Vulpecula-4B-GGUF

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.

Model Files

File Name	Size	Quantization	Format	Description
Vulpecula-4B.F16.gguf	8.05 GB	FP16	GGUF	Float16 precision version
Vulpecula-4B.Q4_K_M.gguf	2.5 GB	Q4_K_M	GGUF	4-bit quantized (K M variant)
Vulpecula-4B.Q5_K_M.gguf	2.89 GB	Q5_K_M	GGUF	5-bit quantized (K M variant)
Vulpecula-4B.Q8_0.gguf	4.28 GB	Q8_0	GGUF	8-bit quantized
.gitattributes	1.8 kB	—	—	Git LFS tracking file
README.md	31 B	—	—	Model documentation

Quants Usage

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	0.4
GGUF	Q3_K_S	0.5
GGUF	Q3_K_M	0.5	lower quality
GGUF	Q3_K_L	0.5
GGUF	IQ4_XS	0.6
GGUF	Q4_K_S	0.6	fast, recommended
GGUF	Q4_K_M	0.6	fast, recommended
GGUF	Q5_K_S	0.6
GGUF	Q5_K_M	0.7
GGUF	Q6_K	0.7	very good quality
GGUF	Q8_0	0.9	fast, best quality
GGUF	f16	1.6	16 bpw, overkill

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):