# GLM-4.6-AWQ

**Repository Path**: hf-models/GLM-4.6-AWQ

## Basic Information

- **Project Name**: GLM-4.6-AWQ
- **Description**: Mirror of https://huggingface.co/QuantTrio/GLM-4.6-AWQ
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-11-11
- **Last Updated**: 2025-11-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

---
library_name: transformers
license: mit
pipeline_tag: text-generation
tags:
- AWQ
- vLLM
base_model:
  - zai-org/GLM-4.6
base_model_relation: quantized
---
# GLM-4.6-AWQ
Base Model: [zai-org/GLM-4.6](https://huggingface.co/zai-org/GLM-4.6)

### 【Dependencies / Installation】
As of **2025-10-01**, create a fresh Python environment and run:
```bash
pip install -U pip
pip install vllm==0.10.2
```

### 【vLLM Startup Command】
<i>Note: When launching with TP=8, include `--enable-expert-parallel`; 
otherwise the expert tensors couldn’t be evenly sharded across GPU devices.</i>

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

### 【Overview】
# GLM-4.6

<div align="center">
<img src=https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg width="15%"/>
</div>
<p align="center">
    👋 Join our <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
    <br>
    📖 Check out the GLM-4.6 <a href="https://z.ai/blog/glm-4.6" target="_blank">technical blog</a>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report(GLM-4.5)</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</a>.
    <br>
    📍 Use GLM-4.6 API services on <a href="https://docs.z.ai/guides/llm/glm-4.6">Z.ai API Platform. </a>
    <br>
    👉 One click to <a href="https://chat.z.ai">GLM-4.6</a>.
</p>

## 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**.

![bench](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/bench_glm46.png)

## Inference

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

you can check our [github](https://github.com/zai-org/GLM-4.5) 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](https://github.com/zai-org/GLM-4.5/blob/main/resources/glm_4.6_tir_guide.md).
- For search benchmark, we design a specific format for searching toolcall in thinking mode to support search agent, please refer to [this](https://github.com/zai-org/GLM-4.5/blob/main/resources/trajectory_search.json). for the detailed template.