How to use from the
Use from the
MLX library
# 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)

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"
Downloads last month
118
MLX
Hardware compatibility
Log In to add your hardware

Quantized

Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐Ÿ™‹ Ask for provider support