liquidai/lfm2-1.2b-rag-gguf Q6_K GGUF - Free GGUF Download is indexed on GraySoft with repository links, GGUF quant files, and Hugging Face metadata. This page helps you pick a local model for guIDE or other runtimes. See related models in the same shard below.
Model Intelligence Sheet
liquidai/lfm2-1.2b-rag-gguf overview
Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems. Use cases: You can find more information about other task-specific models in this blog post.
Downloads
1,815
Likes
44
Pipeline
text-generation
Library
transformers
Visibility
Public
Access
Open
Repository Files & Downloads
6 files detected
Direct downloads for all repository files
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| LFM2-1.2B-RAG-F16.gguf | GGUF | F16 | 2.18 GB | Download |
| LFM2-1.2B-RAG-Q4_0.gguf | GGUF | — | 663.52 MB | Download |
| LFM2-1.2B-RAG-Q4_K_M.gguf | GGUF | Q4_K_M | 697.03 MB | Download |
| LFM2-1.2B-RAG-Q5_K_M.gguf | GGUF | Q5_K_M | 804.28 MB | Download |
| LFM2-1.2B-RAG-Q6_K.gguf | GGUF | Q6_K | 918.24 MB | Download |
| LFM2-1.2B-RAG-Q8_0.gguf | GGUF | — | 1.16 GB | Download |
Model Details Live
Metadata Inspector
Normalized metadata (stored in metadata_json)
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"license_name": "lfm1.0",
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"summary": "Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems. **Use cases**: You can find more information about other task-specific models in this blog post.",
"quick_links": [],
"benchmark_table_html": "",
"readme_markdown": "---\nlibrary_name: transformers\nlicense: other\nlicense_name: lfm1.0\nlicense_link: LICENSE\nlanguage:\n- en\n- ar\n- zh\n- fr\n- de\n- ja\n- ko\n- es\npipeline_tag: text-generation\ntags:\n- liquid\n- lfm2\n- edge\nbase_model: LiquidAI/LFM2-1.2B-RAG\n---\n\n<center>\n<div style=\"text-align: center;\">\n <img \n src=\"https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png\" \n alt=\"Liquid AI\"\n style=\"width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;\"\n />\n</div>\n<div style=\"display: flex; justify-content: center; gap: 0.5em;\">\n <a href=\"https://playground.liquid.ai/chat\">\n<a href=\"https://playground.liquid.ai/\"><strong>Try LFM</strong></a> • <a href=\"https://docs.liquid.ai/lfm\"><strong>Documentation</strong></a> • <a href=\"https://leap.liquid.ai/\"><strong>LEAP</strong></a></a>\n</div>\n</center>\n\n# LFM2-1.2B-RAG-GGUF\n\nBased on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems.\n\n**Use cases**:\n\n- Chatbot to ask questions about the documentation of a particular product.\n- Custom support with an internal knowledge base to provide grounded answers.\n- Academic research assistant with multi-turn conversations about research papers and course materials.\n\nYou can find more information about other task-specific models in this [blog post](https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices).\n\n## 🏃 How to run LFM2\n\nExample usage with [llama.cpp](https://github.com/ggml-org/llama.cpp):\n\n```\nllama-cli -hf LiquidAI/LFM2-1.2B-RAG-GGUF\n```",
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"last_modified": "2026-04-06T18:53:24.000Z",
"created_at": "2025-09-05T16:31:55.000Z",
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Source payload excerpt (from Hugging Face API)
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