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rStar-Coder-Qwen3-0.6B-GGUF
prithivMLmods/rStar-Coder-Qwen3-0.6B-GGUF
rStar-Coder-Qwen3-0.6B-GGUF is a text generation model from prithivMLmods. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
rStar-Coder-Qwen3-0.6B is a compact, multi-domain language model fine-tuned from Qwen-0.6B using the rStar-Coder dataset, which incorporates code expert clusters and an extended symbolic reasoning collection; it excel…
All-time downloads
3.5K
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Repo size
8.9 GB
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1
Public
Repo makeup
Click a slice to open those files.
.gguf8.9 GB · 100%
At a glance
- Task
- Text Generation
- Library
- transformers
- License
- apache-2.0
- Model type
- qwen3
- Access
- Public
- Created
- Aug 6, 2025
- Updated
- Aug 6, 2025
- SHA
- 05d36fcf
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Base models
Model card
From the Hugging Face model README
rStar-Coder-Qwen3-0.6B-GGUF
rStar-Coder-Qwen3-0.6B is a compact, multi-domain language model fine-tuned from Qwen-0.6B using the rStar-Coder dataset, which incorporates code expert clusters and an extended symbolic reasoning collection; it excels at unified reasoning across code, mathematics, and science, delivering advanced code generation, algorithm synthesis, multi-language error detection, and step-by-step scientific problem-solving, while supporting structured output in LaTeX, Markdown, JSON, CSV, and YAML—making it ideal for developers, educators, and researchers requiring efficient STEM-oriented AI on mid-range GPUs, offline clusters, and edge devices, with a focus on logic-driven responses and technical data generation rather than general chat or creative writing.
Model Files
| File Name | Size | Quant Type |
|---|---|---|
| rStar-Coder-Qwen3-0.6B.BF16.gguf | 1.2 GB | BF16 |
| rStar-Coder-Qwen3-0.6B.F16.gguf | 1.2 GB | F16 |
| rStar-Coder-Qwen3-0.6B.F32.gguf | 2.39 GB | F32 |
| rStar-Coder-Qwen3-0.6B.Q2_K.gguf | 296 MB | Q2_K |
| rStar-Coder-Qwen3-0.6B.Q3_K_L.gguf | 368 MB | Q3_K_L |
| rStar-Coder-Qwen3-0.6B.Q3_K_M.gguf | 347 MB | Q3_K_M |
| rStar-Coder-Qwen3-0.6B.Q3_K_S.gguf | 323 MB | Q3_K_S |
| rStar-Coder-Qwen3-0.6B.Q4_K_M.gguf | 397 MB | Q4_K_M |
| rStar-Coder-Qwen3-0.6B.Q4_K_S.gguf | 383 MB | Q4_K_S |
| rStar-Coder-Qwen3-0.6B.Q5_K_M.gguf | 444 MB | Q5_K_M |
| rStar-Coder-Qwen3-0.6B.Q5_K_S.gguf | 437 MB | Q5_K_S |
| rStar-Coder-Qwen3-0.6B.Q6_K.gguf | 495 MB | Q6_K |
| rStar-Coder-Qwen3-0.6B.Q8_0.gguf | 639 MB | Q8_0 |
Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
- Task
- Text Generation
- Library
- transformers
- Type
- qwen3
- License
- apache-2.0
- Languages
- en
- Created
- Aug 6, 2025
- Updated
- Aug 6, 2025