Text Generation
Transformers
Safetensors
English
Chinese
glm_moe_dsa
quark
mxfp4
rocm
sglang
conversational
8-bit precision
Instructions to use OneNexus/GLM-5.3-MXFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OneNexus/GLM-5.3-MXFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OneNexus/GLM-5.3-MXFP4") 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("OneNexus/GLM-5.3-MXFP4") model = AutoModelForCausalLM.from_pretrained("OneNexus/GLM-5.3-MXFP4", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use OneNexus/GLM-5.3-MXFP4 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OneNexus/GLM-5.3-MXFP4" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OneNexus/GLM-5.3-MXFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/OneNexus/GLM-5.3-MXFP4
- SGLang
How to use OneNexus/GLM-5.3-MXFP4 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 "OneNexus/GLM-5.3-MXFP4" \ --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": "OneNexus/GLM-5.3-MXFP4", "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 "OneNexus/GLM-5.3-MXFP4" \ --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": "OneNexus/GLM-5.3-MXFP4", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use OneNexus/GLM-5.3-MXFP4 with Docker Model Runner:
docker model run hf.co/OneNexus/GLM-5.3-MXFP4
Commit History
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Remove image digest comment from model card
Verified signatureRemove the SHA-256 comment from the deployment example while preserving the official SGLang image tag.eab18c6 -
Update model card with latest image and eval results
Verified signatureUpdate the SGLang ROCm image tag and digest comment, and add DeepSWE v1.1 and Terminal-Bench 4.0 evaluation tables.e35224f
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Sync chat template with official Z.ai GLM-5.3
Verified signatureUse chat_template.jinja from zai-org/GLM-5.3@aca966e4e02791568aa6a4ced368624b3d897f42. Only the chat template is updated; checkpoint weights and other files are unchanged.ac499e2
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Restore canonical GLM-5.3 base-model relation
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Remove obsolete runtime-path note from model card
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Promote evaluated Strong7 GLM-5.3 MXFP4 checkpoint
Verified signatureReplace the earlier unevaluated FP8-derived upload with the audited BF16-derived Strong7 checkpoint, stock InferenceX deployment card, and strict row-identity sgl-eval results.b93f777
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Add files using upload-large-folder tool
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Add files using upload-large-folder tool
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initial commit
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