16 lines
2.9 KiB
Markdown
16 lines
2.9 KiB
Markdown
# [The Illustrated DeepSeek-R1](https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1/) (Extends Chapters 12)
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In Chapter 12, we go through common techniques for creating and fine-tuning a model, namely language modeling, supervised fine-tuning and preference tuning. This chapter focuses on non-reasoning models and shows how you can fine-tune a model yourself.
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The impact of DeepSeek-R1 has been phenomenol as an open-weights LLM rivaling OpenAI's o1 model. DeepSeek-R1 is a reasoning LLM that was released unexpectly.
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[The Illustrated DeepSeek-R1](https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1/) explores the model and its training process. It goes through the various steps they use to create a model with such exception capabilities.
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Interestingly, the model uses rule-based verifiers to make sure that it's reasoning process follows a certain standard, such as making sure that the code can actually compile:
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<a href="https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1/"><img src="../images/bonus_deepseek1.png" width="70%" height="70%"></a>
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The architecture is that of a [Mixture-of-Experts](https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-mixture-of-experts) and with 256 experts (8 activated at a time), quite large:
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<a href="https://newsletter.languagemodels.co/p/the-illustrated-deepseek-r1/"><img src="../images/bonus_deepseek2.png" width="70%" height="70%"></a>
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