Instructions to use lhca521/DeepSeek-V4-Pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lhca521/DeepSeek-V4-Pro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lhca521/DeepSeek-V4-Pro")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lhca521/DeepSeek-V4-Pro") model = AutoModelForCausalLM.from_pretrained("lhca521/DeepSeek-V4-Pro") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lhca521/DeepSeek-V4-Pro with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lhca521/DeepSeek-V4-Pro" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lhca521/DeepSeek-V4-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lhca521/DeepSeek-V4-Pro
- SGLang
How to use lhca521/DeepSeek-V4-Pro 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 "lhca521/DeepSeek-V4-Pro" \ --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": "lhca521/DeepSeek-V4-Pro", "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 "lhca521/DeepSeek-V4-Pro" \ --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": "lhca521/DeepSeek-V4-Pro", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lhca521/DeepSeek-V4-Pro with Docker Model Runner:
docker model run hf.co/lhca521/DeepSeek-V4-Pro
- Xet hash:
- 79f68a67f9af7d20aa3b8bab7532a7368e8c57a59ff2c41c83e7a0fcc0095c7a
- Size of remote file:
- 13.9 GB
- SHA256:
- 90c8bcd93574b932491b71445b6bc1c898e9f42ed381160b5cd6a6febfdc06bd
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