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Model: prithivMLmods/Poseidon-Reasoning-1.7B-GGUF
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---
datasets:
- prithivMLmods/Poseidon-Reasoning-5M
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Poseidon-Reasoning-1.7B
pipeline_tag: text-generation
library_name: transformer
tags:
- text-generation-inference
- moe
- code
- science
- biology
- chemistry
- thinking
---
# **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):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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{
"model_type": "qwen3"
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}