mudler/qwen3-coder-next-apex-gguf overview

{
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  "card_data": {
    "license": "apache-2.0",
    "base_model": "Qwen/Qwen3-Coder-Next",
    "tags": [
      "gguf",
      "quantized",
      "apex",
      "moe",
      "mixture-of-experts",
      "qwen3",
      "coder"
    ],
    "frontmatter": {
      "license": "apache-2.0",
      "base_model": "Qwen/Qwen3-Coder-Next",
      "tags": [
        "gguf",
        "quantized",
        "apex",
        "moe",
        "mixture-of-experts",
        "qwen3",
        "coder"
      ]
    },
    "hero_image_url": "",
    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of Qwen3-Coder-Next. **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-Coder-Next\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - qwen3\n  - coder\n---\n\n# Qwen3-Coder-Next APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [Qwen3-Coder-Next](https://huggingface.co/Qwen/Qwen3-Coder-Next).\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**: Qwen3-Coder-Next (Qwen3Next)\n- **Layers**: 48\n- **Experts**: 512 routed + shared (10 active per token)\n- **Total Parameters**: ~80B\n- **Active Parameters**: ~8-10B per token\n- **Attention**: Hybrid (standard every 4th layer + linear attention)\n- **Context**: 262K tokens\n- **APEX Config**: 5+5 symmetric edge gradient across 48 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/Qwen3-Coder-Next-APEX-GGUF@Qwen3-Coder-Next-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",
    "coder",
    "base_model:Qwen/Qwen3-Coder-Next",
    "base_model:quantized:Qwen/Qwen3-Coder-Next",
    "license:apache-2.0",
    "endpoints_compatible",
    "region:us",
    "conversational"
  ],
  "likes": 10,
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  "last_modified": "2026-04-05T10:14:52.000Z",
  "created_at": "2026-04-05T09:37:44.000Z",
  "pipeline_tag": "",
  "library_name": ""
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  "id": "mudler/Qwen3-Coder-Next-APEX-GGUF",
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  "sha": "2d6b95245341784b93c366dc4473009b2a00e404",
  "createdAt": "2026-04-05T09:37:44.000Z",
  "lastModified": "2026-04-05T10:14:52.000Z",
  "author": "mudler",
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