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Model: prithivMLmods/Vulpecula-4B-GGUF Source: Original Platform
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README.md
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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/Vulpecula-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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---
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# Vulpecula-4B-GGUF
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> **Vulpecula-4B** is fine-tuned based on the traces of **SK1.1**, consisting of the same 1,000 entries of the **DeepSeek thinking trajectory**, along with fine-tuning on **Fine-Tome 100k** and **Open Math Reasoning** datasets. This specialized 4B parameter model is designed for enhanced mathematical reasoning, logical problem-solving, and structured content generation, optimized for precision and step-by-step explanation.
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## Model Files
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| File Name | Size | Quantization | Format | Description |
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| -------------------------- | ------- | ------------ | ------ | ----------------------------- |
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| `Vulpecula-4B.F16.gguf` | 8.05 GB | FP16 | GGUF | Float16 precision version |
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| `Vulpecula-4B.Q4_K_M.gguf` | 2.5 GB | Q4\_K\_M | GGUF | 4-bit quantized (K M variant) |
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| `Vulpecula-4B.Q5_K_M.gguf` | 2.89 GB | Q5\_K\_M | GGUF | 5-bit quantized (K M variant) |
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| `Vulpecula-4B.Q8_0.gguf` | 4.28 GB | Q8\_0 | GGUF | 8-bit quantized |
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| `.gitattributes` | 1.8 kB | — | — | Git LFS tracking file |
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| `README.md` | 31 B | — | — | Model documentation |
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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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| Link | Type | Size/GB | Notes |
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|:-----|:-----|--------:|:------|
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q2_K.gguf) | Q2_K | 0.4 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_S.gguf) | Q3_K_S | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.IQ4_XS.gguf) | IQ4_XS | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_S.gguf) | Q4_K_S | 0.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q4_K_M.gguf) | Q4_K_M | 0.6 | fast, recommended |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_S.gguf) | Q5_K_S | 0.6 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q5_K_M.gguf) | Q5_K_M | 0.7 | |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q6_K.gguf) | Q6_K | 0.7 | very good quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.Q8_0.gguf) | Q8_0 | 0.9 | fast, best quality |
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| [GGUF](https://huggingface.co/mradermacher/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B.f16.gguf) | f16 | 1.6 | 16 bpw, overkill |
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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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config.json
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{
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"model_type": "qwen3"
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}
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configuration.json
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{"framework": "pytorch", "task": "others", "allow_remote": true}
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