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Copy file name to clipboardExpand all lines: README.md
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To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face.
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Note: `convert.py` has been moved to `examples/convert-no-torch.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives.
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Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives.
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It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face.
Copy file name to clipboardExpand all lines: docs/HOWTO-add-model.md
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### 1. Convert the model to GGUF
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This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
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Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-no-torch.py](../examples/convert-no-torch.py) (for `llama/llama2` models in `.pth` format).
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Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format).
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The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
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