README.md

metadata

license: mit
datasets:
  - internlm/SWE-Fixer-Eval
  - internlm/SWE-Fixer-Train-110K
base_model: internlm/SWE-Fixer-Editor-72B
pipeline_tag: text-generation
tags:
  - code
  - mlx

mlx-community/SWE-Fixer-Editor-72B-4bit

The Model mlx-community/SWE-Fixer-Editor-72B-4bit was converted to MLX format from internlm/SWE-Fixer-Editor-72B using mlx-lm version 0.21.1.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("mlx-community/SWE-Fixer-Editor-72B-4bit")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)