Instructions to use zai-org/GLM-5.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/GLM-5.2 with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/GLM-5.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-5.2")
model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.2")
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-5.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/GLM-5.2"
# 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-5.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zai-org/GLM-5.2

SGLang

How to use zai-org/GLM-5.2 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-5.2" \
    --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-5.2",
		"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-5.2" \
        --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-5.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zai-org/GLM-5.2 with Docker Model Runner:
```
docker model run hf.co/zai-org/GLM-5.2
```

IDEA: Bitnet 1.58 (a4.8) version in future variants would be so incredible!

#40

by apiarium - opened 3 days ago

Discussion

apiarium

3 days ago

•

edited 3 days ago

GLM-5.2 is incredible — thanks for releasing the weights. Would the team consider releasing a BitNet 1.58 (a4.8) variant in a future update? For anyone unfamiliar: ternary weights {-1, 0, +1} at 1.58 bits/param plus 4-bit activations means matmul becomes pure integer addition, no floating-point multipliers needed. Microsoft's bitnet.cpp already runs a 100B BitNet model on a single CPU at reading speed (5–7 tok/s) with 2–6× speedups on consumer hardware. The BitNet 1.58 paper (arXiv:2402.17764) showed perplexity parity with FP16 at 7B scale, and the a4.8 paper (arXiv:2411.04965) pushed activations down further with minimal quality loss. Training recipe is public at microsoft/unilm. A BitNet-native GLM would run offline on ordinary laptops and phones — no GPU needed — which is exactly the kind of accessibility a model this good deserves. Just putting it out there!

apiarium changed discussion title from Bitnet 1.58 (a4.8) version for the next GLM would make everything much easier! to Idea: Bitnet 1.58 (a4.8) version in future variants would be so incredible! 3 days ago

apiarium changed discussion title from Idea: Bitnet 1.58 (a4.8) version in future variants would be so incredible! to IDEA: Bitnet 1.58 (a4.8) version in future variants would be so incredible! 3 days ago

OblxDev

3 days ago

I totally agree on the first part "GLM-5.2 is incredible" 👌

Then I was unfamiliar with all the other words. It was a dense lesson and I discovered many new rabbit holes.
Thanks OP, and I hope what you describe becomes reality !

I am gueninely excited for what is coming in the second half of 2026.

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