Instructions to use splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Mixtral-8x7B-Instruct-v0.1-mlx-6Bit splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit
The Model splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit was converted to MLX format from mistralai/Mixtral-8x7B-Instruct-v0.1 using mlx-lm version 0.29.1.
Use with mlx
pip install mlx-lm
from mlx_lm import load, generate
model, tokenizer = load("splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit")
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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Model size
47B params
Tensor type
BF16
·
U32 ·
Hardware compatibility
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6-bit
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Model tree for splats/Mixtral-8x7B-Instruct-v0.1-mlx-6Bit
Base model
mistralai/Mixtral-8x7B-v0.1 Finetuned
mistralai/Mixtral-8x7B-Instruct-v0.1