mudler/minimax-m2.5-apex-gguf overview

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    "license": "other",
    "base_model": "MiniMaxAI/MiniMax-M2.5",
    "tags": [
      "gguf",
      "quantized",
      "apex",
      "moe",
      "mixture-of-experts",
      "minimax"
    ],
    "frontmatter": {
      "license": "other",
      "base_model": "MiniMaxAI/MiniMax-M2.5",
      "tags": [
        "gguf",
        "quantized",
        "apex",
        "moe",
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    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of MiniMax-M2.5. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
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    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: other\nbase_model: MiniMaxAI/MiniMax-M2.5\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - minimax\n---\n\n# MiniMax-M2.5 APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5).\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## Available Files\n\n| File | Profile | Size | Best For |\n|------|---------|------|----------|\n| MiniMax-M2.5-APEX-I-Balanced.gguf | I-Balanced | 155 GB | Best overall quality/size ratio |\n| MiniMax-M2.5-APEX-I-Quality.gguf | I-Quality | 130 GB | Highest quality with imatrix |\n| MiniMax-M2.5-APEX-Quality.gguf | Quality | 130 GB | Highest quality standard |\n| MiniMax-M2.5-APEX-Balanced.gguf | Balanced | 155 GB | General purpose |\n| MiniMax-M2.5-APEX-I-Compact.gguf | I-Compact | 100 GB | Multi-GPU setups, best quality/size |\n| MiniMax-M2.5-APEX-Compact.gguf | Compact | 100 GB | Multi-GPU setups |\n| MiniMax-M2.5-APEX-I-Mini.gguf | I-Mini | 81 GB | Smallest viable |\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**: MiniMax-M2.5 (MiniMaxM2)\n- **Layers**: 62\n- **Experts**: 256 routed + 1 shared (8 active per token)\n- **Total Parameters**: 228.7B\n- **Active Parameters**: ~45B per token\n- **APEX Config**: 5+5 symmetric edge gradient across 62 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/MiniMax-M2.5-APEX-GGUF@MiniMax-M2.5-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": []
  },
  "tags": [
    "gguf",
    "quantized",
    "apex",
    "moe",
    "mixture-of-experts",
    "minimax",
    "base_model:MiniMaxAI/MiniMax-M2.5",
    "base_model:quantized:MiniMaxAI/MiniMax-M2.5",
    "license:other",
    "endpoints_compatible",
    "region:us",
    "conversational"
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  "last_modified": "2026-04-04T22:28:36.000Z",
  "created_at": "2026-04-02T21:08:10.000Z",
  "pipeline_tag": "",
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