Key insights

• 🚀 Deep Seek R1 surpasses benchmarks and outperforms other models, setting the stage for open-source thinking models in the future
• 💡 Performance on par with OpenAI, commercially viable and available for free, with API outputs for fine-tuning and distillation at a fraction of the price
• 💭 Open-source model reduces price and increases competition, demonstrating human-like internal thinking with accurate and inaccurate outputs
• 🔍 Solving the marble question by considering different options and reasoning, exploring gravity's effect and possible outcomes
• 🧠 Deep Seek models demonstrate remarkable reasoning capabilities, while encountering challenges like poor readability and language mixing
• 📝 Discussion on using group relative policy optimization, creation of a template for DeepSeek R10, and an aha moment about reinforcement learning and problem-solving strategies

Q&A

• What strategy is used in place of a Critic model, and what templates are available for Deep Seek R10?

  The segment discusses the implementation of the group relative policy optimization strategy instead of a Critic model, along with the availability of a template for Deep Seek R10 for prompting. Additionally, it highlights an 'aha' moment about reinforcement learning and problem-solving strategies, showcasing the success of reinforcement learning in developing advanced problem-solving strategies and the achievements of the AlphaGo team through reinforcement learning.

• What does the segment on Deep Seek models cover?

  The segment discusses the remarkable reasoning capabilities of Deep Seek models, their training process, and the challenges faced, including language mixing and the cold start problem. It also outlines the introduction of Deep Seek R1 to address issues and enhance performance, thereby highlighting the model's strengths and the steps taken to improve its functionality.

• What are the capabilities of the open-source thinking model discussed?

  The open-source model demonstrates human-like thinking with both accurate and inaccurate internal thoughts. Its output price is much lower than that of the 01 models due to open source driving down prices and increasing competition. It showcases remarkable reasoning abilities and corrects itself even when incorrect, making it an impressive and cost-effective option.

• How does Deep Seek R1 compare to OpenAI's 01 model?

  Deep Seek R1 has surpassed various benchmarks, outperformed other models, and is significantly cheaper than OpenAI's 01 model. It has achieved performance on par with OpenAI, making it a more cost-effective alternative. Additionally, it is fully open source, MIT licensed, and commercially viable, with API outputs available for fine-tuning and distillation.

• What is Deep Seek R1?

  Deep Seek R1 is an open-source thinking model equivalent to OpenAI's 01 model. It has surpassed various benchmarks, outperformed other models, and is fully open source and MIT licensed. The model is commercially viable, with released distilled versions performing incredibly well and available for free. It also comes with API outputs for fine-tuning and distillation at a fraction of the price of other models.

• 00:00 An open-source deep seek R1 model, equivalent to open AI's 01 thinking model, is now available. It is completely open-source and significantly cheaper than 01. It has surpassed many benchmarks and outperformed other models, setting the stage for more open-source thinking models in the future.
• 02:04 Deep Seek blog post discusses the performance of their new open source model, which is MIT licensed and fully open source. They have released distilled versions of the model that perform incredibly well and are available for free. The model is commercially viable and comes with API outputs for fine-tuning and distillation at a fraction of the price of other models.
• 04:13 An open-source model demonstrates human-like thinking, with both accurate and inaccurate internal thoughts. Its output price is much lower than the 01 models due to open source driving down prices and increasing competition.
• 06:10 Solving the marble question by considering various possibilities and reasoning step by step.
• 07:54 The segment discusses the capabilities of deep seek models, their training process, and challenges faced, including language mixing and cold start problem. The models demonstrate remarkable reasoning abilities but encounter issues like poor readability. The paper details the training process and the introduction of deep seek R1 to enhance performance.
• 10:03 A discussion on a group relative policy optimization strategy used instead of a Critic model, along with a template for DeepSeek R10 and an aha moment about reinforcement learning and problem-solving strategies.