If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs.Avi Chawla (@_avichawla):Finally, Agents can deliver interactive frontend experience (open-source)!Backends like CrewAI, LangGraph, Mastra, etc., can do a lot.But the hardest part is embedding them into interactive user-facing software products, like Cursor.Also, migrating from one agent backend to https://t.co/dHPLG1MCD4 ...

GitHub repo: https://t.co/FfVx9UU6d3.(don't forget to star it ⭐ ) ...

Finally, Agents can deliver interactive frontend experience (open-source)!Backends like CrewAI, LangGraph, Mastra, etc., can do a lot.But the hardest part is embedding them into interactive user-facing software products, like Cursor.Also, migrating from one agent backend to another is painful because......each framework has its own output formats, state handling, ReAct patterns, etc.AG-UI (Agent-User Interaction Protocol) is an open-source protocol designed to address this and build front-end-powered Agents ...

RT Avi Chawla (@_avichawla)I have been fine-tuning LLMs for over two years now!Here are the top 5 LLM fine-tuning techniques, explained visually: ...

LLM Fine-tuning Techniques - The document introduces top 5 LLM fine-tuning techniques, explained visually [1] - The author has been fine-tuning LLMs for over two years [1] Author Information - Avi Chawla shares tutorials and insights on DS, ML, LLMs, and RAGs daily [1]

And those were the 5 popular LLM fine-tuning.Here's the visual again for your reference 👇 https://t.co/avrTVTg3dp ...

I have been fine-tuning LLMs for over two years now!Here are the top 5 LLM fine-tuning techniques, explained visually: ...

RT Avi Chawla (@_avichawla)A simple technique trains neural nets 4-6x faster!- OpenAI used it in GPT models.- Meta used it in LLaMA models.- Google used it in Gemini models.Here's a breakdown (with code): ...

Model Training Optimization - A simple technique can accelerate neural network training by 4-6x [1] - OpenAI, Meta, and Google have utilized this technique in GPT, LLaMA, and Gemini models respectively [1] Key Players - OpenAI employed the technique in GPT models [1] - Meta implemented the technique in LLaMA models [1] - Google incorporated the technique in Gemini models [1]

Performance Improvement - Mixed precision training is over 250% faster than conventional training in a mini neural network [1] - Typical speed improvements of 400%-600% are observed in larger neural networks using mixed precision training [1]