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
```

fix: guard against tools=None crash and string arguments in chat_template.jinja

#22

by MoonRainy21 - opened Apr 14

base: refs/heads/main

←

from: refs/pr/22

Discussion Files changed

+11

-9

MoonRainy21

Apr 14

Problem

Two crashes can occur in chat_template.jinja when used with sglang or any OpenAI-compatible server:

1. TypeError: 'NoneType' object is not iterable

When a role="tool" message has non-string content (structured list), the template enters the else branch and executes for tool in tools — but tools is None when no tools were passed in the request. The top-level {%- if tools -%} guard at the top of the file does not cover this inner loop.

Reproduces when replaying a tool-calling conversation history without re-passing the tools array, or when any client omits tools from the request.

Related upstream issue: sgl-project/sglang#6702

2. AttributeError: 'str' object has no attribute 'items'

In the tool_calls rendering block, tc.arguments is iterated with .items() assuming it is a dict. Several OpenAI-compatible clients and model outputs serialize arguments as a JSON string, causing a crash on this line.

Fix

Wrap the inner for tool in tools loop inside the role="tool" branch with {%- if tools -%} ... {%- endif -%}

Deserialize tc.arguments if it is a string before calling .items():

{%- set _args = tc.arguments if tc.arguments is mapping else tc.arguments | from_json -%}

Testing

Plain chat request with no tools key: no regression
Tool-calling request with tools array: no regression
Replayed tool-use history without tools in request: no longer crashes
tc.arguments as JSON string: no longer crashes

fix: guard against tools=None crash and string arguments in chat_template.jinjaed810a15

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