72 lines
1.4 KiB
Markdown
72 lines
1.4 KiB
Markdown
---
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license: apache-2.0
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datasets:
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- microsoft/rStar-Coder
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language:
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- en
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base_model: prithivMLmods/rStar-Coder-Qwen3-0.6B
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pipeline_tag: text-generation
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library_name: mlx
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tags:
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- text-generation-inference
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- chain-of-thought
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- trl
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- vllm
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- coder
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- code
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- core
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- python
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- math
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- gspo
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- mlx
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---
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# rStar-Coder-Qwen3-0.6B-bf16-mlx
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Performance evaluation:
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```bash
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21194/21194 [23:40<00:00, 14.92it/s]
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arc_challenge
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acc 0.279, norm 0.299, stderr 0.013
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arc_easy
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acc 0.420, norm 0.379, stderr 0.009
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boolq
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acc 0.378, norm 0.378, stderr 0.008
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hellaswag
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acc 0.366, norm 0.434, stderr 0.004
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openbookqa
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acc 0.186, norm 0.344, stderr 0.021
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piqa
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acc 0.656, norm 0.655, stderr 0.014
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winogrande
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acc 0.524, norm 0.524, stderr 0.014
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```
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This model [rStar-Coder-Qwen3-0.6B-bf16-mlx](https://huggingface.co/rStar-Coder-Qwen3-0.6B-bf16-mlx) was
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converted to MLX format from [prithivMLmods/rStar-Coder-Qwen3-0.6B](https://huggingface.co/prithivMLmods/rStar-Coder-Qwen3-0.6B)
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using mlx-lm version **0.26.3**.
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## Use with mlx
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```bash
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pip install mlx-lm
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```
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("rStar-Coder-Qwen3-0.6B-bf16-mlx")
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prompt = "hello"
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if tokenizer.chat_template is not None:
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True
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)
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response = generate(model, tokenizer, prompt=prompt, verbose=True)
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```
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