Instructions to use deepseek-ai/DeepSeek-V4-Pro-0813 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-V4-Pro-0813 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-V4-Pro-0813") 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-Pro-0813") model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-V4-Pro-0813", device_map="auto") - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepseek-ai/DeepSeek-V4-Pro-0813 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-Pro-0813" # 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-Pro-0813", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-V4-Pro-0813
- SGLang
How to use deepseek-ai/DeepSeek-V4-Pro-0813 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-Pro-0813" \ --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-Pro-0813", "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-Pro-0813" \ --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-Pro-0813", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-V4-Pro-0813 with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-V4-Pro-0813
大家也早点休息吧
唉,不说了兄弟们😄不说了,走了走了🙂……加油DeepSeek,下次优化干回来好吧,下次干回来(后仰)😕,(叹气)大家也早点休息吧。
这V4 Pro GA真tm废😡,这正式版性能真tm废呀今天,太废了(眼保健操)😫,tm灰度时候吹得跟神一样,一发GA直接负优化,跑分拉胯,推理慢得跟个蜗牛一样,灰度那版多丝滑啊,你看看现在这个回答质量真的😓,我真服了🤦,哎呀妈不说了我真tm的唉呀不说了这,这辈子都过不去了这辈子真的,一辈子就靠一个灰度版本吹牛B吗,woc真服了,早知道不该升级了😭
可以了,ds内部估计有些事情不好说,flash已经如此香,还要啥自行车。
说实话ds这把刀太快,如果pro真能追平fable5 那不仅国外的要斩死几个,国内像千问,混元,glm那些也得被斩,那就不好了。
战术性涨价,保护一波友军。
唉,不说了兄弟们😄不说了,走了走了🙂……加油DeepSeek,下次优化干回来好吧,下次干回来(后仰)😕,(叹气)大家也早点休息吧。
这V4 Pro GA真tm废😡,这正式版性能真tm废呀今天,太废了(眼保健操)😫,tm灰度时候吹得跟神一样,一发GA直接负优化,跑分拉胯,推理慢得跟个蜗牛一样,灰度那版多丝滑啊,你看看现在这个回答质量真的😓,我真服了🤦,哎呀妈不说了我真tm的唉呀不说了这,这辈子都过不去了这辈子真的,一辈子就靠一个灰度版本吹牛B吗,woc真服了,早知道不该升级了😭
这里也有玩大手么
唉,不说了兄弟们😄不说了,走了走了🙂……加油DeepSeek,下次优化干回来好吧,下次干回来(后仰)😕,(叹气)大家也早点休息吧。
这V4 Pro GA真tm废😡,这正式版性能真tm废呀今天,太废了(眼保健操)😫,tm灰度时候吹得跟神一样,一发GA直接负优化,跑分拉胯,推理慢得跟个蜗牛一样,灰度那版多丝滑啊,你看看现在这个回答质量真的😓,我真服了🤦,哎呀妈不说了我真tm的唉呀不说了这,这辈子都过不去了这辈子真的,一辈子就靠一个灰度版本吹牛B吗,woc真服了,早知道不该升级了😭
不考😡
唉,不说了兄弟们😄不说了,走了走了🙂……加油DeepSeek,下次优化干回来好吧,下次干回来(后仰)😕,(叹气)大家也早点休息吧。
这V4 Pro GA真tm废😡,这正式版性能真tm废呀今天,太废了(眼保健操)😫,tm灰度时候吹得跟神一样,一发GA直接负优化,跑分拉胯,推理慢得跟个蜗牛一样,灰度那版多丝滑啊,你看看现在这个回答质量真的😓,我真服了🤦,哎呀妈不说了我真tm的唉呀不说了这,这辈子都过不去了这辈子真的,一辈子就靠一个灰度版本吹牛B吗,woc真服了,早知道不该升级了😭
一个两个三个……ohhhhhhh!GLM5.3他全接了!真不愧是清华亲传大弟子,Mythos的接班人!