On January 20, the DeepSeek-R1 model was released on HuggingFace. (See https://huggingface.co/deepseek-ai/DeepSeek-R1)
Key Highlights:
- It is the first open-source “reasoning” model (technically open-weights).
- It rivals OpenAI’s “reasoning” o1 model in many benchmarks.
- Its API cost is significantly lower than comparable AI models.
- The release included several “distilled” Qwen and Llama models capable of running locally (with suitable hardware).
What Are Distilled Models in DeepSeek-R1?
The distilled models (e.g., DeepSeek-R1-Distill-Qwen and DeepSeek-R1-Distill-Llama) are smaller, optimized versions of the base models (Qwen and Llama) that have been fine-tuned using reasoning data generated by DeepSeek-R1. This reasoning data is created through a combination of reinforcement learning (RL) and supervised fine-tuning (SFT) processes. The distilled models are not just fine-tuned with reasoning outputs but are specifically trained on 800,000 curated samples generated by DeepSeek-R1, which include both reasoning and non-reasoning tasks.
The distillation process allows these smaller models to retain much of the reasoning capabilities of the larger DeepSeek-R1 model, making them highly efficient for deployment on local hardware or edge devices. These distilled models are highly efficient, making them ideal for local deployment, offline applications, and security-conscious use cases. They outperform their base versions and even some larger models, demonstrating the effectiveness of distillation in transferring reasoning capabilities from larger to smaller models.
Take for example the DeepSeek-R1-Distill-Qwen-1.5B, a 1.5-billion-parameter model distilled from DeepSeek-R1, designed for efficient, reasoning-intensive tasks. Its small size allows it to run on consumer-grade hardware, making it ideal for local deployment, offline applications, and cost-sensitive use cases like edge AI, educational tools, and secure in-house systems. Despite its compact size, it achieves strong performance in reasoning tasks (e.g., 28.9% on AIME 2024 and 83.9% on MATH-500), outperforming larger non-reasoning models like GPT-4o in specialized domains. While it may lack the generalization of larger models, it demonstrates that smaller models can deliver powerful reasoning capabilities when distilled from advanced teacher models like DeepSeek-R1.
Why is DeepSeek-R1 a Watershed Moment?
- Driving Costs Down: DeepSeek’s affordable API and open-weights model will accelerate the reduction in costs for accessing high-quality AI. This will spur competition, enabling new companies to offer AI-as-a-Service and challenge existing big tech offerings.
- Enhanced Security: Security-conscious organizations can now run distilled models on in-house hardware, eliminating the need to send proprietary data to third-party servers.
- Offline AI: The distilled models enable the development of intelligent, autonomous reasoning systems that operate entirely offline, opening up new possibilities in robotics and edge computing.
- Open-Source Advantage: The open-weights model fosters innovation and customization, allowing developers to tailor the AI to their specific needs.
How to Use DeepSeek-R1
- For Web Users: Try it out at https://chat.deepseek.com/. Use the free DeepThink mode to experience the R1 model.
- For Mobile Users: Download the official app from the Google Play Store or Apple App Store. Be cautious — there’s another app in Play Store with the same name but from a different company and logo.
- For API Users: DeepSeek offers a cost-effective API (https://platform.deepseek.com/) with significantly lower pricing compared to other high-quality AI models, making it an attractive option for developers and businesses.
- For VS Code Users: Access it in Cline and Roo Code Extensions using an API key. I’m often switching between DeepSeek and Sonnet.
Happy coding!
