Instructions to use zai-org/GLM-5.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
GLM 5.2 Nano ( 4 to 20 B params) would be good for edge hardware
please.. should fit on 16GB Vram
I agree, minimax or glm should enter small llm race, qwen recently left, so there is small competition there, mistral stopped working on small models for now, gemma is only competitor in the future I would worry about and others are too far behind, we need new small models
I agree, minimax or glm should enter small llm race, qwen recently left, so there is small competition there, mistral stopped working on small models for now, gemma is only competitor in the future I would worry about and others are too far behind, we need new small models
Why qwen left? Is there any chance to get qwen 3.6 4-9b?
I agree, minimax or glm should enter small llm race, qwen recently left, so there is small competition there, mistral stopped working on small models for now, gemma is only competitor in the future I would worry about and others are too far behind, we need new small models
What about MiniCPM?
I agree, minimax or glm should enter small llm race, qwen recently left, so there is small competition there, mistral stopped working on small models for now, gemma is only competitor in the future I would worry about and others are too far behind, we need new small models
Why qwen left? Is there any chance to get qwen 3.6 4-9b?
Fine tune it yourself on public cpt data