Model Intelligence Sheet
prithivmlmods/procyon-1.5b-theorem-gguf overview
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.
Downloads
98
Likes
0
Pipeline
text-generation
Library
transformers
Visibility
Public
Access
Open
Repository Files & Downloads
13 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Procyon-1.5B-Qwen2-Theorem.BF16.gguf | GGUF | BF16 | 3.32 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.F16.gguf | GGUF | F16 | 3.32 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.F32.gguf | GGUF | F32 | 6.63 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q2_K.gguf | GGUF | Q2_K | 718.00 MB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_L.gguf | GGUF | Q3_K_L | 935.02 MB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_M.gguf | GGUF | Q3_K_M | 881.63 MB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q3_K_S.gguf | GGUF | Q3_K_S | 821.32 MB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q4_K_M.gguf | GGUF | Q4_K_M | 1.04 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q4_K_S.gguf | GGUF | Q4_K_S | 1021.94 MB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q5_K_M.gguf | GGUF | Q5_K_M | 1.20 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q5_K_S.gguf | GGUF | Q5_K_S | 1.17 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q6_K.gguf | GGUF | Q6_K | 1.36 GB | Download |
| Procyon-1.5B-Qwen2-Theorem.Q8_0.gguf | GGUF | — | 1.76 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"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"
],
"frontmatter": {
"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"
]
},
"hero_image_url": "https://www.nethype.de/huggingface_embed/quantpplgraph.png",
"summary": "# **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.",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\nlicense: apache-2.0\nlanguage:\n- en\nbase_model:\n- prithivMLmods/Procyon-1.5B-Qwen2-Theorem\npipeline_tag: text-generation\nlibrary_name: transformers\ntags:\n- text-generation-inference\n- theorem\n---\n# **Procyon-1.5B-Qwen2-Theorem-GGUF**\n\n> **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.\n\n## Model Files\n\n| File Name | Size | Format | Description |\n|-----------|------|--------|-------------|\n| Procyon-1.5B-Qwen2-Theorem.F32.gguf | 7.11 GB | F32 | Full precision 32-bit floating point |\n| Procyon-1.5B-Qwen2-Theorem.F16.gguf | 3.56 GB | F16 | Half precision 16-bit floating point |\n| Procyon-1.5B-Qwen2-Theorem.BF16.gguf | 3.56 GB | BF16 | Brain floating point 16-bit |\n| Procyon-1.5B-Qwen2-Theorem.Q8_0.gguf | 1.89 GB | Q8_0 | 8-bit quantized |\n| Procyon-1.5B-Qwen2-Theorem.Q6_K.gguf | 1.46 GB | Q6_K | 6-bit quantized |\n| Procyon-1.5B-Qwen2-Theorem.Q5_K_M.gguf | 1.29 GB | Q5_K_M | 5-bit quantized, medium quality |\n| Procyon-1.5B-Qwen2-Theorem.Q5_K_S.gguf | 1.26 GB | Q5_K_S | 5-bit quantized, small quality |\n| Procyon-1.5B-Qwen2-Theorem.Q4_K_M.gguf | 1.12 GB | Q4_K_M | 4-bit quantized, medium quality |\n| Procyon-1.5B-Qwen2-Theorem.Q4_K_S.gguf | 1.07 GB | Q4_K_S | 4-bit quantized, small quality |\n| Procyon-1.5B-Qwen2-Theorem.Q3_K_L.gguf | 980 MB | Q3_K_L | 3-bit quantized, large quality |\n| Procyon-1.5B-Qwen2-Theorem.Q3_K_M.gguf | 924 MB | Q3_K_M | 3-bit quantized, medium quality |\n| Procyon-1.5B-Qwen2-Theorem.Q3_K_S.gguf | 861 MB | Q3_K_S | 3-bit quantized, small quality |\n| Procyon-1.5B-Qwen2-Theorem.Q2_K.gguf | 753 MB | Q2_K | 2-bit quantized |\n\n## Quants Usage \n\n(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)\n\nHere is a handy graph by ikawrakow comparing some lower-quality quant\ntypes (lower is better):\n\n",
"related_quantizations": []
},
"tags": [
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"theorem",
"text-generation",
"en",
"base_model:prithivMLmods/Procyon-1.5B-Qwen2-Theorem",
"base_model:quantized:prithivMLmods/Procyon-1.5B-Qwen2-Theorem",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
],
"likes": 0,
"downloads": 98,
"gated": false,
"private": false,
"last_modified": "2025-06-16T06:23:46.000Z",
"created_at": "2025-06-11T09:19:59.000Z",
"pipeline_tag": "text-generation",
"library_name": "transformers"
}
Source payload excerpt (from Hugging Face API)
{
"_id": "68494a3f7142d9075cae4feb",
"id": "prithivMLmods/Procyon-1.5B-Theorem-GGUF",
"modelId": "prithivMLmods/Procyon-1.5B-Theorem-GGUF",
"sha": "aab96e2f38a0b97cbcd007b253018cc699b4537d",
"createdAt": "2025-06-11T09:19:59.000Z",
"lastModified": "2025-06-16T06:23:46.000Z",
"author": "prithivMLmods",
"downloads": 98,
"likes": 0,
"gated": false,
"private": false,
"pipeline_tag": "text-generation",
"library_name": "transformers",
"siblings_count": 16
}