README.md

metadata

datasets:
  - nvidia/OpenCodeReasoning-2
  - GetSoloTech/Code-Reasoning
base_model: GetSoloTech/GPT-OSS-Code-Reasoning-20B
library_name: mlx
tags:
  - code-reasoning
  - coding
  - reasoning
  - problem-solving
  - algorithms
  - python
  - c++
  - competitive-programming
  - vllm
  - mlx
pipeline_tag: text-generation

GPT-OSS-Code-Reasoning-20B-qx86-hi-mlx

This model GPT-OSS-Code-Reasoning-20B-qx86-hi-mlx was converted to MLX format from GetSoloTech/GPT-OSS-Code-Reasoning-20B using mlx-lm version 0.26.4.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("GPT-OSS-Code-Reasoning-20B-qx86-hi-mlx")

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)