Deep Seek
High-performance open-source language models and reasoning assistants.
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Use it free
Open the official tool or demo — no account needed.
Self-host it
Run the open-source version on your own infrastructure.
What is Deep Seek?
DeepSeek is an open-source large language model (LLM) developed by DeepSeek AI, designed to deliver advanced natural language processing capabilities through its 67 billion parameter architecture. Trained on a 2 trillion token dataset spanning English and Chinese, it addresses the need for, multilingual AI systems capable of complex reasoning and specialized tasks. Researchers, developers, and enterprises leverage DeepSeek to overcome challenges in areas like code generation, mathematical problem-solving, and language understanding. The project's MIT license and public API access democratize access to AI, enabling innovation while fostering collaboration in the open-source community.
How it works
DeepSeek is a family of large language models, including variants like DeepSeek LLM 7B/67B Base and Chat, engineered for advanced text generation, reasoning, and multilingual processing. Its primary purpose is to provide researchers and developers with a scalable, high-performance foundation for AI applications. The project prioritizes open-source accessibility, offering models trained on massive datasets to address gaps in language understanding, coding, and mathematical reasoning. It serves as a tool for both academic exploration and industrial deployment, with features like API integration and web/app interfaces. DeepSeek LLM 67B Base outperforms Llama2 70B Base in reasoning, coding, and Chinese comprehension. It achieves HumanEval Pass@1 scores of 73.78 in coding and GSM8K 0-shot accuracy of 84.1 for math problems. The DeepSeek Coder variant excels in programming tasks, while DeepSeek Math specializes in mathematical reasoning. The DeepSeek-V4-Pro version enhances agent capabilities and supports Codex integration.
How to use it
- 1Access the GitHub repository (deepseek-ai/DeepSeek-LLM) to download pre-trained models or explore evaluation benchmarks. 2. Use the official API documentation to integrate the model into applications via the open platform. 3. Test the model through the web interface or mobile app, which provides direct access to the latest DeepSeek-V4-Pro version. 4. Contribute to the project by reporting issues or submitting pull requests to the GitHub repository. Practical tips include leveraging the provided evaluation results to benchmark performance, utilizing the Codex integration for enhanced coding assistance, and consulting the API documentation for rate limit details and request formatting requirements.
What it can do
- open LLM
Use cases
Assumptions and limitations
Assumptions
- source: https://github.com/deepseek-ai/DeepSeek-LLM
- license: MIT — free to use
- privacy: Self-hosted — you control your data
Limitations
- Requires substantial computational resources for training and fine-tuning
- Performance may degrade with highly specialized or niche subject matter
- Limited real-time data access due to static training datasets
- Fine-tuning options are constrained by the open-source license terms
- Ethical considerations in generating code or mathematical content remain under active review
Understanding the result
High-performance open-source language models and reasoning assistants.
Tool details
- Clearly flagged when a network request is needed.
- No account, no sign-up, and no tracking of your content.
- Powered by (MIT).
- Built with
- (deepseek-ai/DeepSeek-LLM)
- License
- MIT
- Runs locally
- No — requires a network request
- Verification
- Not yet verified
- Input
- Query
- Output
- Text
Built with deepseek-ai/DeepSeek-LLM. OpenToolVault provides the discovery and browser interface while crediting the original project maintainers.
- Built with
- License
- MIT
Open-source project
OpenToolVault is an independent directory. We are not affiliated with or endorsed by this project.
References
- / — GitHub Repository
Upstream project · GitHub
- MIT License
Upstream project
Frequently asked
What is DeepSeek and what makes it unique?
DeepSeek is an open-source LLM with 67 billion parameters trained on 2 trillion tokens in English and Chinese. Its uniqueness lies in superior performance in coding (HumanEval Pass@1: 73.78), math (GSM8K 0-shot: 84.1), and Chinese comprehension compared to models like Llama2 70B. The project emphasizes research accessibility through MIT licensing and API integration.
How does DeepSeek's training data differ from other models?
DeepSeek is trained on a 2 trillion token dataset combining English and Chinese texts, with explicit focus on coding and mathematical corpora. This differs from models like GPT-3.5 which prioritize general internet text. The dual-language training enables stronger cross-lingual transfer capabilities, though the dataset lacks recent 2023-2024 internet content.
How can I start using DeepSeek for code generation?
Begin by visiting the official API documentation to obtain access credentials. Use the provided SDKs to integrate the DeepSeek Coder variant into your development environment. For immediate testing, use the web interface's code generation feature, which supports multiple programming languages and includes syntax validation.
How does DeepSeek compare to alternatives like Llama2 and ChatGPT?
DeepSeek outperforms Llama2 70B in coding and math tasks but lacks ChatGPT's conversational tuning. Unlike closed-source models, DeepSeek offers full transparency through its MIT license and open API. However, it lacks ChatGPT's commercial enterprise features and has a smaller community compared to Llama2, which has over 100k stars on Hugging Face.
What should I do if I encounter an API rate limit error?
Check the API documentation for your plan's request limits. Use the provided SDKs to implement rate limiting in your application. For temporary spikes, wait 15-30 minutes before retrying. If the issue persists, contact support with your API key and error logs to request an adjustment.