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DeepSeek-R1 is an open-source language design developed on DeepSeek-V3-Base that's been making waves in the AI community. Not only does it match-or even surpass-OpenAI's o1 model in numerous standards, but it likewise includes completely MIT-licensed weights. This marks it as the very first non-OpenAI/Google design to provide strong thinking capabilities in an open and available way.
What makes DeepSeek-R1 particularly interesting is its openness. Unlike the less-open techniques from some market leaders, DeepSeek has released a detailed training method in their paper.
The design is likewise remarkably economical, with input tokens costing just $0.14-0.55 per million (vs o1's $15) and output tokens at $2.19 per million (vs o1's $60).
Until ~ GPT-4, the common knowledge was that better models required more data and calculate. While that's still valid, models like o1 and R1 show an alternative: inference-time scaling through reasoning.
The Essentials
The DeepSeek-R1 paper presented several models, however main amongst them were R1 and R1-Zero. Following these are a series of distilled models that, while fascinating, I won't discuss here.
DeepSeek-R1 utilizes two significant concepts:
1. A multi-stage pipeline where a small set of cold-start data kickstarts the model, followed by large-scale RL.
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