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

Libraries

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

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.3")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.3", device_map="auto")
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=256)
print(tokenizer.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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

GLM-5.3 / .eval_results

Commit History

Oct 12 13 days ago

13:00
Add Agents' Last Exam result (28.5, ale-cli-pass-rate, from model card) (#30)⁠

ZHANGYUXUAN-zR +1: SaylorTwift

c5087a7

Sep 4

03:57
Add Toolathlon-Verified evaluation result (#15)⁠

ZHANGYUXUAN-zR +1: SaylorTwift

5bcee3f
03:57
Point terminal-bench-3.0 eval result to unified harborframework/terminal-bench dataset (#16)⁠

ZHANGYUXUAN-zR +1: SaylorTwift

5a1460b

Aug 31

13:33
Move harness note into notes field for Terminal-Bench 2.1 and 3.0 (#13)⁠

ZHANGYUXUAN-zR +1: SaylorTwift

187fb9f
10:59
Add harness used for Terminal-Bench 2.1 and 3.0 eval results (#11)⁠

ZHANGYUXUAN-zR +1: SaylorTwift

eec49ed

Aug 29

09:51
Add community evaluation results for HLE (#3)⁠

ZHANGYUXUAN-zR +1: nielsr

e0b07fd

Aug 28

15:22
Add community evaluation results for DEEP-SWE, TERMINAL-BENCH-2.1, TERMINAL-BENCH-3.0 (#2)⁠

ZHANGYUXUAN-zR +1: nielsr

935644c

Commit History

Oct 12 13 days ago

Add Agents' Last Exam result (28.5, ale-cli-pass-rate, from model card) (#30)⁠

Sep 4

Add Toolathlon-Verified evaluation result (#15)⁠

Point terminal-bench-3.0 eval result to unified harborframework/terminal-bench dataset (#16)⁠

Aug 31

Move harness note into notes field for Terminal-Bench 2.1 and 3.0 (#13)⁠

Add harness used for Terminal-Bench 2.1 and 3.0 eval results (#11)⁠

Aug 29

Add community evaluation results for HLE (#3)⁠

Aug 28

Add community evaluation results for DEEP-SWE, TERMINAL-BENCH-2.1, TERMINAL-BENCH-3.0 (#2)⁠