Instructions to use mlx-community/Mistral-7B-v0.1-sql with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/Mistral-7B-v0.1-sql with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mlx-community/Mistral-7B-v0.1-sql") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use mlx-community/Mistral-7B-v0.1-sql with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mlx-community/Mistral-7B-v0.1-sql" --prompt "Once upon a time"
- Atomic Chat
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Download README.md from mlx-community/Mistral-7B-v0.1-sql: direct link, hf CLI and curl.
- Browser
- Download file 572 Bytes
-
https://huggingface.co/mlx-community/Mistral-7B-v0.1-sql/resolve/main/README.md
- Command line
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hf download hf://mlx-community/Mistral-7B-v0.1-sql/README.md
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curl -L -o README.md https://huggingface.co/mlx-community/Mistral-7B-v0.1-sql/resolve/main/README.md
572 Bytes
metadata
language:
- en
license: apache-2.0
tags:
- pretrained
- mlx
pipeline_tag: text-generation
inference:
parameters:
temperature: 0.7
Mistral-7B-v0.1-sql
This model was converted to MLX format from mistralai/Mistral-7B-v0.1.
Refer to the original model card for more details on the model.
Use with mlx
pip install mlx
git clone https://github.com/ml-explore/mlx-examples.git
cd mlx-examples/llms/hf_llm
python generate.py --model mlx-community/Mistral-7B-v0.1-sql --prompt "My name is"