README.md

metadata

license: mit
library_name: mlx
datasets:
  - R2E-Gym/R2E-Gym-Subset
language:
  - en
base_model: agentica-org/DeepSWE-Preview
pipeline_tag: text-generation
tags:
  - mlx

WaveCut/DeepSWE-Preview_MLX-8bit

This model WaveCut/DeepSWE-Preview_MLX-8bit was converted to MLX format from agentica-org/DeepSWE-Preview using mlx-lm version 0.25.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("WaveCut/DeepSWE-Preview_MLX-8bit")

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)