DeepSeek R1
DeepSeek-R1 is a cutting-edge AI reasoning model that combines reinforcement learning with supervised fine-tuning. Excels in complex problem-solving, mathematics, and coding tasks.
API
If you're looking for an API, you can choose from your desired programming language.
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const axios = require('axios');
const api_key = "YOUR API-KEY";
const url = "https://api.segmind.com/v1/deepseek-reasoner";
const data = {
"messages": [
{
"role": "user",
"content": "tell me a joke on cats"
},
{
"role": "assistant",
"content": "here is a joke about cats..."
},
{
"role": "user",
"content": "now a joke on dogs"
}
]
};
(async function() {
try {
const response = await axios.post(url, data, { headers: { 'x-api-key': api_key } });
console.log(response.data);
} catch (error) {
console.error('Error:', error.response.data);
}
})();Attributes
An array of objects containing the role and content
Could be "user", "assistant" or "system".
A string containing the user's query or the assistant's response.
To keep track of your credit usage, you can inspect the response headers of each API call. The x-remaining-credits property will indicate the number of remaining credits in your account. Ensure you monitor this value to avoid any disruptions in your API usage.
Resources to get you started
Everything you need to know to get the most out of DeepSeek R1
DeepSeek-R1
DeepSeek-R1 is a first-generation reasoning model developed by DeepSeek-AI, designed to excel in complex problem-solving. It builds upon the foundation of the DeepSeek-V3-Base model and incorporates advancements in reinforcement learning (RL). The model comes in several versions, including DeepSeek-R1-Zero and various distilled models.
Key Features of DeepSeek-R1
Advanced Reasoning: The model uses a unique training pipeline combining reinforcement learning and supervised fine-tuning to achieve high performance in reasoning, math, and code-related tasks.
Reinforcement Learning: DeepSeek-R1-Zero was trained using large-scale reinforcement learning without supervised fine-tuning, enabling self-verification, reflection, and long chain-of-thought reasoning.
Cold-Start Data: To address issues like repetition, readability, and language mixing in DeepSeek-R1-Zero, DeepSeek-R1 incorporates cold-start data prior to RL training.
Distillation: The reasoning capabilities have been successfully transferred into smaller models while maintaining high performance.
Open Source: The base models and six dense distilled models based on Llama and Qwen are open-sourced for research.
Performance: DeepSeek-R1 achieves performance comparable to OpenAI's models across various benchmarks, with some distilled models outperforming OpenAI-o1-mini.
Parameters: 671B total with 37B activated parameters
Context Length: 128K
Performance Highlights of DeepSeek-R1
- •
Outperforms several models in English, code, math, and Chinese benchmarks
- •
Achieves top scores in MMLU-Redux, DROP, AlpacaEval2.0, ArenaHard, Codeforces, and AIME 2024
- •
DeepSeek-R1-Distill-Qwen-32B sets new state-of-the-art results for dense models.
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