mudler/glm-4.7-flash-apex-gguf overview

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    "license": "apache-2.0",
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    "frontmatter": {
      "license": "apache-2.0",
      "base_model": "zai-org/GLM-4.7-Flash",
      "tags": [
        "gguf",
        "quantized",
        "apex",
        "moe",
        "mixture-of-experts",
        "glm",
        "mla"
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    "summary": "**APEX (Adaptive Precision for EXpert Models)** quantizations of GLM-4.7-Flash. **Brought to you by the LocalAI team** | APEX Project | Technical Report",
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    "benchmark_table_html": "",
    "readme_markdown": "---\nlicense: apache-2.0\nbase_model: zai-org/GLM-4.7-Flash\ntags:\n  - gguf\n  - quantized\n  - apex\n  - moe\n  - mixture-of-experts\n  - glm\n  - mla\n---\n\n# GLM-4.7-Flash APEX GGUF\n\n**APEX (Adaptive Precision for EXpert Models)** quantizations of [GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash).\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| GLM-4.7-Flash-APEX-I-Balanced.gguf | I-Balanced | 21 GB | Best overall quality/size ratio |\n| GLM-4.7-Flash-APEX-I-Quality.gguf | I-Quality | 18 GB | Highest quality with imatrix |\n| GLM-4.7-Flash-APEX-Quality.gguf | Quality | 18 GB | Highest quality standard |\n| GLM-4.7-Flash-APEX-Balanced.gguf | Balanced | 21 GB | General purpose |\n| GLM-4.7-Flash-APEX-I-Compact.gguf | I-Compact | 14 GB | Consumer GPUs, best quality/size |\n| GLM-4.7-Flash-APEX-Compact.gguf | Compact | 14 GB | Consumer GPUs |\n| GLM-4.7-Flash-APEX-I-Mini.gguf | I-Mini | 12 GB | Smallest viable, fastest inference |\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**: GLM-4.7-Flash (Glm4MoeLite)\n- **Layers**: 47 (1 dense + 46 MoE)\n- **Experts**: 64 routed + 1 shared (4 active per token)\n- **Total Parameters**: ~30B\n- **Attention**: Multi-head Latent Attention (MLA, DeepSeek-V2 style)\n- **APEX Config**: 5+5 symmetric edge gradient across 47 layers, MLA-aware tensor mapping\n\n## Run with LocalAI\n\n```bash\nlocal-ai run mudler/GLM-4.7-Flash-APEX-GGUF@GLM-4.7-Flash-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",
    "glm",
    "mla",
    "base_model:zai-org/GLM-4.7-Flash",
    "base_model:quantized:zai-org/GLM-4.7-Flash",
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  "last_modified": "2026-04-04T22:28:39.000Z",
  "created_at": "2026-04-04T20:57:59.000Z",
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