mudler/gemma-4-26b-a4b-it-claude-opus-distill-apex-gguf overview

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  "metadata": {},
  "card_data": {
    "license": "gemma",
    "base_model": "TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill",
    "tags": [
      "gguf",
      "quantized",
      "apex",
      "moe",
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      "claude-distilled",
      "vlm",
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    ],
    "frontmatter": {
      "license": "gemma",
      "base_model": "TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill",
      "tags": [
        "gguf",
        "quantized",
        "apex",
        "moe",
        "mixture-of-experts",
        "gemma4",
        "claude-distilled",
        "vlm",
        "vision"
      ]
    },
    "hero_image_url": "",
    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of gemma-4-26B-A4B-it-Claude-Opus-Distill — a Claude Opus reasoning-distilled version of google/gemma-4-26B-A4B-it by TeichAI. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: gemma\nbase_model: TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - gemma4\n  - claude-distilled\n  - vlm\n  - vision\n---\n\n# Gemma 4 26B-A4B Claude Opus Distill APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [gemma-4-26B-A4B-it-Claude-Opus-Distill](https://huggingface.co/TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill) — a Claude Opus reasoning-distilled version of [google/gemma-4-26B-A4B-it](https://huggingface.co/google/gemma-4-26B-A4B-it) by TeichAI.\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**: gemma-4-26B-A4B-it-Claude-Opus-Distill (same architecture as gemma-4-26B-A4B-it)\n- **Layers**: 30\n- **Experts**: 128 routed (8 active per token)\n- **Total Parameters**: 26B\n- **Active Parameters**: ~4B per token\n- **Vision**: Built-in vision encoder (mmproj included)\n- **APEX Config**: 5+5 symmetric edge gradient across 30 layers\n- **Calibration**: v1.3 diverse dataset\n\n## Run with LocalAI\n\n```bash\nlocal-ai run mudler/gemma-4-26B-A4B-it-Claude-Opus-Distill-APEX-GGUF@gemma-4-26B-A4B-Claude-Distill-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",
    "gemma4",
    "claude-distilled",
    "vlm",
    "vision",
    "base_model:TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill",
    "base_model:quantized:TeichAI/gemma-4-26B-A4B-it-Claude-Opus-Distill",
    "license:gemma",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 11,
  "downloads": 14410,
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  "private": false,
  "last_modified": "2026-04-05T11:01:01.000Z",
  "created_at": "2026-04-05T02:57:32.000Z",
  "pipeline_tag": "",
  "library_name": ""
}

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