Instructions to use ekryski/starcoder2-3b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ekryski/starcoder2-3b-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir starcoder2-3b-4bit ekryski/starcoder2-3b-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
File size: 877 Bytes
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license: apache-2.0
base_model: bigcode/starcoder2-3b
language:
- en
tags:
- mlx
- ffai
- quantized
- 4bit
- affine
---
# starcoder2-3b-4bit
4-bit affine quantization of [bigcode/starcoder2-3b](https://huggingface.co/bigcode/starcoder2-3b), produced with [FFAI](https://github.com/thewafflehaus/FFAI) 0.1.0's `ffai convert` (mlx-affine format, `group_size=64`).
## Conversion
```bash
ffai convert bigcode/starcoder2-3b --bits 4 \
--upload-repo ekryski/starcoder2-3b-4bit
```
## See also
- [FFAI](https://github.com/thewafflehaus/FFAI) — fast Apple Silicon LLM inference. `Model.load("ekryski/starcoder2-3b-4bit")` runs this checkpoint end-to-end.
- [FFAI quickstart](https://github.com/thewafflehaus/FFAI/blob/main/documentation/quickstart.md)
- [FFAI quantization docs](https://github.com/thewafflehaus/FFAI/blob/main/documentation/quantization.md) |