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Working on a cool AI project, that involves vector db and LLM fine-tuning

India should focus on fine-tuning existing AI models and building applications rather than investing heavily in foundational models or AI chips, says Groq CEO Jonathan Ross. Is this the right strategy for India to lead in AI innovation? Thoughts?

Hey everyone, I'm an engineering student geeking out over Generative AI. Loving LangChain, Hugging Face models, Crew.ai's chatbots, fine-tuning, and RAG. Plus, machine learning and data science are pretty cool too! Anyone else into this stuff? Looki

"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

Fine-Tuning: The Secret Sauce of AI Magic! Ever wonder how AI gets so smart? It’s all about fine-tuning! Imagine a pre-trained model as a genius with general knowledge. 🧠✨ Fine-tuning takes that genius and hones its skills for a specific task, li

Hey everyone, I'm an engineering student geeking out over Generative AI. Loving LangChain, Hugging Face models, Crew.ai's chatbots, fine-tuning, and RAG. Plus, machine learning and data science are pretty cool too! Anyone else into this stuff? Looki

I really want to work on AI projects but I'm inexperienced with company work, I used to work as a research intern at a lab and was a data science intern at another place. I really want to get into working on hf models, langchain, langgraph, fine-tuni

💡An Idea to Change the Game for AI Startups: Making AI Processing Faster, Cheaper, and Effortless Running AI models like ChatGPT, DALL·E, or AlphaCode is a computing monster—they need massive power to function, which makes them expensive to operate

10 Best LLM Tools to Simplify Your Workflow 1️⃣ LangChain LangChain's flexibility suits complex AI and multi-stage processing. 2️⃣ Cohere Cohere offers integration, scalability, and customization. 3️⃣ Falcon Falcon offers cost-effective, high-per

Food for thought. Instead of building AI models/agents/RAGs, why not build for AI. There is a dearth of auxiliary products and services for AI development such as benchmarking, training environments, data cleaning, synthetic data generation etc. But