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Introducing RAT: Retrieval Argument Thinking. Extract just the reasoning process from deepSeek-r1 and send it to any LLM via . Boost the second LLM's performance and gain access to missing capabilities like function calling and JSON mode. 👇

Pietro Schirano

102,147 subscribers

532,880 次观看 • 1 年前 •via X (Twitter)

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Pietro Schirano 的头像

Pietro Schirano1 年前

🐀🐀🐀

Pietro Schirano 的头像

Pietro Schirano1 年前

You can install RAT as a command-line tool, allowing you to run it from anywhere by simply typing `rat`. There is also a specialized implementation designed for Claude models that leverages Anthropic's message prefilling capabilities. This version makes Claude believe the reasoning process is its own internal thought process, leading to more coherent and contextually aware responses.

CodeRabbit 的头像

CodeRabbit1 年前

AI-first pull request reviewer with context-aware feedback, line-by-line code suggestions, and real-time chat.

Pietro Schirano 的头像

Pietro Schirano1 年前

I meant Augmented lol 🤦‍♂️

Zain 的头像

Zain1 年前

@OpenRouterAI Shouldn't it be TAG - thinking augmented generation?

The Canaanite 的头像

The Canaanite1 年前

@OpenRouterAI @lateinteraction I cannot not think of DSPy when I see this, lol. It feels like pure gold.

Carlos 的头像

Carlos1 年前

@OpenRouterAI We’re so early that we can just make things up

Ryszard Rzepa 的头像

Ryszard Rzepa1 年前

@OpenRouterAI This is just so good. Get the smart ai do the thinking and “less smart” do the job.

YamangilAnil 的头像

YamangilAnil1 年前

@OpenRouterAI Great! Have been waiting to see this since franken-sonnet introduction of yours lol. It is a freaking good idea. And keeping even only "original" thought token is a really powerful boost. Will try to introduce more proper CoT synthesis + instruction set to Claude as prefill..

🐧 lalo adrian morales 𝕏 的头像

🐧 lalo adrian morales 𝕏1 年前

@OpenRouterAI nice!!! i like the name !

Fábio Zé Domingues 的头像

Fábio Zé Domingues1 年前

@OpenRouterAI Ufff. That last paragraph said it all. Tks

相关视频

By the way, you can extract JUST the reasoning from deepseek-reasoner, which means you can send that thinking process to any model you want before they answer you. Like here where I turn gpt-3.5 turbo into an absolute genius!

By the way, you can extract JUST the reasoning from deepseek-reasoner, which means you can send that thinking process to any model you want before they answer you. Like here where I turn gpt-3.5 turbo into an absolute genius!

Pietro Schirano

1,324,620 次观看 • 1 年前

DeepSeek R1 671B just broke speed records at 198 t/s it is now the fastest reasoning model available you can try it in coding mode on anychat soon prompt: make two fancy D3.js animated looping speedometers, first one is title deepseek R1 671B and second one is OpenAI o3-mini, deep seek should go up to 198 tokens/second and openai o3-mini should go up to 174 tokens/second

DeepSeek R1 671B just broke speed records at 198 t/s it is now the fastest reasoning model available you can try it in coding mode on anychat soon prompt: make two fancy D3.js animated looping speedometers, first one is title deepseek R1 671B and second one is OpenAI o3-mini, deep seek should go up to 198 tokens/second and openai o3-mini should go up to 174 tokens/second

AK

135,360 次观看 • 1 年前

The most amazing aspect of DeepSeek Open Source is that just the Reasoning Engine can be isolated and used with any other LLM. In fact you can have a mixture of Reasoning Engines cascade around a problem and then use an Operator type agent AI to function on the the results.

The most amazing aspect of DeepSeek Open Source is that just the Reasoning Engine can be isolated and used with any other LLM. In fact you can have a mixture of Reasoning Engines cascade around a problem and then use an Operator type agent AI to function on the the results.

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398,739 次观看 • 1 年前

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23,011 次观看 • 1 年前

🚨 DeepSeek just dropped DeepSeek R1-0528 and it's INSANE for coding Built an entire game with Nebius Studio in three turn. The improvement from previous versions is wild. - API access available - Safe to use If this is R1... DeepSeek R2 is going to be absolutely nuts. How to access 👇 1/3

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thirdweb

955,999 次观看 • 1 年前

Turn any AI Model into Reasoning Model with Deepseek r1 <thinking> Architecture. Models like GPT4o and Sonnet 3.5 are Implementation Models But a new breakthrough with Deepseek can make them a Reasoning model. Here's a step-by-step Explanation: 🧵

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192,262 次观看 • 1 年前

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The DeepSeek-R1 paper is a gem! Highly encourage everyone to read it. It's clear that LLM reasoning capabilities can be learned in different ways. RL, if applied correctly and at scale, can lead to some really powerful and interesting scaling and emergent properties. There is more to RL than meets the eye! Here is my breakdown of the paper along with a few tests: The multi-state training might not make sense initially but they provide clues on optimizations that we can continue to tap into. Data quality is still very important for enhancing the usability of the LLM. Unlike other reasoning LLMs, DeepSeek-R1's training recipe and weights are open so we can build on top of it. This opens up exciting research opportunities. About the attached clip: the previous preview model wasn't able to solve this task. DeepSeek-R1 can solve this and many other tasks that o1 can solve. It's a very good model for coding and math.

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140,600 次观看 • 1 年前

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880,229 次观看 • 1 年前

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