Instructions to use zai-org/GLM-4.5-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4.5-FP8 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="zai-org/GLM-4.5-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.5-FP8")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.5-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-4.5-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-4.5-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.5-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-4.5-FP8

SGLang

How to use zai-org/GLM-4.5-FP8 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "zai-org/GLM-4.5-FP8" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.5-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "zai-org/GLM-4.5-FP8" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/GLM-4.5-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-4.5-FP8 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4.5-FP8
```

Improve model card: Add citation and specific tags (moe, fp8)

by nielsr HF Staff - opened Aug 12, 2025

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

+18

-3

Files changed (1) hide show

README.md +18 -3

README.md CHANGED Viewed

@@ -5,6 +5,9 @@ language:
 library_name: transformers
 license: mit
 pipeline_tag: text-generation
 ---
 # GLM-4.5-FP8
@@ -89,8 +92,8 @@ Under the configurations in the table below, the models can utilize their full 1
 | 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
@@ -221,4 +224,16 @@ python3 -m sglang.launch_server \
 + 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.

 library_name: transformers
 license: mit
 pipeline_tag: text-generation
+tags:
+- moe
+- fp8
 ---
 # GLM-4.5-FP8
 | 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
 + 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.
+## Citation
+If you find our work useful or helpful for your R&D works, please feel free to cite our paper as below.
+```bibtex
+@article{zhu2025glm45,
+  title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models},
+  author={Zhu, Xiaohan and Sun, Tianxiang and Wang, Hao and Xu, Yi and Zhang, Yichen and Wang, Junyi and Huang, Junjie and Zeng, Jiao and Huang, Yangyang and Gu, Ruipeng and Zhang, Xiaodong and Du, Mengying and Han, Hao and Li, Chao and Xiao, Jin and Guo, Weidong and Li, Zhen and Lu, Jingkang and Chen, Shu and Chen, Huadong and Chen, Peng and Liu, Hongguang and Guo, Guang and Liu, Wen and Yang, Tianyu and Hu, Bo and Zhang, Wenmin and Sun, Maosong},
+  journal={arXiv preprint arXiv:2508.06471},
+  year={2025}
+}
+```