library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- GPTQ
- vLLM
base_model:
- ZhipuAI/GLM-4.6
base_model_relation: quantized
GLM-4.6-GPTQ-Int4-Int8Mix
Base Model: ZhipuAI/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 \
tclf90/GLM-4.6-GPTQ-Int4-Int8Mix \
--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】
2025-10-03
1. Initial commit
【Model Files】
| File Size | Last Updated |
|---|---|
232GB |
2025-10-03 |
【Model Download】
from modelscope import snapshot_download
snapshot_download('tclf90/GLM-4.6-GPTQ-Int4-Int8Mix', 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 = 0.95top_k = 40