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)# pip install -U transformers accelerate # 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", 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
- 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
Commit History
6 days ago
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Add PLE (Physical-Engineering's Last Exam) benchmark score
Verified signatureHi Z.ai team, This PR adds `.eval_results/ple.yaml` with the [Physical-Engineering's Last Exam (PLE)](https://huggingface.co/datasets/google/physical-engineerings-last-exam) score for `zai-org/GLM-5.2`. - Score: `26.0` / 100 (std 1.7) - Harness: Codex (the best of the 7 harnesses we ran with this model) - Task id: `overall-score` (dev split) - Results date: `2026-10-20` - Leaderboard: https://ple-bench.com/leaderboard?set=dev - Paper: https://arxiv.org/abs/2610.30537 PLE tests AI agents on structural, civil, electrical, and mechanical engineering tasks. The score is the mean reward over the 29 public dev tasks, averaged over 3 runs, with the model at its highest reasoning setting (where it has one) and a 6-hour limit per task. Each task runs in an isolated Harbor container with the project's files. A separate verifier grades the deliverables with automated checks, an expert rubric judged by Claude Opus 5.5, or both. Thank you!aa02e77
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Add community evaluation results for DEEP-SWE, GPQA, HLE, SWE-BENCH_PRO (#12)
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