Meta has recently released LLaMA, a collection of foundational large language models ranging from 7 to 65 billion parameters. LLaMA is creating a lot of excitement because it is smaller than GPT-3 but has better performance. For example, LLaMA's 13B architecture outperforms GPT-3 despite being 10 times smaller. This new collection of fundamental models opens the door to faster inference performance and chatGPT-like real-time assistants, while being cost-effective and running on a single GPU. However, LLaMA was not fine-tuned for instruction task with a Reinforcement Learning from Human Feedback (RLHF) training process.

ChatLLaMA is an experimental chatbot interface for interacting with variants of Facebook's LLaMA. This bot is using the 7 billion parameter variant fine-tuned on the Alpaca dataset.

Source: https://github.com/juncongmoo/chatllama