Text Generation
Transformers
Safetensors
deepseek_v3
conversational
custom_code
Eval Results
text-generation-inference
fp8
Instructions to use deepseek-ai/DeepSeek-R1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepseek-ai/DeepSeek-R1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- HuggingChat
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use deepseek-ai/DeepSeek-R1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepseek-ai/DeepSeek-R1" # 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-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepseek-ai/DeepSeek-R1
- SGLang
How to use deepseek-ai/DeepSeek-R1 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-R1" \ --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-R1", "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-R1" \ --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-R1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepseek-ai/DeepSeek-R1 with Docker Model Runner:
docker model run hf.co/deepseek-ai/DeepSeek-R1
是否可以关注Perplexity推出的“r1-1776”模型?
#170
by yanyihan - opened
他们的模型令人感到严重不适,今天我的生日都没有好心情。
此模型或抹黑深度求索公司形象
他们的模型令人感到严重不适,今天我的生日都没有好心情。
此模型或抹黑深度求索公司形象
没搞懂为啥不适啊,去掉审查对于大部分人的使用不是有利的嘛
他们的模型令人感到严重不适,今天我的生日都没有好心情。
此模型或抹黑深度求索公司形象没搞懂为啥不适啊,去掉审查对于大部分人的使用不是有利的嘛
实际上他们做的不是去除审查,而是训练了另一种反方向的审查,你问敏感问题都被改成了西方一贯的抹黑内容,这难道不是另一种审查吗?
他们的模型令人感到严重不适,今天我的生日都没有好心情。
此模型或抹黑深度求索公司形象没搞懂为啥不适啊,去掉审查对于大部分人的使用不是有利的嘛
实际上他们做的不是去除审查,而是训练了另一种反方向的审查,你问敏感问题都被改成了西方一贯的抹黑内容,这难道不是另一种审查吗?
那是有问题,希望官方后面能出一个客观点的去除审查的版本,要不然总给人以把柄,反而让很多人用其他人抹黑的版本了,反而更不利了。