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.
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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