Instructions to use mlx-community/UTENA-7B-NSFW-V2-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/UTENA-7B-NSFW-V2-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir UTENA-7B-NSFW-V2-4bit mlx-community/UTENA-7B-NSFW-V2-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
mlx-community/UTENA-7B-NSFW-V2-4bit
This model was converted to MLX format from AI-B/UTENA-7B-NSFW-V2.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/UTENA-7B-NSFW-V2-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
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Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard63.310
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard84.540
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard63.970
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard47.810
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard78.690
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard42.380