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3cf436b529
* testing quantization * conversion + quantization working * one config processor * quantization in mistral / nits in llama * args for quantization * llama / mistral conversion in good shape * phi2 quantized * mixtral * qwen conversion
66 lines
2.0 KiB
Markdown
66 lines
2.0 KiB
Markdown
# Llama
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An example of generating text with Llama (1 or 2) using MLX.
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Llama is a set of open source language models from Meta AI Research[^1][^2]
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ranging from 7B to 70B parameters. This example also supports Meta's Llama Chat
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and Code Llama models, as well as the 1.1B TinyLlama models from SUTD.[^3]
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### Setup
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Install the dependencies:
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```
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pip install -r requirements.txt
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```
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Next, download and convert the model. If you do not have access to the model
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weights you will need to request access from Meta:
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- [Request Llama v1](https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform)
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- [Request Llama v2](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
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> [!TIP] Alternatively, you can also download a few converted checkpoints from
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> the [MLX Community](https://huggingface.co/mlx-community) organization on
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> Hugging Face and skip the conversion step.
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You can download the TinyLlama models directly from [Hugging
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Face](https://huggingface.co/TinyLlama).
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Convert the weights with:
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```
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python convert.py --torch-path <path_to_torch_model>
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```
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To generate a 4-bit quantized model use the `-q` flag:
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```
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python convert.py --torch-path <path_to_torch_model> -q
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```
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For TinyLlama use
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```
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python convert.py --torch-path <path_to_torch_model> --model-name tiny_llama
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```
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By default, the conversion script will make the directory `mlx_model` and save
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the converted `weights.npz`, `tokenizer.model`, and `config.json` there.
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### Run
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Once you've converted the weights to MLX format, you can interact with the
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LlamA model:
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```
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python llama.py --prompt "hello"
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```
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Run `python llama.py --help` for more details.
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[^1]: For Llama v1 refer to the [arXiv paper](https://arxiv.org/abs/2302.13971) and [blog post](https://ai.meta.com/blog/large-language-model-llama-meta-ai/) for more details.
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[^2]: For Llama v2 refer to the [blob post](https://ai.meta.com/llama/)
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[^3]: For TinyLlama refer to the [gihub repository](https://github.com/jzhang38/TinyLlama?tab=readme-ov-file)
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