Instructions to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Intel/DeepSeek-V4-Flash-W4A16-AutoRound")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Intel/DeepSeek-V4-Flash-W4A16-AutoRound") model = AutoModelForCausalLM.from_pretrained("Intel/DeepSeek-V4-Flash-W4A16-AutoRound", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Intel/DeepSeek-V4-Flash-W4A16-AutoRound" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound
- SGLang
How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound 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 "Intel/DeepSeek-V4-Flash-W4A16-AutoRound" \ --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": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound", "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 "Intel/DeepSeek-V4-Flash-W4A16-AutoRound" \ --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": "Intel/DeepSeek-V4-Flash-W4A16-AutoRound", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Intel/DeepSeek-V4-Flash-W4A16-AutoRound with Docker Model Runner:
docker model run hf.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound
Download model-00003-of-00046.safetensors from Intel/DeepSeek-V4-Flash-W4A16-AutoRound: direct link, hf CLI and curl.
- Browser
- Download file 3.48 GB
-
https://huggingface.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound/resolve/main/model-00003-of-00046.safetensors
- Command line
-
hf download hf://Intel/DeepSeek-V4-Flash-W4A16-AutoRound/model-00003-of-00046.safetensors
-
curl -L -o model-00003-of-00046.safetensors https://huggingface.co/Intel/DeepSeek-V4-Flash-W4A16-AutoRound/resolve/main/model-00003-of-00046.safetensors
3.48 GB
- Xet hash:
- 79cb856c32760d64475420b461f3b17ee09be9674bdf112a704ff630a198f8fe
- Size of remote file:
- 3.48 GB
- SHA256:
- 9d0a73917baa25d1c01e3a42df95aba1dc2cb68c558e518bce9d0e1a196110fe
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