GLM-4.6-AWQ

Base Model: zai-org/GLM-4.6

【Dependencies / Installation】

As of 2025-10-01, create a fresh Python environment and run:

pip install -U pip
pip install vllm==0.10.2

【vLLM Startup Command】

Note: When launching with TP=8, include --enable-expert-parallel; otherwise the expert tensors couldn’t be evenly sharded across GPU devices.

CONTEXT_LENGTH=32768
vllm serve \
    QuantTrio/GLM-4.6-AWQ \
    --served-model-name My_Model \
    --enable-auto-tool-choice \
    --tool-call-parser glm45 \
    --reasoning-parser glm45 \
    --swap-space 16 \
    --max-num-seqs 64 \
    --max-model-len $CONTEXT_LENGTH \
    --gpu-memory-utilization 0.9 \
    --tensor-parallel-size 8 \
    --enable-expert-parallel \
    --trust-remote-code \
    --disable-log-requests \
    --host 0.0.0.0 \
    --port 8000

【Logs】

2025-10-01
1. Initial commit

【Model Files】

File Size	Last Updated
184GB	2025-10-01

【Model Download】

from modelscope import snapshot_download
snapshot_download('QuantTrio/GLM-4.6-AWQ', cache_dir="your_local_path")

【Overview】

GLM-4.6

👋 Join our Discord community.
📖 Check out the GLM-4.6 technical blog, technical report(GLM-4.5), and Zhipu AI technical documentation.
📍 Use GLM-4.6 API services on Z.ai API Platform.
👉 One click to GLM-4.6.

Model Introduction

Compared with GLM-4.5, GLM-4.6 brings several key improvements:

• Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex agentic tasks.
• Superior coding performance: The model achieves higher scores on code benchmarks and demonstrates better real-world performance in applications such as Claude Code、Cline、Roo Code and Kilo Code, including improvements in generating visually polished front-end pages.
• Advanced reasoning: GLM-4.6 shows a clear improvement in reasoning performance and supports tool use during inference, leading to stronger overall capability.
• More capable agents: GLM-4.6 exhibits stronger performance in tool using and search-based agents, and integrates more effectively within agent frameworks.
• Refined writing: Better aligns with human preferences in style and readability, and performs more naturally in role-playing scenarios.

We evaluated GLM-4.6 across eight public benchmarks covering agents, reasoning, and coding. Results show clear gains over GLM-4.5, with GLM-4.6 also holding competitive advantages over leading domestic and international models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4.

Inference

Both GLM-4.5 and GLM-4.6 use the same inference method.

you can check our github for more detail.

Recommended Evaluation Parameters

For general evaluations, we recommend using a sampling temperature of 1.0.

For code-related evaluation tasks (such as LCB), it is further recommended to set:

• top_p = 0.95
• top_k = 40

Evaluation

• For tool-integrated reasoning, please refer to this doc.
• For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to this. for the detailed template.