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Model: prithivMLmods/Procyon-1.5B-Theorem-GGUF
Source: Original Platform

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README.md

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+---
+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)

config.json

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+{
+    "model_type": "qwen2"
+}

configuration.json

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+{"framework": "pytorch", "task": "others", "allow_remote": true}