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ModelHub XC 60131e8c1a 初始化项目,由ModelHub XC社区提供模型
Model: prithivMLmods/Procyon-1.5B-Theorem-GGUF
Source: Original Platform
2026-04-24 00:57:56 +08:00

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2.1 KiB
Markdown

---
license: apache-2.0
language:
- en
base_model:
- prithivMLmods/Procyon-1.5B-Qwen2-Theorem
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
- theorem
---
# **Procyon-1.5B-Qwen2-Theorem-GGUF**
> **Procyon-1.5B-Qwen2-Theorem** is an **experimental theorem explanation model** fine-tuned on **Qwen2-1.5B**. Specially crafted for mathematical theorem understanding, structured concept breakdowns, and non-reasoning based explanation tasks, it targets domains where clarity and formal structure take precedence over freeform reasoning.
## Model Files
| File Name | Size | Format | Description |
|-----------|------|--------|-------------|
| Procyon-1.5B-Qwen2-Theorem.F32.gguf | 7.11 GB | F32 | Full precision 32-bit floating point |
| Procyon-1.5B-Qwen2-Theorem.F16.gguf | 3.56 GB | F16 | Half precision 16-bit floating point |
| Procyon-1.5B-Qwen2-Theorem.BF16.gguf | 3.56 GB | BF16 | Brain floating point 16-bit |
| Procyon-1.5B-Qwen2-Theorem.Q8_0.gguf | 1.89 GB | Q8_0 | 8-bit quantized |
| Procyon-1.5B-Qwen2-Theorem.Q6_K.gguf | 1.46 GB | Q6_K | 6-bit quantized |
| Procyon-1.5B-Qwen2-Theorem.Q5_K_M.gguf | 1.29 GB | Q5_K_M | 5-bit quantized, medium quality |
| Procyon-1.5B-Qwen2-Theorem.Q5_K_S.gguf | 1.26 GB | Q5_K_S | 5-bit quantized, small quality |
| Procyon-1.5B-Qwen2-Theorem.Q4_K_M.gguf | 1.12 GB | Q4_K_M | 4-bit quantized, medium quality |
| Procyon-1.5B-Qwen2-Theorem.Q4_K_S.gguf | 1.07 GB | Q4_K_S | 4-bit quantized, small quality |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_L.gguf | 980 MB | Q3_K_L | 3-bit quantized, large quality |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_M.gguf | 924 MB | Q3_K_M | 3-bit quantized, medium quality |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_S.gguf | 861 MB | Q3_K_S | 3-bit quantized, small quality |
| Procyon-1.5B-Qwen2-Theorem.Q2_K.gguf | 753 MB | Q2_K | 2-bit quantized |
## 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)