Text Generation
Transformers
Safetensors
qwen3
feature-extraction
dflash
speculative-decoding
draft-model
block-diffusion
glm
custom_code
text-generation-inference
Instructions to use UCloud-org/GLM-5.2-FP8-DFlash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UCloud-org/GLM-5.2-FP8-DFlash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True) model = AutoModel.from_pretrained("UCloud-org/GLM-5.2-FP8-DFlash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use UCloud-org/GLM-5.2-FP8-DFlash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "UCloud-org/GLM-5.2-FP8-DFlash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "UCloud-org/GLM-5.2-FP8-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/UCloud-org/GLM-5.2-FP8-DFlash
- SGLang
How to use UCloud-org/GLM-5.2-FP8-DFlash 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 "UCloud-org/GLM-5.2-FP8-DFlash" \ --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": "UCloud-org/GLM-5.2-FP8-DFlash", "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 "UCloud-org/GLM-5.2-FP8-DFlash" \ --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": "UCloud-org/GLM-5.2-FP8-DFlash", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use UCloud-org/GLM-5.2-FP8-DFlash with Docker Model Runner:
docker model run hf.co/UCloud-org/GLM-5.2-FP8-DFlash
- Xet hash:
- a5298d05e8ce2fdb833008d3ff47289381ad27102b92f38f90e0b2f9f0dc99c0
- Size of remote file:
- 7.46 GB
- SHA256:
- 4801e62be0c6a4a0f82093c7b1158b8d7dd78d28c0892b48361a6965b6cef2a2
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