
Artificial Intelligence is evolving rapidly which is why DeepSeek.AI is at the forefront of this evolution. The company offers speedy, cost-effective, and free-of-cost AI options, DeepSeek.AI provides users with an extremely powerful platform for creating text multimodal reasoning, real-time search on the web. This guide will take users through the DeepSeek.AI interface as well as its main functions and the unique features that distinguish it from its competitors on the market.
What is DeepSeek.AI?
DeepSeek.AI is a cutting-edge AI platform specifically designed to aid users in tasks like natural language processing data analysis, creative writing, data analysis as well as coding. It makes use of the most advanced machine learning algorithms to deliver accurate, relevant and personalised responses. DeepSeek.AI is designed to be flexible which makes it suitable for both personal and business applications.
How to Get Started with DeepSeek.AI
1. Accessing the Platform
To begin making use of DeepSeek.AI, follow these steps:
- Visit DeepSeek.AI
- Sign-Up and Log In You can sign up for your own account for free, or login with already existing accounts.
- Web and App Accessibility: DeepSeek.AI is accessible through both mobile and web applications which allow you to download and install it on the device of your choice.
- Browse the Dashboard Once you’ve logged in, you’ll be welcomed by a simple and clear user interface that provides seamless navigation.
We present DeepSeek-V3, which is a robust Mixture-of-Experts (MoE) language model that includes the total of 671B parameters and 37B of them activated per token. To ensure an efficient inference process and a cost-effective training, DeepSeek-V3 uses the Multi-head Latent Attention (MLA) along with DeepSeekMoE models which were extensively validated in DeepSeek-V2. Additionally, DeepSeek-V3 has pioneered an auxiliary-loss-free approach to load balancing.
It also has the goal of training with multiple tokens to increase performance. We prepare DeepSeek-V3 for training on 14.8 trillion of diverse and high-quality tokens. This is then follow by Supervised Fine-Tuning as well as Reinforcement Learning phases to make the most of its potential.
A thorough analysis shows that DeepSeek-V3 is superior to other open-source models, and has performances comparable to top closed-source models. Despite its outstanding performance, it requires just 2.788M of H800 hours to perform its complete training. Additionally, the training is extremely stable. In the course of training we didn’t encounter any unrecoverable loss spikes, nor did we do any rollbacks.
Understanding the DeepSeek.AI Interface
The platform is designed in a way that makes it extremely interactive and user-friendly and interactive, and includes the following essential components:
1. Accessing the Platform
- Internet Interface: DeepSeek.AI can be accessed via the official site. Just sign up for an account and you’ll be presented with an intuitive and clean dashboard. Same for App.
- API integration Developers can benefit from this integration. DeepSeek.AI allows API access that allows seamless integration into existing workflows or applications.
2. Chat Interface
- Like the other AI chatbots DeepSeek is a real-time chat interface which allows users to type queries and get answers.
- Supports natural conversation in a language, as well as code support, and multiple inputs.
3. File Upload System
- supports uploads of large files as large as 100 MB
- Accepts different file formats including PDF PPT, DOCX and PPT. TXT, XLSX, as well as images.
- It allows AI-powered document summarization, data extraction and analysis of content.
4. Real-Time Web Search
- One of the most effective features is that DeepSeek.AI can look up more than 100 websites in real-time, bringing you the most current data.
- Offers references to sources to verify the credibility of content.
- It eliminates the requirement to manually surf the internet to search for information.
4. DeepThink (R1)
- DeepThink improves AI reasoning through the use of sophisticated chain-of-thought abilities, allowing it to analyse complex questions and provide insightful responses, and mimic logical processes for problem solving.
- This feature provides accurate, relevant responses to both creative and technical tasks.
Unique Features of DeepSeek.AI vs Market Rivals
DeepSeek.AI is different from rivals such as GPT-4o Claude Sonnet 3.5, and Gemini by offering a variety of unique advantages:
What Makes DeepSeek.AI Unique?
- Truely Open Source In contrast to GPT-4o or Claude, DeepSeek is an open-source model licensed by MIT that makes it accessible to customize and commercialization.
- No Limits on Use Unlike most competitors have strict limit on queries for users who are free, DeepSeek offers unlimited access to the internet.
- Bigger file handling -The program DeepSeek allows uploads of files of that can reach 100MB and is much more powerful than rivals.
- More Cost-Effective When compared with enterprises AI alternatives, DeepSeek provides a high-performance AI model for less than half the price.
- real-time web search across 100plus sources — Provides more extensive coverage than the majority of competitors AI models.
- Superior Options for Customization Features such as customized prompt templates enhance productivity and improve user experience.
DeepSeek.AI is rapidly changing the AI landscape, offering an open-source, low-cost as well as high-performance option to conventional AI giants. If you’re a researcher, developer or a creator of content This platform gives you an exceptional artificial intelligence experience.
So, why put off? Get into the realm of DeepSeek.AI and unleash all the potential that lies in AI now!
2. DeepSeek-V3 Model Summary
Architecture: Innovative Load Balancing Strategy and Training Objective
- In addition to the highly efficient structure of DeepSeek V2 we have invented an auxiliary-loss-free strategy for load-balancing which reduces the performance decline caused by the inclination to balance load.
- We explore a Multi-Token prediction (MTP) objective and show it is beneficial in terms of model performance. It is also utilized for speculative decoding in acceleration of inference.
Pre-Training: Towards Ultimate Training Efficiency
- We propose an FP8 mixed precision training framework. We also in the very first instance, verify the effectiveness and efficacy of FP8 training on a massive model.
- Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap.
This greatly improves our efficiency in training and lowers the cost of training, allowing us to expand the model’s size without adding cost. - For a price of just 2.664M HP800 hours of GPU time, we have completed the pre-training of DeepSeek V3 using 14.8T tokens, creating the current strongest Open-Source base model. The next stages of training after pre-training only require 0.1M CPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
- We introduce an innovative methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline cleverly integrates the reflection and verification pattern of the R1 model into the DeepSeek-V3 and dramatically enhances its reasoning capabilities. We also keep an control on the output design and length of the DeepSeek-V3.
How to Use DeepSeek Coder
DeepSeek Coder is a comprises series of models for code languages. That were trained from scratch using of code 87% and 13% natural languages in English & Chinese and Each model already trained on 2T tokens.
DeepSeek Coder is provide a range of sizes of codes models, range from 1B to 33B models. Each model is pre-trained on repo-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, resulting in foundational models (DeepSeek-Coder-Base). We further fine-tune the base model with 2B tokens of instruction data for get instruction-tuned models, namedly DeepSeek-Coder-Instruct.
- The training was based in two trillion tokens in an array of languages.
- There are a variety of sizes ( 1.3B, 5.7B, 6.7B and 33B) to meet different needs.
- The window’s size is sixteen thousand pixelssize that supports the project’s completeness of code and filling.
- The most advanced performance in the open-source models.
- Free and open source for commercial and research use.
DeepSeek Coder Performance
DeepSeek Coder Performance is tested on various coding-related bench-marks. The result shows that DeepSeek Coder Base-33B significantly outperforms existing open-source code LLMs.
When compared to CodeLLama-34B, it outperforms by 7.9 percent, 9.3%, 10.8 percent and 5.9 percentages in HumanEval Python, HumanEval Multilingual, MBPP and DS-1000. Surprisingly, our DeepSeek-Coder-Base-7B reaches the performance of CodeLlama-34B.
And the DeepSeek-Coder-Instruct-33B model after instruction tuning outperforms GPT-3.5-turbo on HumanEval and achieves comparable result with GPT-3.5-turbo on MBPP.
- – Try now, please visit our [
DeepSeek-Coder].
- – More details and evaluations are available on our [
Github].
- – Model weights are also available on [🤗 Huggingface]