license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | |||||
|---|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
Blitzar-Coder-4B-F.1-GGUF
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.
Model Files
| Filename | Size | Format | Description |
|---|---|---|---|
| Blitzar-Coder-4B-F.1.BF16.gguf | 8.05 GB | BF16 | Brain Float 16-bit quantization |
| Blitzar-Coder-4B-F.1.F16.gguf | 8.05 GB | F16 | Half precision (16-bit) floating point |
| Blitzar-Coder-4B-F.1.F32.gguf | 16.1 GB | F32 | Full precision (32-bit) floating point |
| Blitzar-Coder-4B-F.1.Q2_K.gguf | 1.67 GB | Q2_K | 2-bit quantization with K-quant |
| Blitzar-Coder-4B-F.1.Q3_K_L.gguf | 2.24 GB | Q3_K_L | 3-bit quantization (Large) with K-quant |
| Blitzar-Coder-4B-F.1.Q3_K_M.gguf | 2.08 GB | Q3_K_M | 3-bit quantization (Medium) with K-quant |
| Blitzar-Coder-4B-F.1.Q3_K_S.gguf | 1.89 GB | Q3_K_S | 3-bit quantization (Small) with K-quant |
| Blitzar-Coder-4B-F.1.Q4_K_M.gguf | 2.5 GB | Q4_K_M | 4-bit quantization (Medium) with K-quant |
| Blitzar-Coder-4B-F.1.Q4_K_S.gguf | 2.38 GB | Q4_K_S | 4-bit quantization (Small) with K-quant |
| Blitzar-Coder-4B-F.1.Q5_K_M.gguf | 2.89 GB | Q5_K_M | 5-bit quantization (Medium) with K-quant |
| Blitzar-Coder-4B-F.1.Q5_K_S.gguf | 2.82 GB | Q5_K_S | 5-bit quantization (Small) with K-quant |
| Blitzar-Coder-4B-F.1.Q6_K.gguf | 3.31 GB | Q6_K | 6-bit quantization with K-quant |
| Blitzar-Coder-4B-F.1.Q8_0.gguf | 4.28 GB | Q8_0 | 8-bit quantization |
Recommended Usage
- Q4_K_M or Q5_K_M: Best balance of quality and performance for most users
- Q6_K or Q8_0: Higher quality, larger file sizes
- Q2_K or Q3_K_S: Fastest inference, lower quality
- F16 or BF16: High quality, requires more VRAM
- F32: Highest quality, requires significant VRAM
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):
