liquidai/lfm2-24b-a2b-gguf overview

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    "license_name": "lfm1.0",
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    "tags": [
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      "library_name": "transformers",
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    "hero_image_url": "https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png",
    "summary": "LFM2 is a family of hybrid models designed for on-device deployment. LFM2-24B-A2B is the largest model in the family, scaling the architecture to 24 billion parameters while keeping inference efficient. !image Find more information about LFM2-24B-A2B in our blog post.",
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    "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\n- pt\npipeline_tag: text-generation\ntags:\n- liquid\n- lfm2\n- edge\n- moe\n- llama.cpp\n- gguf\nbase_model:\n- LiquidAI/LFM2-24B-A2B\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/\"><strong>Try LFM</strong></a> • <a href=\"https://docs.liquid.ai/lfm/getting-started/welcome\"><strong>Docs</strong></a> • <a href=\"https://leap.liquid.ai/\"><strong>LEAP</strong></a> • <a href=\"https://discord.com/invite/liquid-ai\"><strong>Discord</strong></a>\n</div>\n</center>\n\n<br>\n\n# LFM2-24B-A2B-GGUF\n\nLFM2 is a family of hybrid models designed for on-device deployment. LFM2-24B-A2B is the largest model in the family, scaling the architecture to 24 billion parameters while keeping inference efficient.\n\n- **Best-in-class efficiency**: A 24B MoE model with only 2B active parameters per token, fitting in 32 GB of RAM for deployment on consumer laptops and desktops.\n- **Fast edge inference**: 112 tok/s decode on AMD CPU, 293 tok/s on H100. Fits in 32B GB of RAM with day-one support llama.cpp, vLLM, and SGLang.\n- **Predictable scaling**: Quality improves log-linearly from 350M to 24B total parameters, confirming the LFM2 hybrid architecture scales reliably across nearly two orders of magnitude.\n\n![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/VhdjtAT5zWTWdVYgW69O0.png)\n\nFind more information about LFM2-24B-A2B in our [blog post](https://www.liquid.ai/blog/).\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/LFM2-24B-A2B-GGUF\n```\n\n## 📬 Contact\n\n- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)\n- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).\n",
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  "last_modified": "2026-03-30T12:54:26.000Z",
  "created_at": "2026-02-17T12:53:20.000Z",
  "pipeline_tag": "text-generation",
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