Model Intelligence Sheet
richardyoung/olmocr-2-7b-1025-gguf overview
GGUF quantized version of allenai/olmOCR-2-7B-1025 — Allen AI's state-of-the-art OCR vision-language model, optimized for local inference with llama.cpp and Ollama. olmOCR excels at extracting structured text from documents, PDFs, images, and handwriting — all running locally on your hardware.
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294
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3
Pipeline
image-text-to-text
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—
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Public
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Normalized metadata (stored in metadata_json)
{
"metadata": {},
"card_data": {
"license": "apache-2.0",
"base_model": "allenai/olmOCR-2-7B-1025",
"tags": [
"gguf",
"quantized",
"ocr",
"vision-language-model",
"qwen2.5-vl",
"llama-cpp",
"ollama",
"document-understanding"
],
"pipeline_tag": "image-text-to-text",
"language": [
"en"
],
"frontmatter": {
"license": "apache-2.0",
"base_model": "allenai/olmOCR-2-7B-1025",
"tags": [
"gguf",
"quantized",
"ocr",
"vision-language-model",
"qwen2.5-vl",
"llama-cpp",
"ollama",
"document-understanding"
],
"pipeline_tag": "image-text-to-text",
"language": [
"en"
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},
"hero_image_url": "",
"summary": "GGUF quantized version of allenai/olmOCR-2-7B-1025 — Allen AI's state-of-the-art OCR vision-language model, optimized for **local inference** with llama.cpp and Ollama. olmOCR excels at extracting structured text from documents, PDFs, images, and handwriting — all running locally on your hardware.",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\nlicense: apache-2.0\nbase_model: allenai/olmOCR-2-7B-1025\ntags:\n - gguf\n - quantized\n - ocr\n - vision-language-model\n - qwen2.5-vl\n - llama-cpp\n - ollama\n - document-understanding\npipeline_tag: image-text-to-text\nlanguage:\n - en\n---\n\n# olmOCR-2-7B-1025 (GGUF)\n\nGGUF quantized version of [allenai/olmOCR-2-7B-1025](https://huggingface.co/allenai/olmOCR-2-7B-1025) — Allen AI's state-of-the-art OCR vision-language model, optimized for **local inference** with llama.cpp and Ollama.\n\nolmOCR excels at extracting structured text from documents, PDFs, images, and handwriting — all running locally on your hardware.\n\n## Quick Start\n\n### With Ollama\n\n```bash\nollama run hf.co/richardyoung/olmOCR-2-7B-1025-GGUF\n```\n\n### With llama.cpp\n\n```bash\nhuggingface-cli download richardyoung/olmOCR-2-7B-1025-GGUF \\\n --include \"*Q8_0*\" --local-dir ./models\n\n./llama-cli -m ./models/*Q8_0*.gguf \\\n --image document.png \\\n -p \"Extract all text from this document.\" \\\n -ngl 99\n```\n\n## Why This Model?\n\n- **Best-in-class OCR**: olmOCR outperforms many commercial OCR solutions on academic benchmarks\n- **Local & private**: Process sensitive documents without sending them to cloud APIs\n- **Structured output**: Extracts text with layout awareness — tables, columns, headers\n- **GGUF format**: Runs on consumer hardware with llama.cpp (CPU or GPU)\n\n## Use Cases\n\n- Extract text from scanned PDFs and documents\n- Digitize handwritten notes\n- Process invoices, receipts, and forms locally\n- Build privacy-preserving document pipelines\n\n## Also Available\n\n- [olmOCR MLX 8-bit](https://hf.co/richardyoung/olmOCR-2-7B-1025-MLX-8bit) — Apple Silicon optimized\n- [olmOCR MLX 6-bit](https://hf.co/richardyoung/olmOCR-2-7B-1025-MLX-6bit) — Smaller footprint on Mac\n- [olmOCR MLX 4-bit](https://hf.co/richardyoung/olmOCR-2-7B-1025-MLX-4bit) — Minimum RAM on Mac\n\n## Other Models by richardyoung\n\n- **Abliterated/Uncensored models**: [Qwen2.5-7B](https://hf.co/richardyoung/Qwen2.5-7B-Instruct-abliterated-GGUF) | [Qwen3-14B](https://hf.co/richardyoung/Qwen3-14B-abliterated-GGUF) | [DeepSeek-R1-32B](https://hf.co/richardyoung/Deepseek-R1-Distill-Qwen-32b-uncensored) | [Qwen3-8B](https://hf.co/richardyoung/Qwen3-8B-Abliterated)\n- **MLX quantizations (Apple Silicon)**: [Kimi-K2 series](https://hf.co/richardyoung/Kimi-K2-Instruct-0905-MLX-4bit) | [olmOCR MLX](https://hf.co/richardyoung/olmOCR-2-7B-1025-MLX-4bit)\n- **OCR & Vision**: [olmOCR GGUF](https://hf.co/richardyoung/olmOCR-2-7B-1025-GGUF)\n- **Healthcare/Medical**: [Synthea 575K patients dataset](https://hf.co/datasets/richardyoung/synthea-575k-patients) | [CardioEmbed](https://hf.co/richardyoung/CardioEmbed)\n- **Research**: [LLM Instruction-Following Evaluation](https://hf.co/richardyoung/llm-instruction-following-paper) (arxiv:2510.18892)\n",
"related_quantizations": []
},
"tags": [
"gguf",
"quantized",
"ocr",
"vision-language-model",
"qwen2.5-vl",
"llama-cpp",
"ollama",
"document-understanding",
"image-text-to-text",
"en",
"arxiv:2510.18892",
"base_model:allenai/olmOCR-2-7B-1025",
"base_model:quantized:allenai/olmOCR-2-7B-1025",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
],
"likes": 3,
"downloads": 294,
"gated": false,
"private": false,
"last_modified": "2026-03-23T02:07:04.000Z",
"created_at": "2025-10-24T20:48:31.000Z",
"pipeline_tag": "image-text-to-text",
"library_name": ""
}
Source payload excerpt (from Hugging Face API)
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"createdAt": "2025-10-24T20:48:31.000Z",
"lastModified": "2026-03-23T02:07:04.000Z",
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