Thread by @ankur_maker

🧵 Exciting advances are being made in the field of large language models (LLMs)! Let's dive into some recent ones: Falcon-40B, LLaMA, Vicuna, and Alpaca
#AI #ChatGPT

Falcon-40B is an open-source LLM with an impressive 40 billion parameters, trained on a vast corpus of 1,000 billion tokens. It outperforms its predecessors in speed and smoothness of operation. Falcon-40B is also commercially available under Apache license.

Meta's LLaMA is a foundational LLM with versions spanning from 7B to 65B parameters. It's designed for researchers lacking the infrastructure for larger models, democratizing access in the field. The model is trained on 1.4 trillion tokens and can be fine-tuned for various tasks.

Vicuna, developed by a team from UC Berkeley, CMU, Stanford, and UC San Diego, is an open-source chatbot fine-tuned on LLaMA using conversations from ShareGPT. It's primarily used for research on LLMs and chatbots and is aimed at researchers and hobbyists in NLP, ML, and AI

🚀Introducing Alpaca 7B, a breakthrough in instruction-following language models! Trained on 52K demonstrations, this small but mighty model echoes the performance of OpenAI's text-davinci-003 at a fraction of the size and cost. Better AI is now more accessible than ever!

These models demonstrate the immense potential of LLMs in various applications, from business to research. However, it's important to remember that as the parameters and training data size increase, so do the challenges of bias and potential for generating misinformation.

Follow me (@ankur_maker) for AI insights and @LangChainAI tutorials.