Instructions to use zai-org/GLM-4.7-Flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-4.7-Flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-4.7-Flash") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-4.7-Flash", device_map="auto") 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=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- AMD Developer Cloud
- Local Apps Settings
- vLLM
How to use zai-org/GLM-4.7-Flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-4.7-Flash" # 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-4.7-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/GLM-4.7-Flash
- SGLang
How to use zai-org/GLM-4.7-Flash 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-4.7-Flash" \ --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-4.7-Flash", "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-4.7-Flash" \ --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-4.7-Flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/GLM-4.7-Flash with Docker Model Runner:
docker model run hf.co/zai-org/GLM-4.7-Flash
Commit History
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Update README.md
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[Docs] Add LightLLM deployment example (faster than SGLang)
Verified signatureHi @zai-org team, We have recently added support for **GLM-4.7-Flash** in **[LightLLM](https://github.com/ModelTC/lightllm)**. To provide the community with more deployment options, we would like to contribute a brief guide and some performance references to the Model Card. We have implemented and verified the **tool calling** and **reasoning** capabilities of this model to ensure a robust user experience. #### Performance Reference (TP2) In our local benchmarks (64 concurrent requests, 8k input / 1k output), LightLLM demonstrates efficient serving capabilities: * **Throughput:** Reaches **18,931 tok/s** (~31% optimization over SGLang in this specific setup). * **Latency:** Reduced Mean TPOT by approximately 24%. #### Accuracy Reference (BFCL) On the Berkeley Function Calling Leaderboard: * **LightLLM Overall Accuracy:** **49.12%** * **SGLang Overall Accuracy:** 45.41% #### Full Result ```bash # Benchmark script python -m sglang.bench_serving --backend sglang-oai --model /de…e10500a
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Add HLE evaluation result (#55)
Verified signature- Add HLE evaluation result (af22a4e6671db99caac943633fa153c1016eeb03) Co-authored-by: ben burtenshaw <burtenshaw@users.noreply.huggingface.co>7dd2089 -
Add GPQA evaluation result (#54)
Verified signature- Add GPQA evaluation result (0ae26748999a1179b83e7f50e653b32ea404af5a) - Fix task_id to match benchmark eval.yaml (7524de5db643949641e1bafa14f2d9c37a170763) Co-authored-by: ben burtenshaw <burtenshaw@users.noreply.huggingface.co>2b2bd73
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Merge branch 'main' of hf.co:zai-org/GLM-4.7-Flash
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update bench
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Update SGLang guide (#14)
Verified signature- Update SGLang guide (e85ed67926ee68acc2c9259b5f41d6b524fe602a) Co-authored-by: Xinyuan Tong <JustinTong@users.noreply.huggingface.co>279ecdf
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update
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bench update
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