Instructions to use mlx-community/DeepSeek-R1-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/DeepSeek-R1-3bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir DeepSeek-R1-3bit mlx-community/DeepSeek-R1-3bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
| base_model: deepseek-ai/DeepSeek-R1 | |
| tags: | |
| - mlx | |
| # mlx-community/DeepSeek-R1-3bit | |
| The Model [mlx-community/DeepSeek-R1-3bit](https://huggingface.co/mlx-community/DeepSeek-R1-3bit) was | |
| converted to MLX format from [deepseek-ai/DeepSeek-R1](https://huggingface.co/deepseek-ai/DeepSeek-R1) | |
| using mlx-lm version **0.21.0**. | |
| ## Use with mlx | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ```python | |
| from mlx_lm import load, generate | |
| model, tokenizer = load("mlx-community/DeepSeek-R1-3bit") | |
| prompt = "hello" | |
| if tokenizer.chat_template is not None: | |
| messages = [{"role": "user", "content": prompt}] | |
| prompt = tokenizer.apply_chat_template( | |
| messages, add_generation_prompt=True | |
| ) | |
| response = generate(model, tokenizer, prompt=prompt, verbose=True) | |
| ``` | |