mudler/qwen3.6-35b-a3b-apex-gguf overview

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    "license": "apache-2.0",
    "base_model": "Qwen/Qwen3.6-35B-A3B",
    "tags": [
      "gguf",
      "quantized",
      "apex",
      "moe",
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    "frontmatter": {
      "license": "apache-2.0",
      "base_model": "Qwen/Qwen3.6-35B-A3B",
      "tags": [
        "gguf",
        "quantized",
        "apex",
        "moe",
        "mixture-of-experts",
        "qwen3",
        "vlm",
        "vision"
      ]
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    "hero_image_url": "",
    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of Qwen/Qwen3.6-35B-A3B. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
    "quick_links": [],
    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: apache-2.0\nbase_model: Qwen/Qwen3.6-35B-A3B\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - qwen3\n  - vlm\n  - vision\n---\n\n# Qwen 3.6 35B-A3B APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [Qwen/Qwen3.6-35B-A3B](https://huggingface.co/Qwen/Qwen3.6-35B-A3B).\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## Available Files\n\n| File | Profile | Size | Best For |\n|------|---------|------|----------|\n| Qwen3.6-35B-A3B-APEX-I-Quality.gguf | I-Quality | 22 GB | Highest quality with imatrix |\n| Qwen3.6-35B-A3B-APEX-Quality.gguf | Quality | 22 GB | Highest quality standard |\n| Qwen3.6-35B-A3B-APEX-I-Balanced.gguf | I-Balanced | 24 GB | Best overall quality/size ratio |\n| Qwen3.6-35B-A3B-APEX-Balanced.gguf | Balanced | 24 GB | General purpose |\n| Qwen3.6-35B-A3B-APEX-I-Compact.gguf | I-Compact | 17 GB | Consumer GPUs, best quality/size |\n| Qwen3.6-35B-A3B-APEX-Compact.gguf | Compact | 17 GB | Consumer GPUs |\n| Qwen3.6-35B-A3B-APEX-I-Mini.gguf | I-Mini | 14 GB | Smallest viable, fastest inference |\n| mmproj.gguf | Vision projector | ~1 GB | Required for image understanding |\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**: Qwen 3.6 35B-A3B (Qwen/Qwen3.6-35B-A3B)\n- **Layers**: 40\n- **Experts**: 256 routed + shared (8 active per token)\n- **Total Parameters**: ~35B\n- **Active Parameters**: ~3B per token\n- **Attention**: Hybrid (full attention every 4th layer, linear/Mamba otherwise)\n- **Vision**: Built-in vision encoder (mmproj included)\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- **llama.cpp**: Built with b8797\n\n## Run with LocalAI\n\n```bash\nlocal-ai run mudler/Qwen3.6-35B-A3B-APEX-GGUF@Qwen3.6-35B-A3B-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",
    "qwen3",
    "vlm",
    "vision",
    "base_model:Qwen/Qwen3.6-35B-A3B",
    "base_model:quantized:Qwen/Qwen3.6-35B-A3B",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
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  "likes": 9,
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  "last_modified": "2026-04-17T06:34:05.000Z",
  "created_at": "2026-04-17T00:07:18.000Z",
  "pipeline_tag": "",
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