DeepSeek-R1 - AI inference model from DeepSeek, performance aligned to OpenAI o1 release

What is DeepSeek-R1?

DeepSeek-R1 is a high-performance AI inference model from Hangzhou-based DeepSeek, benchmarking against OpenAI's o1 official version. The model is post-trained based on large-scale reinforcement learning techniques, and requires only a very small amount of labeled data to achieve excellent performance on tasks such as mathematical, coding, and natural language reasoning.DeepSeek-R1 is open-source under the MIT License, and supports distillation of the model, which allows users to freely use, modify, and commercialize it. The model's long-chain reasoning technology enables thought chains up to tens of thousands of words in length, which can gradually decompose complex problems and solve them based on multi-step logical reasoning, and is widely used in scientific research, natural language processing, education, and data analytics.

Key Features of DeepSeek-R1

• Excellent inference performance: It excels in complex tasks such as math, code generation and natural language reasoning, with reasoning capabilities comparable to OpenAI's o1 official version, supporting efficient processing of all types of complex logic problems.
• Efficient data utilization: With the help of reinforcement learning techniques and very small amount of labeled data for training, it significantly improves the inference ability of the model, dramatically reduces the cost of data labeling, and improves the training efficiency.
• Powerful model distillation support: Support users to distill models with DeepSeek-R1 outputs and train smaller models to meet the needs of specific application scenarios, such as deploying lightweight models on resource-constrained devices.
• Open source and flexible licenses: Following the MIT License open source, users can freely use, modify and commercialize, with high flexibility and scalability, applicable to a variety of development and research scenarios.

DeepSeek-R1's official website address

• GitHub repository::https://github.com/deepseek-ai/DeepSeek-R1
• HuggingFace Model Library::https://huggingface.co/deepseek-ai/DeepSeek-R1
• Technical Papers::https://github.com/deepseek-ai/DeepSeek-R1/blob/main/DeepSeek_R1

How to use DeepSeek-R1

• Official website experience: Access to DeepSeek'sOfficial website. Follow the instructions to complete registration and login. Turn on the "Deep Thinking" mode and call DeepSeek-R1 directly to complete various reasoning tasks.
• API Services::
  • Access to the API platform: Register and login to DeepSeek's API platform. Get the API key.
  • interface call: Set model='deepseek-reasoner' in the code to call the API interface. Sample code:

import requests

api_key = 'your_api_key'
url = 'https://api.deepseek.com/v1/inference'
headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}
data = {
    'model': 'deepseek-reasoner',
    'prompt': '你的问题或任务描述',
    'max_tokens': 100  # 输出的最大token数
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

• local deployment: Access the GitHub repository for DeepSeek-R1. Clone the repository to install the dependencies. Follow the instructions in the repository for model loading and inference. Sample code (Python):

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'deepseek-ai/DeepSeek-R1'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = '你的问题或任务描述'
inputs = tokenizer(prompt, return_tensors='pt')
output = model.generate(**inputs, max_length=100)
print(tokenizer.decode(output[0], skip_special_tokens=True))

Core Benefits of DeepSeek-R1

• Good reasoning skills: It performs well on tasks such as math, code, and natural language reasoning, and is comparable to the performance of OpenAI's o1 official version.
• Efficient utilization of data: Based on reinforcement learning techniques, only a small amount of labeled data is needed to significantly improve inference and reduce data costs.
• long chain inference technique: Based on long-chain reasoning, with chains of thought up to tens of thousands of words long, it can gradually decompose complex problems and improve the efficiency of complex tasks.
• Support for model distillation: Use the model output to train smaller models for specific scenarios, such as lightweight device deployment.
• Open source and flexible licenses: Follow the MIT License open source , users can freely use , modify and commercial , widely applicable .
• Wide range of application scenarios: Applicable to multiple fields such as scientific research, natural language processing, enterprise intelligence, education, data analysis, and so on.
• Efficient API services: Provides API interface, easy integration, reasonable pricing and suitable for large-scale commercial applications.

Who is DeepSeek-R1 for?

• (scientific) researcher: For researchers who need to perform complex mathematical modeling, algorithm optimization, and engineering technology research.
• natural language processing developer: For NLP developers working in natural language understanding, automated reasoning, and text generation.
• Corporate Technical Team: Ideal for enterprise teams that need to enhance their intelligent customer service, automated decision-making and personalized recommendation systems.
• Educators and students: Ideal for educators who need to help students master complex reasoning, and for students studying math and programming.
• Data analysis and decision support staff: For data analysts and decision makers who need to deal with complex logical reasoning tasks, market forecasting and strategy development.