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I fine-tuned 3 models this week to understand why people fail. Used LLaMA-2-7B, Mistral-7B, and Phi-2. Different datasets. Different methods (full tuning vs LoRA vs QLoRA). Here's what I learned that nobody talks about: 1. Data quality > Data quan

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

"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 1️⃣ Fin

What Are Some Real Examples of Large Language Models, and How Are They Used? Large Language Models (LLMs) are revolutionizing the way we interact with artificial intelligence. From chatbots to code generators, these models are reshaping industries b

Steps to building AI systems with LLM's. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗟𝗟𝗠𝘀 (𝗟𝗮𝗿𝗴𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗠𝗼𝗱𝗲𝗹𝘀) • These are the 𝗯𝗿𝗮𝗶𝗻𝘀 of the system. • Examples: GPT (OpenAI), Gemini, Claude etc. • Th

Steps to building real-world AI systems. I've given a simple detailed explanation below. 𝗦𝘁𝗲𝗽 1 – 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 & 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 𝗟𝗮𝘆𝗲𝗿 • This is where all the 𝗵𝗲𝗮𝘃𝘆 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗵𝗮𝗽𝗽𝗲𝗻𝘀. • It provides the 𝗵𝗮𝗿�

any ai ml scientist here?, i wanna validate an idea of a platform that offers gpu renting, how this will work is:- there's a person A who has a heavy duty and strong pc and he does a lot of gaming on it but his pc remains ideal for 8 hours. and there

Jivi MedX outperformed other established LLMs such as OpenAI's GPT-4 and Google's Med-PaLM 2. Here are some of the key points about Jivi MedX: •Developed by JiviAI, a new startup founded in January 2024 by Ankur Jain. ( former BharatPe Chief Produ

Top 10 AI Research Papers Since 2015 🧠 1. Attention Is All You Need (Vaswani et al., 2017) Impact: Introduced the Transformer architecture, revolutionizing natural language processing (NLP). Key contribution: Attention mechanism, enabling models