Text Generation
Transformers
Safetensors
Trellis
English
Chinese
glm_moe_dsa
glm
exl3
vllm
blackwell
mixture-of-experts
conversational
modelopt
Instructions to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw") 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("brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw") model = AutoModelForCausalLM.from_pretrained("brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw", 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]:])) - Trellis
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Trellis:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw
- SGLang
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw 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 "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw" \ --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": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw", "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 "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw" \ --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": "brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw with Docker Model Runner:
docker model run hf.co/brandonmusic/GLM-5.2-EXL3-TR3-3.0bpw
File size: 12,797 Bytes
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"metadata": {
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"engine": "vllm",
"model": "GLM-5.2-EXL3-TR3-3.0bpw",
"server": "127.0.0.1:8000",
"timestamp": "2026-07-22T05:36:14.936220",
"decode_mode": "duration",
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},
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],
"returncode": 0,
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"stderr": ""
},
"nvidia_smi_topo": {
"cmd": [
"nvidia-smi",
"topo",
"-m"
],
"returncode": 0,
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"stderr": ""
}
},
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"params_available": true,
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"modprobe_available": true,
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"suggested_reload": "stop GPU workloads, then reload NVIDIA modules or reboot; the modprobe file alone is not enough until the nvidia module is reloaded"
},
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},
"results": [],
"summary_table": {},
"burst_results": [],
"burst_summary_table": {},
"methodology": {
"prefill": {
"name": "Prefill",
"present": true,
"mode": "standalone_cold",
"formula": "prompt_tokens / TTFT",
"notes": "Default mode records the required decode scout request for each non-zero decode context, so normal runs do not pay for a separate prefill phase. Standalone mode repeats cold-prefill samples. Prometheus prefill counters, when available and uncontaminated, are stored as validation."
},
"sustained_decode": {
"name": "Sustained Decode",
"present": false,
"formula": "OpenAI stream usage completion_tokens per measured window; client chunk fallback only when continuous usage is unavailable",
"notes": "Duration-based steady-state cell after warmup. This is the main tuning/regression signal for kernels, NCCL, DCP, MTP, and scheduling. Prometheus metrics are stored as validation and scheduler state, not the default headline."
},
"burst_e2e_decode": {
"name": "Burst / E2E Decode",
"present": false,
"status": "not run; use --run-burst",
"formula": "sum(completion_tokens) / profiling_wall_time",
"notes": "Finite client-facing request burst using OpenAI stream usage. It includes request admission, scheduling, prefill/cache behavior, and completion."
}
}
} |