Instructions to use zai-org/GLM-5.3-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.3-Flash with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.3-Flash")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zai-org/GLM-5.3-Flash")
model = AutoModelForMultimodalLM.from_pretrained("zai-org/GLM-5.3-Flash", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-5.3-Flash with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.3-Flash"
# 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-5.3-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.3-Flash

SGLang

How to use zai-org/GLM-5.3-Flash 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-5.3-Flash" \
    --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-5.3-Flash",
		"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-5.3-Flash" \
        --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-5.3-Flash",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.3-Flash with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.3-Flash
```

chat template update

#18

pinned

by ZHANGYUXUAN-zR - opened 3 days ago

Discussion

ZHANGYUXUAN-zR

Z.ai org 3 days ago

•

edited 3 days ago

We’ve updated the chat template. If you’ve already downloaded the model before Aug 27 0:30 AM(UTC+8), please download it(chat template) again. Thanks!

ZHANGYUXUAN-zR pinned discussion 3 days ago

h4xon

3 days ago

•

edited 3 days ago

but there seems to be a bug in your online plattform now. I get a lot of \n in the "reasoning.text" responses. That did not happen with ox-alpha. (using openrouter)

sanz12

2 days ago

(https://huggingface.co/zai-org/GLM-5.3-Flash/commit/c5b82b63e37b10546e7ac2eae571fd8d099ae898)

i see you guys remove the multimodal support in chat_template.jinja;

my question is, will you open source the multimodal abilities such as vision encode in later time?

btw, many thanks to your great model!

ZHANGYUXUAN-zR

Z.ai org 2 days ago

•

edited 2 days ago

No, this commit is incorrect, so it needs to be updated.

(https://huggingface.co/zai-org/GLM-5.3-Flash/commit/c5b82b63e37b10546e7ac2eae571fd8d099ae898)

i see you guys remove the multimodal support in chat_template.jinja;

my question is, will you open source the multimodal abilities such as vision encode in later time?

btw, many thanks to your great model!

No, that commit was incorrect, so it needs to be updated. The main branch does support it now.

LLMSUN

2 days ago

No, this commit is incorrect, so it needs to be updated.

(https://huggingface.co/zai-org/GLM-5.3-Flash/commit/c5b82b63e37b10546e7ac2eae571fd8d099ae898)

i see you guys remove the multimodal support in chat_template.jinja;

my question is, will you open source the multimodal abilities such as vision encode in later time?

btw, many thanks to your great model!
No, that commit was incorrect, so it needs to be updated. The main branch does support it now.
See translation

NILKNARFGonzo

1 day ago

No, this commit is incorrect, so it needs to be updated.

(https://huggingface.co/zai-org/GLM-5.3-Flash/commit/c5b82b63e37b10546e7ac2eae571fd8d099ae898)

i see you guys remove the multimodal support in chat_template.jinja;

my question is, will you open source the multimodal abilities such as vision encode in later time?

btw, many thanks to your great model!
No, that commit was incorrect, so it needs to be updated. The main branch does support it now.
See translation

r/ihadastroke

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment