44 lines
2.1 KiB
Markdown
44 lines
2.1 KiB
Markdown
---
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license: apache-2.0
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language:
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- en
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base_model:
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- GetSoloTech/Qwen3-Code-Reasoning-4B
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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- Coder
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- Code
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---
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# **Qwen3-Code-Reasoning-4B-f32-GGUF**
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> [GetSoloTech/Qwen3-Code-Reasoning-4B](https://huggingface.co/GetSoloTech/Qwen3-Code-Reasoning-4B) is a 4-billion parameter language model fine-tuned from Qwen3-4B-Thinking-2507 with LoRA adapters, specifically optimized for competitive programming and advanced code reasoning tasks. Trained on the high-quality Code-Reasoning dataset, including problems from TACO, APPS, CodeContests, and Codeforces, this model is engineered to deliver detailed, step-by-step solutions with ≥50% test case pass rates, improved constraint handling, and structured output for both code generation and complex reasoning scenarios. With configurable context length up to 262,144 tokens, it inherits the reasoning strengths of its base model and is recommended for users seeking well-justified code solutions and enhanced comprehensiveness in programming problem-solving.
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## Model Files
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| File Name | Quant Type | File Size |
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| - | - | - |
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| Qwen3-Code-Reasoning-4B.BF16.gguf | BF16 | 8.05 GB |
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| Qwen3-Code-Reasoning-4B.F16.gguf | F16 | 8.05 GB |
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| Qwen3-Code-Reasoning-4B.F32.gguf | F32 | 16.1 GB |
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| Qwen3-Code-Reasoning-4B.Q2_K.gguf | Q2_K | 1.67 GB |
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| Qwen3-Code-Reasoning-4B.Q3_K_L.gguf | Q3_K_L | 2.24 GB |
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| Qwen3-Code-Reasoning-4B.Q3_K_M.gguf | Q3_K_M | 2.08 GB |
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| Qwen3-Code-Reasoning-4B.Q3_K_S.gguf | Q3_K_S | 1.89 GB |
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| Qwen3-Code-Reasoning-4B.Q4_K_M.gguf | Q4_K_M | 2.5 GB |
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| Qwen3-Code-Reasoning-4B.Q4_K_S.gguf | Q4_K_S | 2.38 GB |
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| Qwen3-Code-Reasoning-4B.Q5_K_M.gguf | Q5_K_M | 2.89 GB |
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| Qwen3-Code-Reasoning-4B.Q5_K_S.gguf | Q5_K_S | 2.82 GB |
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| Qwen3-Code-Reasoning-4B.Q6_K.gguf | Q6_K | 3.31 GB |
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| Qwen3-Code-Reasoning-4B.Q8_0.gguf | Q8_0 | 4.28 GB |
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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