Instructions to use zai-org/GLM-4.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-4.5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-4.5") 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") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.5") 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 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" # 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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/GLM-4.5
- SGLang
How to use zai-org/GLM-4.5 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" \ --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", "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" \ --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", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/GLM-4.5 with Docker Model Runner:
docker model run hf.co/zai-org/GLM-4.5
When GGUF?
Yes, supporting ollama can help the model gain better reputation
Confucius say: Man who rush tofu, soon taste regret!
ββ from GLM-4.5
Hey @zai-org-3 , FYI I started on a PR to llama.cpp to try and add your models architecture but it's very much a best effort, if you wish to help out and contribute please do feel free to submit changes to, or comment on my PR that is currently in draft https://github.com/ggml-org/llama.cpp/pull/14939
For anyone else reading this that sees my PR, please do not create GGUFs from it yet, it will have issues as it is not yet finished.
Would really love some folks from @zai-org-3 to help out on https://github.com/ggml-org/llama.cpp/pull/14939 if they could?
Be ready to be happy. Support was merged today into llama.cpp. https://github.com/ggml-org/llama.cpp/commit/ef0144c087b33e5b8da42d529ac71aaf05cb49df
Lets fucking goooo!