DeepSeek R1 - o1 Performance, Completely Open-Source

Matthew Berman
20 Jan 202512:15

TLDRThe video script discusses the release of DeepSeek R1, an open-source AI model comparable to OpenAI's 01. It highlights the model's performance on various benchmarks, showing it rivals or exceeds 01 in several areas. DeepSeek R1 is fully open-source, MIT-licensed, and significantly cheaper than 01, with a hosted version available for free. The script also mentions the model's human-like thinking process and its ability to generate detailed reasoning steps. Additionally, it covers the technical aspects of how DeepSeek achieved this level of reasoning through reinforcement learning and multi-stage training.

Takeaways

  • ๐Ÿ˜€ DeepSeek R1 is an open-source model comparable to OpenAI's 01, with MIT licensing and significantly lower costs.
  • ๐Ÿ˜Ž The model performs impressively on various benchmarks, often matching or exceeding OpenAI's 01 in tasks like codeforces and AIM 2024.
  • ๐Ÿค“ DeepSeek has released distilled versions of the model, including smaller models like R1 distill quen 1.5, 7, 14, and 32b, which also perform very well.
  • ๐Ÿค– The model's reasoning process is human-like, showing a chain of thought that includes self-correction and consideration of multiple possibilities.
  • ๐Ÿ’ธ The pricing of DeepSeek R1 is a fraction of OpenAI's 01 models, with input API prices as low as $0.14 per million tokens and output prices at $2.19 per million tokens.
  • ๐Ÿ”— DeepSeek provides API outputs for fine-tuning and distillation, and the model weights are openly accessible for the community to leverage.
  • ๐Ÿ“š The technical paper released by DeepSeek details the training process, including the use of reinforcement learning without supervised fine-tuning.
  • ๐Ÿ’ก DeepSeek R10, a preliminary model, demonstrates remarkable reasoning capabilities through pure reinforcement learning, addressing issues like readability and language mixing.
  • ๐Ÿ› ๏ธ The model uses a group relative policy optimization strategy instead of a critic model, leading to more efficient and sophisticated problem-solving strategies.
  • ๐ŸŽ‰ This is a significant milestone for open-source AI, potentially leading to a flood of similar open-source thinking models and increased competition in the market.

Q & A

  • What is the significance of DeepSeek R1 being open-source?

    -DeepSeek R1 being open-source means that the model's weights and training methods are publicly available under the MIT license, allowing anyone to use, modify, and commercialize the model freely. This transparency and accessibility can lead to faster innovation, community-driven improvements, and reduced costs compared to proprietary models.

  • How does DeepSeek R1 compare to OpenAI's 01 in terms of performance?

    -DeepSeek R1 is on par with OpenAI's 01 in terms of performance. It beats OpenAI 01 on the AIM 2024 Benchmark for code forces, is close on GP QA Diamond, and slightly behind on Math 500 and MMLU. For the SWE bench, it performs comparably, showing that it is a strong contender in the open-source domain.

  • What are the distilled versions of DeepSeek R1, and how do they perform?

    -DeepSeek has released distilled versions of R1, including distill quen 1.5, 7, 14, and 32b, as well as R1 distill llama AP and 70b. These distilled models perform incredibly well, with the 70b version significantly outperforming GPT 40 on the AIM Benchmark and Live Codebench score, demonstrating the effectiveness of the distillation process.

  • What is the pricing difference between DeepSeek R1 and OpenAI's 01 models?

    -DeepSeek R1 is significantly cheaper than OpenAI's 01 models. The input API price for DeepSeek R1 is $0.14 per million tokens, compared to $7.5 for 01 and 01 preview. The output price for DeepSeek R1 is $2.19 per million tokens, compared to $60 for 01 preview and 01, making it a more cost-effective option for users.

  • How does DeepSeek R1 handle reasoning tasks compared to other models?

    -DeepSeek R1 demonstrates advanced reasoning capabilities, often showing human-like internal thought processes. It re-evaluates its initial approach to problems, allocates more thinking time, and considers multiple outcomes before arriving at a conclusion, which is a testament to its sophisticated reasoning abilities.

  • What is the 'cold start problem' that DeepSeek R10 addresses?

    -The 'cold start problem' refers to the challenge of training a model without relying on supervised fine-tuning or human feedback. DeepSeek R10 uses pure reinforcement learning to solve this problem, allowing the model to develop reasoning behaviors autonomously without the need for explicit human guidance.

  • How does DeepSeek R1 improve upon DeepSeek R10?

    -DeepSeek R1 incorporates multi-stage training and cold start data before reinforcement learning to address issues such as poor readability and language mixing encountered by DeepSeek R10. This results in enhanced reasoning performance and more coherent outputs.

  • What is the licensing model for DeepSeek R1, and why is it important?

    -DeepSeek R1 is licensed under the MIT license, which allows for clear open access and community leverage of model weights and outputs. This licensing model is important because it promotes transparency, encourages community contributions, and enables commercial use without restrictions, fostering a collaborative and innovative environment.

  • Can DeepSeek R1 be used for free, and if so, how?

    -Yes, DeepSeek R1 can be used for free. Users can access the model weights and use the API outputs for fine-tuning and distillation. The hosted version is also available for free at chat.deepseek.com, making it accessible to a wide range of users.

  • What are some potential future developments for open-source AI models based on DeepSeek R1?

    -Based on the success of DeepSeek R1, we can expect a flood of open-source thinking models in the future. These models will likely continue to close the performance gap with closed-source models, drive down costs, and increase competition in the AI market. Additionally, the open-source community will likely contribute to further improvements and innovations in AI technology.

Outlines

00:00

๐Ÿ˜€ Introduction to DeepSeek R1

The video script introduces DeepSeek R1, an open-source model comparable to OpenAI's 01. It highlights the model's open-source nature, including open weights and MIT licensing, and its cost-effectiveness. The script presents benchmark results showing DeepSeek R1's performance against other models, emphasizing its competitive edge. It also discusses the model's roadmap and the potential for future open-source models. The script mentions the availability of distilled versions of the model and provides details on pricing, showing significant cost savings compared to OpenAI's models.

05:01

๐Ÿ˜€ Testing DeepSeek R1's Reasoning Abilities

The script delves into testing DeepSeek R1's reasoning capabilities through various questions. It describes the model's human-like thought process and its ability to correct itself, as seen in the 'strawberry' test. The script also presents the model's detailed reasoning in the 'marble' question, showcasing its step-by-step approach and final conclusion. Additionally, it highlights the model's ability to generate sentences ending with a specific word, demonstrating its versatility and reasoning skills.

10:02

๐Ÿ˜€ DeepSeek R1's Technical Details and Future Implications

The final paragraph discusses the technical aspects of DeepSeek R1, including its training methods and the use of reinforcement learning without supervised fine-tuning. It explains the model's multi-stage training process and the removal of the critic model, leading to more efficient and sophisticated reasoning. The script mentions the model's ability to allocate more thinking time to problems, similar to AlphaGo's learning strategy. It concludes by encouraging viewers to explore the model further and highlights the significant advancements in open-source AI.

Mindmap

Keywords

๐Ÿ’กDeepSeek R1

DeepSeek R1 is an open-source model that is comparable to OpenAI's 01 thinking model. It is fully open-source, including its weights, and is licensed under MIT, which allows for free use, modification, and distribution. The video highlights that DeepSeek R1 is a significant milestone in the open-source AI community, as it demonstrates that open-source models can achieve performance levels similar to proprietary models. For example, the script mentions that DeepSeek R1 beats OpenAI 01 on the AIM 2024 Benchmark in terms of code forces, showing its strong reasoning capabilities.

๐Ÿ’กOpen Source

Open Source refers to the practice of making the source code of software available to the public, allowing anyone to view, modify, and distribute it. In the context of the video, the term is used to describe the nature of DeepSeek R1, which is completely open-source. This means that the model's weights and training methods are freely available, enabling the community to use, improve, and build upon the model. The video emphasizes the benefits of open-source, such as driving down costs and fostering innovation, as seen in the comparison of pricing between DeepSeek R1 and proprietary models like OpenAI's 01.

๐Ÿ’กMIT Licensed

MIT Licensed indicates that the software is released under the MIT License, a permissive free software license that allows users to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the software. In the video, DeepSeek R1 is described as being MIT Licensed, which means that users have the freedom to use the model for various purposes, including commercial applications, without the need for restrictive licensing agreements. This licensing model encourages widespread adoption and collaboration within the AI community.

๐Ÿ’กBenchmarks

Benchmarks are standardized tests used to evaluate the performance of software or models. In the video, benchmarks are used to compare the performance of DeepSeek R1 with other models, such as OpenAI's 01. The script mentions several benchmarks, including AIM 2024, Code Forces, and Math 500, where DeepSeek R1 shows competitive or superior performance. These benchmarks are crucial for demonstrating the model's capabilities and validating its effectiveness in various tasks, such as reasoning and problem-solving.

๐Ÿ’กDistilled Versions

Distilled Versions refer to smaller, more efficient versions of a model that are derived from a larger, more complex model through a process called knowledge distillation. In the video, DeepSeek R1 has several distilled versions, such as DeepSeek R1 distill quen 1.5, 7, 14, and 32b, and R1 distill llama AP and 70b. These distilled versions maintain the performance of the original model while being more lightweight and easier to deploy. The script highlights the impressive performance of these distilled versions, especially in comparison to other non-thinking models.

๐Ÿ’กAPI Outputs

API Outputs are the results or data that a model provides through an Application Programming Interface (API). In the context of the video, DeepSeek R1 offers API outputs that can be used for fine-tuning and distillation. This means that users can leverage the model's outputs to further customize and optimize the model for specific tasks or applications. The availability of API outputs is a key feature that enhances the flexibility and usability of DeepSeek R1.

๐Ÿ’กChain of Thought

Chain of Thought refers to the step-by-step reasoning process that a model goes through to arrive at a conclusion. In the video, DeepSeek R1 is described as having a human-like Chain of Thought, which means that it thinks through problems in a way that is similar to how humans do. The script provides examples of the model's internal thinking, such as when it counts the number of 'R's in the word 'strawberry' or reasons about the location of a marble in a glass. This Chain of Thought is a significant aspect of the model's reasoning capabilities and contributes to its human-like behavior.

๐Ÿ’กReinforcement Learning

Reinforcement Learning is a type of machine learning where an agent learns to make decisions by taking actions in an environment to maximize some notion of cumulative reward. In the video, DeepSeek R10, a preliminary model, is trained using large-scale reinforcement learning without supervised fine-tuning. This approach allows the model to develop reasoning capabilities autonomously, without the need for explicit human feedback. The script mentions that DeepSeek R10 demonstrates remarkable reasoning behaviors through reinforcement learning, which is a key innovation in the development of the model.

๐Ÿ’กCold Start Problem

Cold Start Problem refers to the challenge of initializing a model or system without prior data or experience. In the context of the video, DeepSeek R10 solves the cold start problem by using pure reinforcement learning, which means that it can start learning from scratch without relying on pre-existing data or human feedback. This is a significant achievement, as it allows the model to develop its reasoning abilities independently. The script highlights how DeepSeek R10 naturally emerges with powerful reasoning behaviors through this approach.

๐Ÿ’กGroup Relative Policy Optimization

Group Relative Policy Optimization is a strategy used in reinforcement learning to optimize the policy of an agent by comparing candidate results to a baseline, rather than using a critic model. In the video, DeepSeek R1 uses this strategy to enhance its reasoning performance. Instead of relying on a critic model to evaluate potential answers, the model compares the results to a baseline to determine which one might be correct. This approach removes the need for a separate critic model and allows for more efficient and effective optimization. The script explains that this strategy is part of the multi-stage training process of DeepSeek R1, contributing to its advanced reasoning capabilities.

Highlights

DeepSeek R1, an open-source model, matches OpenAI's 01 in performance and is MIT licensed.

DeepSeek R1 outperforms OpenAI 01 in several benchmarks, including AIM 2024 and Codeforces.

The model is a fraction of the price of OpenAI's 01, making it highly cost-effective.

DeepSeek has released distilled versions of the model, including R1 distill Quen 1.5, 7, 14, and 32b.

DeepSeek R1 is fully open-source, with model weights and outputs available for community use.

The model demonstrates human-like thinking processes, as seen in the 'strawberry' and 'marble' examples.

DeepSeek R10, a preliminary model, uses large-scale reinforcement learning without supervised fine-tuning.

DeepSeek R1 incorporates multi-stage training to enhance reasoning performance.

The model uses a group relative policy optimization strategy instead of a critic model.

DeepSeek R1 can generate sentences ending with a specific word, showcasing its versatility.

The model's performance is detailed in a technical paper, with a roadmap for future development.

DeepSeek R1 is commercially viable and can be used for free at chat.deepseek.com.

The model's output prices are significantly lower than OpenAI's 01, driving down costs.

DeepSeek R1's reasoning capabilities are comparable to OpenAI's 01, making it a strong alternative.

The model's development marks a significant milestone for open-source AI, encouraging further innovation.

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