README.md

---
library_name: transformers
pipeline_tag: text-generation
tags:
- glm4_moe
- AWQ
- 量化修复
- vLLM
base_model:
  - zai-org/GLM-4.5
base_model_relation: quantized
---
# GLM-4.5-AWQ
Base model [zai-org/GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)


### 【VLLM Launch Command for 8-GPU Single Node】
<i>Note: When launching this model on 8 GPUs, you must include --enable-expert-parallel, otherwise expert tensor partitioning will fail due to mismatch. This flag is not required for 4-GPU setups. </i>
```
CONTEXT_LENGTH=32768

vllm serve \
    QuantTrio/GLM-4.5-AWQ \
    --served-model-name GLM-4.5-AWQ \
    --enable-expert-parallel \
    --swap-space 16 \
    --max-num-seqs 512 \
    --max-model-len $CONTEXT_LENGTH \
    --max-seq-len-to-capture $CONTEXT_LENGTH \
    --gpu-memory-utilization 0.9 \
    --tensor-parallel-size 8 \
    --trust-remote-code \
    --disable-log-requests \
    --host 0.0.0.0 \
    --port 8000
```

### 【Dependencies】

```
vllm==0.10.0
```

### 【Model Update】
``` 
2025-07-30
1. fast commit
```

### 【Model Files】

| File Size	    | Last Updated       |
|---------|--------------|
| `181GB` | `2025-07-30` |



### 【Model Download】

```python
from huggingface_hub import snapshot_download
snapshot_download('QuantTrio/GLM-4.5-AWQ', cache_dir="your_local_path")
```


### 【Overview】
# GLM-4.5

<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-4.5 <a href="https://z.ai/blog/glm-4.5" target="_blank">technical blog</a>.
    <br>
    📍 Use GLM-4.5 API services on <a href="https://docs.z.ai/guides/llm/glm-4.5">Z.ai API Platform (Global)</a> or <br> <a href="https://docs.bigmodel.cn/cn/guide/models/text/glm-4.5">Zhipu AI Open Platform (Mainland China)</a>.
    <br>
    👉 One click to <a href="https://chat.z.ai">GLM-4.5</a>.
</p>
  
## Model Introduction

The **GLM-4.5** series models are foundation models designed for intelligent agents. GLM-4.5 has **355** billion total parameters with **32** billion active parameters, while GLM-4.5-Air adopts a more compact design with **106** billion total parameters and **12** billion active parameters. GLM-4.5 models unify reasoning, coding, and intelligent agent capabilities to meet the complex demands of intelligent agent applications.

Both GLM-4.5 and GLM-4.5-Air are hybrid reasoning models that provide two modes: thinking mode for complex reasoning and tool usage, and non-thinking mode for immediate responses.

We have open-sourced the base models, hybrid reasoning models, and FP8 versions of the hybrid reasoning models for both GLM-4.5 and GLM-4.5-Air. They are released under the MIT open-source license and can be used commercially and for secondary development.

As demonstrated in our comprehensive evaluation across 12 industry-standard benchmarks, GLM-4.5 achieves exceptional performance with a score of **63.2**, in the **3rd** place among all the proprietary and open-source  models. Notably, GLM-4.5-Air delivers competitive results at **59.8** while maintaining superior efficiency.

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

For more eval results, show cases, and technical details, please visit
our [technical blog](https://z.ai/blog/glm-4.5). The technical report will be released soon.


The model code, tool parser and reasoning parser can be found in the implementation of [transformers](https://github.com/huggingface/transformers/tree/main/src/transformers/models/glm4_moe), [vLLM](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/glm4_moe_mtp.py) and [SGLang](https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/models/glm4_moe.py).

## Quick Start

Please refer our [github page](https://github.com/zai-org/GLM-4.5) for more detail.