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--- |
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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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- prithivMLmods/Blitzar-Coder-4B-F.1 |
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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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- code |
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- coder |
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--- |
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# **Blitzar-Coder-4B-F.1-GGUF** |
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> **Blitzar-Coder-4B-F.1** is a high-efficiency, multi-language coding model fine-tuned on **Qwen3-4B** using **larger coding traces datasets** spanning **10+ programming languages** including Python, Java, C#, C++, C, Go, JavaScript, TypeScript, Rust, and more. This model delivers exceptional code generation, debugging, and reasoning capabilities—making it an ideal tool for developers seeking advanced programming assistance under constrained compute. |
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## Model Files |
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| Filename | Size | Format | Description | |
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|----------|------|--------|-------------| |
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| Blitzar-Coder-4B-F.1.BF16.gguf | 8.05 GB | BF16 | Brain Float 16-bit quantization | |
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| Blitzar-Coder-4B-F.1.F16.gguf | 8.05 GB | F16 | Half precision (16-bit) floating point | |
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| Blitzar-Coder-4B-F.1.F32.gguf | 16.1 GB | F32 | Full precision (32-bit) floating point | |
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| Blitzar-Coder-4B-F.1.Q2_K.gguf | 1.67 GB | Q2_K | 2-bit quantization with K-quant | |
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| Blitzar-Coder-4B-F.1.Q3_K_L.gguf | 2.24 GB | Q3_K_L | 3-bit quantization (Large) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q3_K_M.gguf | 2.08 GB | Q3_K_M | 3-bit quantization (Medium) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q3_K_S.gguf | 1.89 GB | Q3_K_S | 3-bit quantization (Small) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q4_K_M.gguf | 2.5 GB | Q4_K_M | 4-bit quantization (Medium) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q4_K_S.gguf | 2.38 GB | Q4_K_S | 4-bit quantization (Small) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q5_K_M.gguf | 2.89 GB | Q5_K_M | 5-bit quantization (Medium) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q5_K_S.gguf | 2.82 GB | Q5_K_S | 5-bit quantization (Small) with K-quant | |
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| Blitzar-Coder-4B-F.1.Q6_K.gguf | 3.31 GB | Q6_K | 6-bit quantization with K-quant | |
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| Blitzar-Coder-4B-F.1.Q8_0.gguf | 4.28 GB | Q8_0 | 8-bit quantization | |
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### Recommended Usage |
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- **Q4_K_M** or **Q5_K_M**: Best balance of quality and performance for most users |
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- **Q6_K** or **Q8_0**: Higher quality, larger file sizes |
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- **Q2_K** or **Q3_K_S**: Fastest inference, lower quality |
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- **F16** or **BF16**: High quality, requires more VRAM |
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- **F32**: Highest quality, requires significant VRAM |
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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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