Model: prithivMLmods/Procyon-1.5B-Theorem-GGUF Source: Original Platform
license, language, base_model, pipeline_tag, library_name, tags
| license | language | base_model | pipeline_tag | library_name | tags | ||||
|---|---|---|---|---|---|---|---|---|---|
| apache-2.0 |
|
|
text-generation | transformers |
|
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):
Description
