())
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config.pop("sliding_window")
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config.pop("sliding_window", None)
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config.pop("model_type", None)
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model_args = ModelArgs(**config)
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weights = mx.load(str(model_path / "weights.npz"))
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weights = tree_unflatten(list(weights.items()))
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