Instructions to use zai-org/GLM-5.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.1 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.1")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.1")
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

vLLM

How to use zai-org/GLM-5.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.1"
# 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-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.1

SGLang

How to use zai-org/GLM-5.1 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-5.1" \
    --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-5.1",
		"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-5.1" \
        --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-5.1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.1 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.1
```

GLM-5.1 tool call output parsing unstable (~50% failure rate) while GLM-4.7 works consistently

#25

by wudaolj - opened Apr 15

Discussion

wudaolj

Apr 15

Title: GLM-5.1 tool call output parsing unstable (~50% failure rate) while GLM-4.7 works consistently

Environment:

Model: glm5.1 (via OpenAI-compatible API)

Reference model: GLM-4.7

Observed Behavior:
When using GLM-5.1 with function calling, the model fails to recognize tool execution outputs approximately 50% of the time. The specific symptoms are:

After the client executes a tool (e.g., date or pwd) and returns the output in a role: "tool" message, the model claims it did not receive any output or states that the tool returned empty content.

As a result, the model enters a repetitive loop, attempting different variations of commands (cd, Get-Location, echo %cd%, etc.) to obtain information that was already provided.

The exact same client code and message construction logic works correctly with GLM-4.7, where the model consistently acknowledges tool outputs and completes tasks without looping.

Reproducibility:
The issue occurs intermittently. In multiple test runs with identical inputs and tool outputs, GLM-5.1 successfully processes the output roughly 50% of the time. The failures do not appear to correlate with specific command types or output lengths.

ZHANGYUXUAN-zR

Z.ai org Apr 16

maybe is this issue?
https://huggingface.co/zai-org/GLM-5.1-FP8/discussions/3/files#d2h-526183
we are checking today

wudaolj

Apr 16

Thanks, we'll give it a try.

ZHANGYUXUAN-zR

Z.ai org Apr 16

We have updated the template, you can use our new template.

wudaolj

Apr 16

It's all set! We're all set here! Thanks!

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