Instructions to use tencent/Tencent-Hunyuan-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Tencent-Hunyuan-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tencent/Tencent-Hunyuan-Large")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tencent/Tencent-Hunyuan-Large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tencent/Tencent-Hunyuan-Large with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tencent/Tencent-Hunyuan-Large" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tencent/Tencent-Hunyuan-Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tencent/Tencent-Hunyuan-Large
- SGLang
How to use tencent/Tencent-Hunyuan-Large 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 "tencent/Tencent-Hunyuan-Large" \ --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": "tencent/Tencent-Hunyuan-Large", "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 "tencent/Tencent-Hunyuan-Large" \ --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": "tencent/Tencent-Hunyuan-Large", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tencent/Tencent-Hunyuan-Large with Docker Model Runner:
docker model run hf.co/tencent/Tencent-Hunyuan-Large
Download pytorch_model-00014-of-00080.bin from tencent/Tencent-Hunyuan-Large: direct link, hf CLI and curl.
- Browser
- Download file 9.84 GB
-
https://huggingface.co/tencent/Tencent-Hunyuan-Large/resolve/e5ea569747a304feade8fe94dc67391b627ae763/pytorch_model-00014-of-00080.bin
- Command line
-
hf download hf://tencent/Tencent-Hunyuan-Large@e5ea569747a304feade8fe94dc67391b627ae763/pytorch_model-00014-of-00080.bin
-
curl -L -o pytorch_model-00014-of-00080.bin https://huggingface.co/tencent/Tencent-Hunyuan-Large/resolve/e5ea569747a304feade8fe94dc67391b627ae763/pytorch_model-00014-of-00080.bin
9.84 GB
- Xet hash:
- 730f51ac8fc70c5cdf10048f9ce277541f89c39ceeb5eb972f70a5b9c174b4d6
- Size of remote file:
- 9.84 GB
- SHA256:
- 79f9600b46b7f7e18c761ef08231296efae726afd09129fc927654c5a505e0f4
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