metadata

base_model: mlx-community/Mistral-7B-Instruct-v0.2-4bit
license: apache-2.0
pipeline_tag: text-generation
tags:
  - finetuned
  - mlx
  - mlx
inference: true
widget:
  - messages:
      - role: user
        content: What is your favorite condiment?

AravD/Mistral-7B-Instruct-PaulAI

The Model AravD/Mistral-7B-Instruct-PaulAI was converted to MLX format from mlx-community/Mistral-7B-Instruct-v0.2-4bit using mlx-lm version 0.19.0.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("AravD/Mistral-7B-Instruct-PaulAI")

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)