Full Title: 🚨 Llama 3.1: Open-Source Finally Beats GPT
Highlights
The models can handle up to 128k tokens of context. This was achieved through a multi-stage process: initial pretraining on 8k token windows due to resource limits, followed by continued pretraining that gradually increased the context length to 128k tokens over six stages. (View Highlight)
With 128K context window Llama 3.1 will be great for RAG applications. And the main strength of 405B model is that it’s ideal for distilling smaller, task-specific expert models. So from synthetic data generation to model distillation, the possibilities are limitless with Llama 3.1 (View Highlight)
Model distillation transfers knowledge from a large teacher LLM to a smaller student model, aiming to maintain performance while reducing computational requirements. (View Highlight)
The process typically involves training the student model to mimic the output distribution of the teacher model, often using softmax with temperature scaling to emphasize informative soft targets. (View Highlight)
The current released version of Llama-3.1 is not yet multimodal. The image, video, and speech capabilities are integrated into Llama 3.1. However, these models are under development and not yet broadly released. (View Highlight)
Andrej Karpathy’s “Zero to Hero” course on neural networks is a comprehensive guide that takes learners from foundational principles to advanced techniques. (View Highlight)
This course features a series of YouTube videos and a GitHub repository containing Jupyter notebooks and exercises. It covers everything from basic neural network concepts to building sophisticated models like GPT. Each lecture is designed to provide hands-on experience and deepen understanding of neural networks. (View Highlight)
