Text Generation
Transformers
Safetensors
English
Chinese
glm_moe_dsa
conversational
Eval Results
fp8
Instructions to use zai-org/GLM-5.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zai-org/GLM-5.3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="zai-org/GLM-5.3") 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-5.3") model = AutoModelForCausalLM.from_pretrained("zai-org/GLM-5.3", 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-5.3 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "zai-org/GLM-5.3" # 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-5.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/zai-org/GLM-5.3
- SGLang
How to use zai-org/GLM-5.3 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-5.3" \ --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-5.3", "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-5.3" \ --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-5.3", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use zai-org/GLM-5.3 with Docker Model Runner:
docker model run hf.co/zai-org/GLM-5.3
Commit History
Today
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chat template: speed up tool-result validation and reordering (#31)
- chat template: linear tool result id checks and reordering (ad14a0aee275cb7bf3c7329b0a40c2491af45c53) - chat template: pair tool results by sort-merge instead of list.index (ffc0d7c686847398144f193851b06894860bfc62) Co-authored-by: Maroon Ayoub <vMaroon@users.noreply.huggingface.co>ba32f8c
2 days ago
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Add Agents' Last Exam result (28.5, ale-cli-pass-rate, from model card) (#30)
- Add Agents' Last Exam result (28.5, ale-cli-pass-rate, from model card) (47757b8c8902bc5c8d3ae2af242b9784895d1205) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>c5087a7
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chat template: early break in tool result reordering check
Verified signatureFollowing the optimization in [this SGLang PR](https://github.com/sgl-project/sglang/pull/36957), added four `break` statements so the loop stops early once it detects that the results can't be reordered.aca966e -
Add Toolathlon-Verified evaluation result (#15)
- Add Toolathlon-Verified evaluation result (8515e61d7237b5ef5c9dfaa16549e5c0abe6ff5e) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>5bcee3f -
Point terminal-bench-3.0 eval result to unified harborframework/terminal-bench dataset (#16)
- Point terminal-bench-3.0 eval result to unified harborframework/terminal-bench dataset (9d5722e8e148ced38a7b2e20a14c9c51edc1e0c6) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>5a1460b
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Move harness note into notes field for Terminal-Bench 2.1 and 3.0 (#13)
- Move harness note into notes field for Terminal-Bench 2.1 (554e6429ccfa801c2009ef1ea7c17afd20d8565a) - Move harness note into notes field for Terminal-Bench 3.0 (1100db900e0e7fed941d8d2867959b27b5bc767c) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>187fb9f -
Add harness used for Terminal-Bench 2.1 and 3.0 eval results (#11)
- Add harness used for Terminal-Bench 2.1 eval result (4d71278849031f0570352bb0338dfbe38c0000a3) - Add harness used for Terminal-Bench 3.0 eval result (c1fe3f5b2ebcf4658d33989404c1f7cb43881fd5) Co-authored-by: Nathan Habib <SaylorTwift@users.noreply.huggingface.co>eec49ed
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Add community evaluation results for HLE (#3)
- Add community evaluation results for HLE (ef3c5118f221bb8fa8e1ee828f2a78cd33c6e368) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>e0b07fd
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Add community evaluation results for DEEP-SWE, TERMINAL-BENCH-2.1, TERMINAL-BENCH-3.0 (#2)
- Add community evaluation results for DEEP-SWE, TERMINAL-BENCH-2.1, TERMINAL-BENCH-3.0 (d27e53c56e644b535765774242a07e32d503f4ca) Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>935644c -
update
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Initial commit 0828
Co-authored-by: zRzRzRzRzRzRzR <zRzRzRzRzRzRzR@users.noreply.huggingface.co>7cda819