mudler/lfm2-24b-a2b-apex-gguf overview

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    "license": "other",
    "base_model": "LiquidAI/LFM2-24B-A2B",
    "tags": [
      "gguf",
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      "moe",
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    "frontmatter": {
      "license": "other",
      "base_model": "LiquidAI/LFM2-24B-A2B",
      "tags": [
        "gguf",
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    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of LFM2-24B-A2B by LiquidAI. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
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    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: other\nbase_model: LiquidAI/LFM2-24B-A2B\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - liquidai\n  - lfm2\n  - hybrid\n---\n\n# LFM2-24B-A2B APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [LFM2-24B-A2B](https://huggingface.co/LiquidAI/LFM2-24B-A2B) by LiquidAI.\n\n**Brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team** | [APEX Project](https://github.com/mudler/apex-quant) | [Technical Report](https://github.com/mudler/apex-quant/blob/main/paper/APEX_Technical_Report.pdf)\n\n## Benchmark Results\n\nBenchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see [mudler/Qwen3.5-35B-A3B-APEX-GGUF](https://huggingface.co/mudler/Qwen3.5-35B-A3B-APEX-GGUF).\n\n## What is APEX?\n\nAPEX is a quantization strategy for Mixture-of-Experts (MoE) models. It classifies tensors by role (routed expert, shared expert, attention) and applies a layer-wise precision gradient -- edge layers get higher precision, middle layers get more aggressive compression. I-variants use diverse imatrix calibration (chat, code, reasoning, tool-calling, agentic traces, Wikipedia).\n\nSee the [APEX project](https://github.com/mudler/apex-quant) for full details, technical report, and scripts.\n\n## Architecture\n\n- **Model**: LFM2-24B-A2B (lfm2_moe) by LiquidAI\n- **Layers**: 40 (30 convolutional + 10 full attention, hybrid)\n- **Experts**: 64 routed (4 active per token) + 2 dense layers\n- **Total Parameters**: 24B\n- **Active Parameters**: ~2B per token\n- **APEX Config**: 5+5 symmetric edge gradient across 40 layers\n- **Calibration**: v1.3 diverse dataset (chat, code, reasoning, multilingual, tool-calling, Wikipedia)\n\n## Run with LocalAI\n\n```bash\nlocal-ai run mudler/LFM2-24B-A2B-APEX-GGUF@LFM2-24B-A2B-APEX-I-Balanced.gguf\n```\n\n## Credits\n\nAPEX is brought to you by the [LocalAI](https://github.com/mudler/LocalAI) team. Developed through human-driven, AI-assisted research. Built on [llama.cpp](https://github.com/ggerganov/llama.cpp).\n",
    "related_quantizations": []
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    "apex",
    "moe",
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    "base_model:LiquidAI/LFM2-24B-A2B",
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  "last_modified": "2026-04-05T16:37:08.000Z",
  "created_at": "2026-04-05T16:28:27.000Z",
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