Instructions to use reach-vb/mistral-lora-mlx-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use reach-vb/mistral-lora-mlx-test 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("reach-vb/mistral-lora-mlx-test") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use reach-vb/mistral-lora-mlx-test with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "reach-vb/mistral-lora-mlx-test" --prompt "Once upon a time"
reach-vb/mistral-lora-mlx-test
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 reach-vb/mistral-lora-mlx-test --prompt "My name is"
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