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---
language:
- en
- zh
library_name: transformers
license: mit
pipeline_tag: text-generation
---
# 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>, <a href="https://arxiv.org/abs/2508.06471" target="_blank">technical report</a>, and <a href="https://zhipu-ai.feishu.cn/wiki/Gv3swM0Yci7w7Zke9E0crhU7n7D" target="_blank">Zhipu AI technical documentation</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.
For more eval results, show cases, and technical details, please visit
our [technical blog](https://z.ai/blog/glm-4.5) or [technical report](https://arxiv.org/abs/2508.06471).
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).
## Model Downloads
You can directly experience the model on [Hugging Face](https://huggingface.co/spaces/zai-org/GLM-4.5-Space)
or [ModelScope](https://modelscope.cn/studios/ZhipuAI/GLM-4.5-Demo) or download the model by following the links below.
| Model | Download Links | Model Size | Precision |
|------------------|-----------------------------------------------------------------------------------------------------------------------------------------------|------------|-----------|
| GLM-4.5 | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5) | 355B-A32B | BF16 |
| GLM-4.5-Air | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air) | 106B-A12B | BF16 |
| GLM-4.5-FP8 | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-FP8) | 355B-A32B | FP8 |
| GLM-4.5-Air-FP8 | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-FP8)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-FP8) | 106B-A12B | FP8 |
| GLM-4.5-Base | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Base) | 355B-A32B | BF16 |
| GLM-4.5-Air-Base | [🤗 Hugging Face](https://huggingface.co/zai-org/GLM-4.5-Air-Base)<br> [🤖 ModelScope](https://modelscope.cn/models/ZhipuAI/GLM-4.5-Air-Base) | 106B-A12B | BF16 |
## System Requirements
### Inference
We provide minimum and recommended configurations for "full-featured" model inference. The data in the table below is
based on the following conditions:
1. All models use MTP layers and specify
`--speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4` to ensure competitive
inference speed.
2. The `cpu-offload` parameter is not used.
3. Inference batch size does not exceed `8`.
4. All are executed on devices that natively support FP8 inference, ensuring both weights and cache are in FP8 format.
5. Server memory must exceed `1T` to ensure normal model loading and operation.
The models can run under the configurations in the table below:
| Model | Precision | GPU Type and Count | Test Framework |
|-------------|-----------|----------------------|----------------|
| GLM-4.5 | BF16 | H100 x 16 / H200 x 8 | sglang |
| GLM-4.5 | FP8 | H100 x 8 / H200 x 4 | sglang |
| GLM-4.5-Air | BF16 | H100 x 4 / H200 x 2 | sglang |
| GLM-4.5-Air | FP8 | H100 x 2 / H200 x 1 | sglang |
Under the configurations in the table below, the models can utilize their full 128K context length:
| Model | Precision | GPU Type and Count | Test Framework |
|-------------|-----------|-----------------------|----------------|
| GLM-4.5 | BF16 | H100 x 32 / H200 x 16 | sglang |
| GLM-4.5 | FP8 | H100 x 16 / H200 x 8 | sglang |
| GLM-4.5-Air | BF16 | H100 x 8 / H200 x 4 | sglang |
| GLM-4.5-Air | FP8 | H100 x 4 / H200 x 2 | sglang |
### Fine-tuning
The code can run under the configurations in the table below
using [Llama Factory](https://github.com/hiyouga/LLaMA-Factory):
| Model | GPU Type and Count | Strategy | Batch Size (per GPU) |
|-------------|--------------------|----------|----------------------|
| GLM-4.5 | H100 x 16 | Lora | 1 |
| GLM-4.5-Air | H100 x 4 | Lora | 1 |
The code can run under the configurations in the table below using [Swift](https://github.com/modelscope/ms-swift):
| Model | GPU Type and Count | Strategy | Batch Size (per GPU) |
|-------------|--------------------|----------|----------------------|
| GLM-4.5 | H20 (96GiB) x 16 | Lora | 1 |
| GLM-4.5-Air | H20 (96GiB) x 4 | Lora | 1 |
| GLM-4.5 | H20 (96GiB) x 128 | SFT | 1 |
| GLM-4.5-Air | H20 (96GiB) x 32 | SFT | 1 |
| GLM-4.5 | H20 (96GiB) x 128 | RL | 1 |
| GLM-4.5-Air | H20 (96GiB) x 32 | RL | 1 |
## Quick Start
Please install the required packages according to `requirements.txt`.
```shell
pip install -r requirements.txt
```
### transformers
Please refer to the `trans_infer_cli.py` code in the `inference` folder.
### vLLM
+ Both BF16 and FP8 can be started with the following code:
```shell
vllm serve zai-org/GLM-4.5-Air \
--tensor-parallel-size 8 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--served-model-name glm-4.5-air
```
If you're using 8x H100 GPUs and encounter insufficient memory when running the GLM-4.5 model, you'll need
`--cpu-offload-gb 16` (only applicable to vLLM).
If you encounter `flash infer` issues, use `VLLM_ATTENTION_BACKEND=XFORMERS` as a temporary replacement. You can also
specify `TORCH_CUDA_ARCH_LIST='9.0+PTX'` to use `flash infer` (different GPUs have different TORCH_CUDA_ARCH_LIST
values, please check accordingly).
### SGLang
+ BF16
```shell
python3 -m sglang.launch_server \
--model-path zai-org/GLM-4.5-Air \
--tp-size 8 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--mem-fraction-static 0.7 \
--served-model-name glm-4.5-air \
--host 0.0.0.0 \
--port 8000
```
+ FP8
```shell
python3 -m sglang.launch_server \
--model-path zai-org/GLM-4.5-Air-FP8 \
--tp-size 4 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--speculative-algorithm EAGLE \
--speculative-num-steps 3 \
--speculative-eagle-topk 1 \
--speculative-num-draft-tokens 4 \
--mem-fraction-static 0.7 \
--disable-shared-experts-fusion \
--served-model-name glm-4.5-air-fp8 \
--host 0.0.0.0 \
--port 8000
```
### Request Parameter Instructions
+ When using `vLLM` and `SGLang`, thinking mode is enabled by default when sending requests. If you want to disable the
thinking switch, you need to add the `extra_body={"chat_template_kwargs": {"enable_thinking": False}}` parameter.
+ Both support tool calling. Please use OpenAI-style tool description format for calls.
+ For specific code, please refer to `api_request.py` in the `inference` folder. |