mudler/qwopus-moe-35b-a3b-apex-gguf overview

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    "license": "apache-2.0",
    "base_model": "samuelcardillo/Qwopus-MoE-35B-A3B-GGUF",
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      "license": "apache-2.0",
      "base_model": "samuelcardillo/Qwopus-MoE-35B-A3B-GGUF",
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    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of Qwopus-MoE-35B-A3B. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
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    "readme_markdown": "---\nlicense: apache-2.0\nbase_model: samuelcardillo/Qwopus-MoE-35B-A3B-GGUF\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - qwen3.5\n  - claude-distill\n---\n\n# Qwopus-MoE-35B-A3B APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [Qwopus-MoE-35B-A3B](https://huggingface.co/samuelcardillo/Qwopus-MoE-35B-A3B-GGUF).\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| Qwopus-MoE-35B-A3B-APEX-I-Balanced.gguf | I-Balanced | TBD | Best overall quality/size ratio (with imatrix) |\n| Qwopus-MoE-35B-A3B-APEX-I-Quality.gguf | I-Quality | TBD | Best quality/compression ratio (with imatrix) |\n| Qwopus-MoE-35B-A3B-APEX-Quality.gguf | Quality | TBD | Best quality/compression ratio |\n| Qwopus-MoE-35B-A3B-APEX-Balanced.gguf | Balanced | TBD | Best absolute quality |\n| Qwopus-MoE-35B-A3B-APEX-I-Compact.gguf | I-Compact | TBD | Consumer GPUs (with imatrix) |\n| Qwopus-MoE-35B-A3B-APEX-Compact.gguf | Compact | TBD | Consumer GPUs |\n| Qwopus-MoE-35B-A3B-APEX-I-Mini.gguf | I-Mini | TBD | 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**: Qwopus-MoE-35B-A3B (qwen3_5_moe)\n- **Layers**: 40 (hybrid: linear attention + full attention every 4th layer)\n- **Experts**: 256 routed (8 active per token)\n- **Total Parameters**: ~35B\n- **Active Parameters**: ~3B per token\n- **Origin**: Claude Opus 4.6 QLoRA distill of Qwen3.5-35B-A3B\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- **Source**: [samuelcardillo/Qwopus-MoE-35B-A3B](https://huggingface.co/samuelcardillo/Qwopus-MoE-35B-A3B)\n\n## Run with LocalAI\n\n```bash\nlocal-ai run mudler/Qwopus-MoE-35B-A3B-APEX-GGUF@Qwopus-MoE-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",
    "qwen3.5",
    "claude-distill",
    "base_model:samuelcardillo/Qwopus-MoE-35B-A3B-GGUF",
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  "last_modified": "2026-04-06T12:35:37.000Z",
  "created_at": "2026-04-06T12:14:57.000Z",
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