Instructions to use zai-org/GLM-Z1-32B-0414 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-Z1-32B-0414 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-Z1-32B-0414")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-Z1-32B-0414")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-Z1-32B-0414")
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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zai-org/GLM-Z1-32B-0414 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-Z1-32B-0414"
# 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-Z1-32B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-Z1-32B-0414

SGLang

How to use zai-org/GLM-Z1-32B-0414 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-Z1-32B-0414" \
    --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-Z1-32B-0414",
		"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-Z1-32B-0414" \
        --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-Z1-32B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-Z1-32B-0414 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-Z1-32B-0414
```

The output of MathResponse is empty

by zh211211 - opened Apr 22, 2025

Discussion

zh211211

Apr 22, 2025

class Step(BaseModel):
explanation: str
output: str

class MathResponse(BaseModel):
steps: list[Step]
final_answer: str

client = OpenAI(base_url="http://localhost:8000/v1",api_key="-")
completion = client.beta.chat.completions.parse(
model="glmz1",
messages=[
{"role": "system", "content": "You are a helpful expert math tutor."},
{"role": "user", "content": "Solve 8x + 31 = 2."},
],
response_format=MathResponse,
extra_body=dict(guided_decoding_backend="outlines"),
)
output：
ParsedChatCompletionMessage[MathResponse](content='{"steps": [], "final_answer": ""}', refusal=None, role='assistant', annotations=None, audio=None, function_call=None, tool_calls=None, parsed=MathResponse(steps=[], final_answer=''), reasoning_content=None)

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