mudler / GLM-4.7-Flash-APEX-GGUF

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Total runs: 5.2K
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Model's Last Updated: August 17 2026

Introduction of GLM-4.7-Flash-APEX-GGUF

Model Details of GLM-4.7-Flash-APEX-GGUF

GLM-4.7-Flash APEX GGUF

APEX (Adaptive Precision for EXpert Models) quantizations of GLM-4.7-Flash .

Brought to you by the LocalAI team | APEX Project | Technical Report

Benchmark Results

Benchmarks coming soon. For reference APEX benchmarks on the Qwen3.5-35B-A3B architecture, see mudler/Qwen3.5-35B-A3B-APEX-GGUF .

Available Files
File Profile Size Best For
GLM-4.7-Flash-APEX-I-Balanced.gguf I-Balanced 21 GB Best overall quality/size ratio
GLM-4.7-Flash-APEX-I-Quality.gguf I-Quality 18 GB Highest quality with imatrix
GLM-4.7-Flash-APEX-Quality.gguf Quality 18 GB Highest quality standard
GLM-4.7-Flash-APEX-Balanced.gguf Balanced 21 GB General purpose
GLM-4.7-Flash-APEX-I-Compact.gguf I-Compact 14 GB Consumer GPUs, best quality/size
GLM-4.7-Flash-APEX-Compact.gguf Compact 14 GB Consumer GPUs
GLM-4.7-Flash-APEX-I-Mini.gguf I-Mini 12 GB Smallest viable, fastest inference
What is APEX?

APEX 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).

See the APEX project for full details, technical report, and scripts.

Architecture
  • Model : GLM-4.7-Flash (Glm4MoeLite)
  • Layers : 47 (1 dense + 46 MoE)
  • Experts : 64 routed + 1 shared (4 active per token)
  • Total Parameters : ~30B
  • Attention : Multi-head Latent Attention (MLA, DeepSeek-V2 style)
  • APEX Config : 5+5 symmetric edge gradient across 47 layers, MLA-aware tensor mapping
Run with LocalAI
local-ai run mudler/[email protected]
Credits

APEX is brought to you by the LocalAI team. Developed through human-driven, AI-assisted research. Built on llama.cpp .

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GLM-4.7-Flash-APEX-GGUF huggingface.co Url

https://huggingface.co/mudler/GLM-4.7-Flash-APEX-GGUF

mudler GLM-4.7-Flash-APEX-GGUF online free

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GLM-4.7-Flash-APEX-GGUF install

GLM-4.7-Flash-APEX-GGUF is an open source model from GitHub that offers a free installation service, and any user can find GLM-4.7-Flash-APEX-GGUF on GitHub to install. At the same time, huggingface.co provides the effect of GLM-4.7-Flash-APEX-GGUF install, users can directly use GLM-4.7-Flash-APEX-GGUF installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/mudler/GLM-4.7-Flash-APEX-GGUF

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