Instructions to use zai-org/GLM-5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-5")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zai-org/GLM-5", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use zai-org/GLM-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-5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/zai-org/GLM-5
- SGLang
How to use zai-org/GLM-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-5" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "zai-org/GLM-5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use zai-org/GLM-5 with Docker Model Runner:
docker model run hf.co/zai-org/GLM-5
Add evaluation results for GPQA, HLE (#22)
Browse files- Add evaluation results for GPQA, HLE (25f841a1ce34c104d54d5ea52adef53e47976de0)
- Update .eval_results/hle.yaml (22c99c0ca7ae8b010ee0577a7acbe01246d13415)
- Update .eval_results/hle_with_tools.yaml (f7d34fbc5116ab7837d7fa2def080ce402a7dd87)
- Update .eval_results/hle_with_tools.yaml (a185b9b508c09353db5c426449b62cef6fcbddac)
- Update .eval_results/hle_with_tools.yaml (c3a7b733fcb172baa9dee798ad4dd3e08504c32a)
- Update .eval_results/gpqa.yaml (0dbd2351ed12335e8c31bcdb34193925735e6899)
- Update .eval_results/hle_with_tools.yaml (ced4d5301501da279a87495a6c144fd8201bb640)
Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>
- .eval_results/gpqa.yaml +8 -0
- .eval_results/hle.yaml +9 -0
- .eval_results/hle_with_tools.yaml +10 -0
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- dataset:
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id: Idavidrein/gpqa
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task_id: diamond
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value: 86.0
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date: '2026-02-13'
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source:
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url: https://huggingface.co/zai-org/GLM-5
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name: Model Card
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- dataset:
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id: cais/hle
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task_id: hle
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value: 30.5
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date: '2026-02-13'
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source:
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url: https://huggingface.co/zai-org/GLM-5
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name: Model Card
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user: SaylorTwift
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- dataset:
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id: cais/hle
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task_id: hle
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value: 50.4
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date: '2026-02-13'
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source:
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url: https://huggingface.co/zai-org/GLM-5
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name: Model Card
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user: SaylorTwift
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notes: "With tools"
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