A nerdy bug šĀ ā¢Ā 1y
May sound noob but what exactly makes DEEPSEEK better than other ai as I don't believe benchmarks much, I only understand that till now DEEPSEEK didn't use supervised learning unlike other llms so it learns at each step on its own, other AIs couldn't do this?
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LLM Post-Training: A Deep Dive into Reasoning LLMs This survey paper provides an in-depth examination of post-training methodologies in Large Language Models (LLMs) focusing on improving reasoning capabilities. While LLMs achieve strong performance
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In the ever-evolving AI landscape, a new player is making waves ā Deepseek. While OpenAI, Google DeepMind, and Meta AI have been dominant forces, Deepseek is emerging as a formidable contender in the AI race.The recent buzz around Deepseek stems from
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Indiaās biggest AI drop so far š®š³ Introducing Sarvam-30B and Sarvam-105B frontier LLMs built from India, for India. Sarvam-30B ⢠30B parameters (MoE) ⢠1B active params/token ⢠32K context window ⢠Trained on 16T tokens ⢠Competitive with Gemma-
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It took me three hours (yes, hours š) to make a list of all the new AIs out there. Seriously, I live and breathe in this stuffāand with new tools dropping every other day, I started saving them just to test later. Well, today was that day. I actuall
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MedialĀ ā¢Ā 7m
š šš½š²š»š š°+ šµš¼ššæš šæš²šš®šš°šµš¶š»š“ šš®šæš½š®ššµšāš š¬š šøš²šš»š¼šš². And I realized ā weāve been looking at LLMs the wrong way. Theyāre not just āAI models.ā Theyāre a new kind of computer. ⢠LLM = CPU ⢠Context window = mem
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Give me 2 minutes, I will tell you How to Learn Reinforcement Learning for LLMs A humorous analogy for reinforcement learning uses cake as an example.Ā Reinforcement learning, much like baking a cake, involves trial and error to achieve a desired ou
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SEO isnāt dead. Itās evolving LLM + SEO = LEO (LLM Engine Optimization). This is how brands are now getting traffic from ChatGPT, Claude, Perplexity, and other AI platforms. While most marketers still chase Google rankings, LEO is about relevance
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"A Survey on Post-Training of Large Language Models" This paper systematically categorizes post-training into five major paradigms: 1. Fine-Tuning 2. Alignment 3. Reasoning Enhancement 4. Efficiency Optimization 5. Integration & Adaptation 1ļøā£ Fin
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5 AI Topics Explained Like Youāre 5 (But Building the Future) Ever felt like AI is too complicated to talk about on Medial? Letās simplify the most advanced topics in AI into bite-sized, founder-friendly ideas. Because if you can explain it to
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