Instructions to use mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit --local-dir SFR-Iterative-DPO-LLaMA-3-8B-R-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
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Download README.md from mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit: direct link, hf CLI and curl.
- Browser
- Download file 655 Bytes
-
https://huggingface.co/mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit/resolve/main/README.md
- Command line
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hf download hf://mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit/README.md
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curl -L -o README.md https://huggingface.co/mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit/resolve/main/README.md
655 Bytes
metadata
license: cc-by-nc-nd-3.0
tags:
- mlx
mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit
The Model mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit was converted to MLX format from Salesforce/SFR-Iterative-DPO-LLaMA-3-8B-R using mlx-lm version 0.13.0.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)