license, datasets, language, base_model, pipeline_tag, library_name, tags

license	datasets	language	base_model	pipeline_tag	library_name	tags
apache-2.0	microsoft/rStar-Coder	en	prithivMLmods/rStar-Coder-Qwen3-0.6B	text-generation	mlx	text-generation-inference chain-of-thought trl vllm coder code core python math gspo mlx

rStar-Coder-Qwen3-0.6B-bf16-mlx

Performance evaluation:

21194/21194 [23:40<00:00, 14.92it/s]

arc_challenge
acc 0.279, norm 0.299, stderr 0.013
arc_easy
acc 0.420, norm 0.379, stderr 0.009
boolq
acc 0.378, norm 0.378, stderr 0.008
hellaswag
acc 0.366, norm 0.434, stderr 0.004
openbookqa
acc 0.186, norm 0.344, stderr 0.021
piqa
acc 0.656, norm 0.655, stderr 0.014
winogrande
acc 0.524, norm 0.524, stderr 0.014

This model rStar-Coder-Qwen3-0.6B-bf16-mlx was converted to MLX format from prithivMLmods/rStar-Coder-Qwen3-0.6B using mlx-lm version 0.26.3.

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("rStar-Coder-Qwen3-0.6B-bf16-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)