Instructions to use mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized 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-unquantized with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized
The Model mlx-community/SFR-Iterative-DPO-LLaMA-3-8B-R-unquantized 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-unquantized")
response = generate(model, tokenizer, prompt="hello", verbose=True)
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