Instructions to use zai-org/GLM-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5", 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=40)
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 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5

SGLang

How to use zai-org/GLM-5 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" \
    --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",
		"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" \
        --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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5
```

zRzRzRzRzRzRzR commited on Feb 11

Commit

fbfe45b

1 Parent(s): c35b39f

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@@ -44,7 +44,7 @@ Reinforcement learning aims to bridge the gap between competence and excellence
 | SWE-bench Multilingual           | 73.3                   | 66.7      | 70.2          | 73.0      | 77.5            | 65.0         | 72.0            |
 | Terminal-Bench 2.0 (Terminus 2)  | 56.2 / 60.7 (verified) | 41.0      | 39.3          | 50.8      | 59.3            | 54.2         | 54.0            |
 | Terminal-Bench 2.0 (Claude Code) | 56.2 / 61.1 (verified)  | 32.8      | 46.4          | -         | 57.9            | -            | -               |
-| CyberGym                         | 48.3                   | 23.5      | 17.3          | 41.3      | 50.6            | 39.9         | -               |
 | Agentic                          |                        |           |               |           |                 |              |                 |
 | BrowseComp                       | 62.0                   | 52.0      | 51.4          | 52 / 60.6 | 37.0            | 37.8         | -               |
 | BrowseComp (w/ Context Manage)   | 75.9                   | 67.5      | 67.6          | 74.9      | 57.8            | 59.2         | 65.8            |

 | SWE-bench Multilingual           | 73.3                   | 66.7      | 70.2          | 73.0      | 77.5            | 65.0         | 72.0            |
 | Terminal-Bench 2.0 (Terminus 2)  | 56.2 / 60.7 (verified) | 41.0      | 39.3          | 50.8      | 59.3            | 54.2         | 54.0            |
 | Terminal-Bench 2.0 (Claude Code) | 56.2 / 61.1 (verified)  | 32.8      | 46.4          | -         | 57.9            | -            | -               |
+| CyberGym                         | 43.2                   | 23.5      | 17.3          | 41.3      | 50.6            | 39.9         | -               |
 | Agentic                          |                        |           |               |           |                 |              |                 |
 | BrowseComp                       | 62.0                   | 52.0      | 51.4          | 52 / 60.6 | 37.0            | 37.8         | -               |
 | BrowseComp (w/ Context Manage)   | 75.9                   | 67.5      | 67.6          | 74.9      | 57.8            | 59.2         | 65.8            |