DeepSeek-R1: A Deep Dive into Reasoning Models and Their Distilled Counterparts

The world of Large Language Models (LLMs) is constantly evolving, and DeepSeek is making waves with its first-generation reasoning models. Designed for performance comparable to OpenAI's cutting-edge models, DeepSeek-R1 and its distilled versions offer intriguing possibilities for developers and researchers alike. This article will explore DeepSeek-R1, its architecture, its distilled versions, and how to use them with Ollama.

What is DeepSeek-R1?

DeepSeek-R1 represents DeepSeek's initial foray into sophisticated reasoning models. It aims to rival the capabilities of models like OpenAI's offerings, particularly in areas like:

• Mathematics: Solving complex equations and performing mathematical reasoning.
• Coding: Generating, understanding, and debugging code in various programming languages.
• General Reasoning: Analyzing information, drawing inferences, and solving logic problems.

DeepSeek-R1 stands out due to its design as a foundation model from which smaller, more efficient models can be derived through a process called distillation.

Understanding Model Distillation

Model distillation is a crucial concept in understanding DeepSeek's approach. It involves training smaller models to mimic the behavior of a larger, more powerful model. This offers several advantages:

• Improved Efficiency: Smaller models require less computational power and memory, making them suitable for deployment on resource-constrained devices.
• Faster Inference: Smaller models generate responses more quickly.
• Preserved Reasoning Abilities: The distilled models retain a significant portion of the reasoning capabilities of the original, larger model.

DeepSeek's approach demonstrates that knowledge and reasoning patterns can be effectively transferred from larger models to smaller ones, achieving better performance than training small models from scratch using reinforcement learning.

DeepSeek-R1 Distilled Models

DeepSeek offers a range of distilled models based on the DeepSeek-R1 architecture, utilizing popular base models like Llama and Qwen. Here’s a breakdown of available options:

• DeepSeek-R1-Distill-Qwen-1.5B: A highly compact model, ideal for low-resource environments. Use with ollama run deepseek-r1:1.5b.
• DeepSeek-R1-Distill-Qwen-7B: A balance between size and performance. Use with ollama run deepseek-r1:7b.
• DeepSeek-R1-Distill-Llama-8B: Leverages the Llama architecture for reasoning tasks. Use with ollama run deepseek-r1:8b.
• DeepSeek-R1-Distill-Qwen-14B: Offers improved performance compared to the 7B model. Use with ollama run deepseek-r1:14b.
• DeepSeek-R1-Distill-Qwen-32B: A larger model designed for more complex reasoning challenges. Use with ollama run deepseek-r1:32b.
• DeepSeek-R1-Distill-Llama-70B: One of the most powerful distilled models, offering near-original DeepSeek-R1 performance. Use with ollama run deepseek-r1:70b.
• DeepSeek-R1: The full-sized base model, offering complete performance (${\sim}$404GB). Use with ollama run deepseek-r1:671b.

Getting Started with DeepSeek-R1 and Ollama

Ollama makes it incredibly easy to run DeepSeek-R1 and its distilled models. Ollama packages models into a self-contained format, including all dependencies, making deployment straightforward. To get started:

1. Download and Install Ollama: Visit the Ollama download page and follow the instructions for your operating system.
2. Run your desired DeepSeek Model: Use the ollama run command, specifying the model you want to use (e.g., ollama run deepseek-r1:7b). Ollama downloads the model if it's not already present and prepares it for use.
3. Interact with the Model: Once the model is loaded, you can start providing prompts and receiving generated text!

Licensing and Commercial Use

A significant advantage of the DeepSeek-R1 series is its permissive MIT License. This license allows for:

• Commercial Use: You can use DeepSeek-R1 and its distilled models in commercial applications.
• Modifications: You're free to modify the models to suit your specific needs.
• Derivative Works: You can create derivative works based on the models, including further distillation or fine-tuning for specialized tasks.

Important Note: The Qwen distilled models are derived from Qwen-2.5 series, which are originally licensed under Apache 2.0 License, and now finetuned with 800k samples curated with DeepSeek-R1. The Llama 8B distilled model is derived from Llama3.1-8B-Base and is originally licensed under llama3.1 license. The Llama 70B distilled model is derived from Llama3.3-70B-Instruct and is originally licensed under llama3.3 license.

Conclusion

DeepSeek-R1 and its distilled models represent a significant advancement in the field of open-source reasoning models. Its performance, combined with the ease of use provided by Ollama and the permissive MIT license, makes it an attractive option for developers, researchers, and businesses looking to leverage the power of LLMs. Whether you need a compact model for edge deployment or a powerful model for complex tasks, the DeepSeek-R1 family has something to offer. Stay tuned to DeepSeek's ongoing developments and explore the possibilities these models unlock! Always ensure to read the DeepSeek's Terms of Use before usage.