DeepSeek R1 - o1 Performance, Completely Open-Source
Deep Seek R1: The Open Source AI Model
Introduction to Deep Seek R1
- Deep Seek R1 is an open-source AI model that competes with OpenAI's models, offering similar capabilities at a fraction of the cost.
- Released approximately three months after OpenAI's latest model, it showcases significant advancements in open-source technology.
Benchmark Performance
- In various benchmarks, Deep Seek R1 outperforms or matches OpenAI's models across several tasks, indicating its competitive edge.
- Notably, it excels against CLA’s cutting-edge models and GPT-4 in most categories except for specific coding benchmarks.
Implications of Open Source Development
- The success of Deep Seek R1 may inspire a surge in open-source thinking models as other companies recognize its viability.
- Predictions suggest that within three months, we could see even more advanced open-source models emerging.
Licensing and Accessibility
- The model is MIT licensed, allowing users to freely commercialize and utilize it without restrictions.
- Users can access the model weights and API outputs for fine-tuning purposes; links will be provided for easy access.
Cost Comparison with Closed Source Models
- Pricing analysis shows that Deep Seek R1 offers significantly lower costs compared to OpenAI’s offerings—$0.14 per million tokens versus $7.5 for their main models.
- This pricing strategy exemplifies how open source can drive down costs while enhancing competition in the AI market.
Testing and Internal Reasoning Capabilities
- Initial tests reveal that Deep Seek R1 exhibits human-like reasoning processes when solving problems, such as counting letters in words.
Understanding Reasoning Models
Exploring the Marble Problem
- The speaker discusses a reasoning model's approach to a problem involving a marble in an upside-down glass, noting that traditional models think step-by-step by default.
- The output from the model shows extensive thinking and consideration of various outcomes regarding the marble's position after inverting the glass.
- It is highlighted that standard marbles are typically smaller than the mouth of a glass, leading to confusion about whether the marble remains inside or falls out when inverted.
- The conclusion drawn is that once the glass is turned over, gravity causes the marble to fall onto the table, indicating it cannot be inside anymore.
- The speaker notes that there’s no definitive way to know if the marble is in or out of the glass post-inversion since it falls out immediately.
Model Capabilities and Limitations
- A new test is introduced where users request ten sentences ending with "apple," showcasing how models can struggle with specific tasks like this one.
- The model successfully generates ten sentences ending with "apple," demonstrating its improved capabilities and highlighting each sentence distinctly.
- Discussion shifts to deep learning models like Deep Seek R10, which utilize large-scale reinforcement learning without needing supervised fine-tuning for enhanced reasoning abilities.
Advancements in Deep Learning Models
- Deep Seek R10 addresses cold start problems using pure reinforcement learning techniques, allowing it to develop powerful reasoning behaviors despite challenges like poor readability.
- To improve performance further, Deep Seek R1 incorporates multi-stage training before reinforcement learning, achieving significant advancements in reasoning capabilities.
Innovative Training Strategies
- Instead of employing a critic model for evaluating candidate answers, Deep Seek uses group relative policy optimization strategies to determine baseline responses effectively.
- A template for prompting within Deep Seek R10 illustrates how user queries are processed through an internal reasoning framework before providing answers.
Reinforcement Learning Insights
- An important insight reveals that Deep Seek learns to allocate more time for complex problems by reassessing initial approaches—showcasing advanced reasoning development through reinforcement learning incentives.