Text Generation
Transformers
Safetensors
hy_v4
hunyuan
hy4
Mixture of Experts
conversational
Eval Results
Instructions to use tencent/Hy4-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Hy4-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tencent/Hy4-preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("tencent/Hy4-preview", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tencent/Hy4-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tencent/Hy4-preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tencent/Hy4-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/tencent/Hy4-preview
- SGLang
How to use tencent/Hy4-preview 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/Hy4-preview" \ --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": "tencent/Hy4-preview", "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 "tencent/Hy4-preview" \ --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": "tencent/Hy4-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use tencent/Hy4-preview with Docker Model Runner:
docker model run hf.co/tencent/Hy4-preview
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.gitignore
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# Python
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__pycache__/
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*.py[cod]
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*$py.class
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*.so
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*.egg-info/
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dist/
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build/
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*.egg
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# Virtual environments
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.venv/
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venv/
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env/
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# IDE
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.idea/
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.vscode/
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*.swp
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*.swo
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# OS
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.DS_Store
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Thumbs.db
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# Model checkpoints (do not commit large model files)
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*.safetensors
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*.bin
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*.pt
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*.pth
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*.ckpt
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# Training outputs
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output/
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saves/
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# Generated by the multi-node launch scripts
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env.txt
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hostfile
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pssh.hosts
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# Logs
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*.log
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logs/
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# Cache
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.cache/
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