DeepSeek-R1 is an open-source language design constructed on DeepSeek-V3-Base that's been making waves in the AI neighborhood. Not just does it match-or galgbtqhistoryproject.org even surpass-OpenAI's o1 model in lots of benchmarks, but it likewise includes completely MIT-licensed weights. This marks it as the first non-OpenAI/Google design to provide strong reasoning capabilities in an open and available way.
What makes DeepSeek-R1 especially exciting is its openness. Unlike the less-open methods from some industry leaders, DeepSeek has published a detailed training methodology in their paper.
The design is likewise remarkably cost-efficient, with input tokens costing simply $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 wisdom was that better models needed more information and compute. While that's still valid, designs like o1 and R1 show an option: inference-time scaling through thinking.
The Essentials
The DeepSeek-R1 paper presented several designs, but main amongst them were R1 and R1-Zero. Following these are a series of distilled designs that, while intriguing, I won't discuss here.
DeepSeek-R1 utilizes two major [classicrock.awardspace.biz](http://classicrock.awardspace.biz/index.php?PHPSESSID=d84b231bd037e2b56e639221b197c2a3&action=profile
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Understanding DeepSeek R1
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