Instructions to use zai-org/GLM-4.5-Air with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-4.5-Air with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-4.5-Air") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.5-Air") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.5-Air") 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-4.5-Air with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-4.5-Air" # 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-4.5-Air", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/GLM-4.5-Air
- SGLang
How to use zai-org/GLM-4.5-Air 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-4.5-Air" \ --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-4.5-Air", "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-4.5-Air" \ --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-4.5-Air", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/GLM-4.5-Air with Docker Model Runner:
docker model run hf.co/zai-org/GLM-4.5-Air
When will GLM4.5 Flash be released
Looking forward to it as well
Flash is based on the Air model with inference optimizations, making the inference cost relatively low. It includes constraints on output tokens, with the main purpose of allowing community developers to experience the latest models for free.
Lets' go !!!
Flash is based on the Air model with inference optimizations, making the inference cost relatively low. It includes constraints on output tokens, with the main purpose of allowing community developers to experience the latest models for free.
I'm wondering if there is a plan of releasing a smaller model like the GLM4 9b? or a 16B MoE would be very interesting.
Flash is based on the Air model with inference optimizations, making the inference cost relatively low. It includes constraints on output tokens, with the main purpose of allowing community developers to experience the latest models for free.
I'm wondering if there is a plan of releasing a smaller model like the GLM4 9b? or a 16B MoE would be very interesting.
I also want these models, Qwen3 Coder works well on bigger projects but GLM4.5 make better results on GUI and three.js .
