Poseidon-Reasoning-1.7B-GGUF

Poseidon-Reasoning-1.7B is a general-purpose, high-efficiency reasoning model fine-tuned on Qwen3-1.7B using the Poseidon-Reasoning-5M dataset (first 70K entries). Designed for mathematical, scientific, and code-related reasoning, this model strikes a balance between structured logic and contextual fluency—ideal for domains demanding symbolic precision and algorithmic thought.

Model Files

File Name	Format	Size	Precision	Description
Poseidon-Reasoning-1.7B.F32.gguf	GGUF	6.89 GB	32-bit Float	Full precision model, highest quality
Poseidon-Reasoning-1.7B.F16.gguf	GGUF	3.45 GB	16-bit Float	Half precision, good balance of size and quality
Poseidon-Reasoning-1.7B.BF16.gguf	GGUF	3.45 GB	16-bit BFloat	Brain floating point, optimized for inference
Poseidon-Reasoning-1.7B.Q8_0.gguf	GGUF	1.83 GB	8-bit Quantized	High quality quantized model
Poseidon-Reasoning-1.7B.Q6_K.gguf	GGUF	1.42 GB	6-bit Quantized	Very good quality with smaller size
Poseidon-Reasoning-1.7B.Q5_K_M.gguf	GGUF	1.26 GB	5-bit Quantized (Medium)	Good quality, balanced compression
Poseidon-Reasoning-1.7B.Q5_K_S.gguf	GGUF	1.23 GB	5-bit Quantized (Small)	Good quality, higher compression
Poseidon-Reasoning-1.7B.Q4_K_M.gguf	GGUF	1.11 GB	4-bit Quantized (Medium)	Decent quality with good compression
Poseidon-Reasoning-1.7B.Q4_K_S.gguf	GGUF	1.06 GB	4-bit Quantized (Small)	Decent quality, higher compression
Poseidon-Reasoning-1.7B.Q3_K_L.gguf	GGUF	1 GB	3-bit Quantized (Large)	Lower quality but very compact
Poseidon-Reasoning-1.7B.Q3_K_M.gguf	GGUF	940 MB	3-bit Quantized (Medium)	Lower quality, more compact
Poseidon-Reasoning-1.7B.Q3_K_S.gguf	GGUF	867 MB	3-bit Quantized (Small)	Lower quality, most compact
Poseidon-Reasoning-1.7B.Q2_K.gguf	GGUF	778 MB	2-bit Quantized	Minimal quality, maximum compression

Configuration Files

File Name	Size	Description
config.json	29 Bytes	Model configuration parameters
.gitattributes	2.44 kB	Git LFS configuration for large files
README.md	288 Bytes	Project documentation

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):