Instructions to use carlmuck/Star-Command-R-32B-v1-mlx-4Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use carlmuck/Star-Command-R-32B-v1-mlx-4Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Star-Command-R-32B-v1-mlx-4Bit carlmuck/Star-Command-R-32B-v1-mlx-4Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
File size: 899 Bytes
5c4de2d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | ---
license: other
base_model: TheDrummer/Star-Command-R-32B-v1
tags:
- mlx
---
# carlmuck/Star-Command-R-32B-v1-mlx-4Bit
The Model [carlmuck/Star-Command-R-32B-v1-mlx-4Bit](https://huggingface.co/carlmuck/Star-Command-R-32B-v1-mlx-4Bit) was converted to MLX format from [TheDrummer/Star-Command-R-32B-v1](https://huggingface.co/TheDrummer/Star-Command-R-32B-v1) 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("carlmuck/Star-Command-R-32B-v1-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)
```
|