31ddbd7806
* feat: add example for deepseek coder * chore: remove hardcoded rope_scaling_factor * feat: add quantization support * chore: update readme * chore: clean up the rope scalling factor param in create cos sin theta * feat: add repetition_penalty * style /consistency changes to ease future integration * nits in README * one more typo --------- Co-authored-by: Awni Hannun <awni@apple.com> |
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| .. | ||
| convert.py | ||
| deepseek_coder.py | ||
| README.md | ||
| requirements.txt | ||
Deepseek Coder
Deepseek Coder is a family of code generating language models based on the Llama architecture.1 The models were trained from scratch on a corpus of 2T tokens, with a composition of 87% code and 13% natural language containing both English and Chinese.
Setup
Install the dependencies:
pip install -r requirements.txt
Next, download and convert the model.
python convert.py --hf-path <path_to_huggingface_model>
To generate a 4-bit quantized model, use -q. For a full list of options run:
python convert.py --help
The converter downloads the model from Hugging Face. The default model is
deepseek-ai/deepseek-coder-6.7b-instruct. Check out the Hugging Face
page to see a list of available models.
By default, the conversion script will save the converted weights.npz,
tokenizer, and config.json in the mlx_model directory.
Run
Once you've converted the weights, you can interact with the Deepseek coder model:
python deepseek_coder.py --prompt "write a quick sort algorithm in python."