metadata
license: apache-2.0
datasets:
- microsoft/rStar-Coder
language:
- en
base_model: prithivMLmods/rStar-Coder-Qwen3-0.6B
pipeline_tag: text-generation
library_name: mlx
tags:
- text-generation-inference
- chain-of-thought
- trl
- vllm
- coder
- code
- core
- python
- math
- gspo
- mlx
rStar-Coder-Qwen3-0.6B-q8-hi-mlx
Performance evaluation
21194/21194 [16:09<00:00, 21.85it/s]
arc_challenge
acc 0.280, norm 0.300, stderr 0.013
arc_easy
acc 0.416, norm 0.377, 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 0.184, norm 0.342, stderr 0.021
piqa
acc 0.651, norm 0.652, stderr 0.011
winogrande
acc 0.532, norm 0.532, stderr 0.014
Performance evaluation of the parent model at BF16
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-q8-hi-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-q8-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)