Instructions to use mlx-community/Mistral-Nemo-Instruct-2407-3bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Mistral-Nemo-Instruct-2407-3bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Mistral-Nemo-Instruct-2407-3bit mlx-community/Mistral-Nemo-Instruct-2407-3bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
mlx-community/Mistral-Nemo-Instruct-2407-q3
The Model mlx-community/Mistral-Nemo-Instruct-2407-q3 was converted to MLX format from mistralai/Mistral-Nemo-Instruct-2407 using mlx-lm version 0.20.5.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Mistral-Nemo-Instruct-2407-q3")
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)
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Hardware compatibility
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3-bit
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Model tree for mlx-community/Mistral-Nemo-Instruct-2407-3bit
Base model
mistralai/Mistral-Nemo-Base-2407 Finetuned
mistralai/Mistral-Nemo-Instruct-2407