Instructions to use zai-org/GLM-4-9B-0414 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-4-9B-0414 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-4-9B-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-4-9B-0414")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4-9B-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

vLLM

How to use zai-org/GLM-4-9B-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-4-9B-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-4-9B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-4-9B-0414

SGLang

How to use zai-org/GLM-4-9B-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-4-9B-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-4-9B-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-4-9B-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-4-9B-0414",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-4-9B-0414 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-4-9B-0414
```

When will the GLM-4/Z1 series model support VLLM?

by David3698 - opened Apr 16, 2025

Discussion

David3698

Apr 16, 2025

EngineCore hit an exception: Traceback (most recent call last):
File "/usr/local/lib/python3.9/dist-packages/torch/_dynamo/utils.py", line 2586, in run_node
return node.target(*args, **kwargs)
TypeError: linear(): argument 'input' (position 1) must be Tensor, not tuple

ZHANGYUXUAN-zR

Z.ai org Apr 16, 2025

Check this

https://github.com/THUDM/GLM-4/issues/745

flymyd

Apr 16, 2025

Compile manually from the specified PR of the vLLM project because the fix has not yet been merged.

git clone https://github.com/vllm-project/vllm.git
cd vllm
git fetch origin pull/16618/head:pr-16618
VLLM_USE_PRECOMPILED=1 pip install --editable .

After compilation, you can normally perform inference with the model. Note, you must inject the environment variable VLLM_USE_V1=0 to avoid garbled output. Here are some example script:

GLM-4-0414.sh

CUDA_VISIBLE_DEVICES=0,1 VLLM_USE_V1=0 vllm serve /root/models/GLM-4-32B-0414 \
    --served-model-name ChainBlock-Turbo \
    --gpu-memory-utilization 0.95 \
    --max-model-len 32768 \
    --tensor-parallel-size 2 \
    --host 0.0.0.0 \
    --port 9997

GLM-Z1-0414.sh

CUDA_VISIBLE_DEVICES=2,3 VLLM_USE_V1=0 vllm serve /root/models/GLM-Z1-32B-0414 \
    --served-model-name ChainBlock-Turbo-Reasoning \
    --gpu-memory-utilization 0.95 \
    --max-model-len 32768 \
    --enable-reasoning --reasoning-parser granite \
    --tensor-parallel-size 2 \
    --host 0.0.0.0 \
    --port 9998

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