Instructions to use deepseek-ai/DeepSeek-V4-Flash-0731 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-V4-Flash-0731 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V4-Flash-0731") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-V4-Flash-0731") model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V4-Flash-0731", device_map="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepseek-ai/DeepSeek-V4-Flash-0731 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/DeepSeek-V4-Flash-0731" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepseek-ai/DeepSeek-V4-Flash-0731", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-V4-Flash-0731
- SGLang
How to use deepseek-ai/DeepSeek-V4-Flash-0731 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 "deepseek-ai/DeepSeek-V4-Flash-0731" \ --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": "deepseek-ai/DeepSeek-V4-Flash-0731", "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 "deepseek-ai/DeepSeek-V4-Flash-0731" \ --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": "deepseek-ai/DeepSeek-V4-Flash-0731", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-V4-Flash-0731 with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-V4-Flash-0731
Going completely mad
Absolutely insane benchmarks.
True Arts!!!!!
INSANE
First date, and it feels like a match made in heaven! Great job Deepseek team!
The benchmark score of this model fully aligns with the hands-on coding experience, with no exaggeration whatsoever. Totally INSANE model in such a small size.
Mind-blowing benchmark results.
i'm usually not one to give much thought to benchmarks but holy crap. these benchmark numbers are absolutely WILD for a model this size. my daily driver right now is glm-5.2 on a local rig. i thought i was set for the entire year, but the glm-5.2 has literally been running 3 weeks. now I'm going to have to give this deepseek a try. i'll say the benchmarks made me do it.
Is this the beginning of the small model era?
It cannot be stressed either how this model is costing 9 CENTS on OpenRouter rn (DeepInfra, the cheapest provider serving this model). Which is schizophrenic pricing, what the hell is going on
Is this the beginning of the small model era?
that's not an slm dpsk with 284 b parameters moe active 13 b parameters u can call it llm ...but if there was something called mlm u could call it that ...

