Instructions to use sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir deepseek-coder-33b-instruct-mlx-4Bit sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
File size: 1,005 Bytes
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license: other
license_name: deepseek
license_link: LICENSE
tags:
- mlx
base_model: deepseek-ai/deepseek-coder-33b-instruct
---
# sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit
The Model [sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit](https://huggingface.co/sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit) was converted to MLX format from [deepseek-ai/deepseek-coder-33b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-33b-instruct) using mlx-lm version **0.31.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("sleepy186247/deepseek-coder-33b-instruct-mlx-4Bit")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
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