How to train a Million Context LLM — with Mark Huang of Gradient.ai

Scaling Llama3 beyond 1M context window with ~perfect utilization, the difference between ALiBi and RoPE, how to use GPT-4 to create synthetic data for your context extension finetunes, and more! Full writeup: https://www.latent.space/p/gradient 00:00:00 Introductions 00:01:30 Founding story of Gradient and its mission 00:04:35 "Minimum viable agents" 00:09:19 Differentiating ML and AI, focusing on out-of-domain generalization 00:10:12 Extending Llama3 to 1M tokens 00:14:32 Technical challenges with long context sequences 00:17:45 Data quality and the importance of diverse datasets 00:19:45 What's a theta value? 00:22:42 RoPE vs Ring Attention vs ALiBi vs YaARN 00:25:06 Why RingAttention matters 00:28:01 How to refine datasets for context extension 00:33:34 Multi-stage training data and avoiding overfitting to recent data 00:34:27 The potential of using synthetic data in training 00:38:22 Applying LoRa adapters to extend model capabilities 00:42:25 Benchmarking long context models and evaluating their performance 00:47:20 Pushing to 4M context and output quality degradation 00:50:08 What do you need this context for? 00:52:57 Impact of long context in chat vs Docs Summarization 00:56:25 Future directions for long context models and multimodality 00:59:38 How do you know what research matters? 01:02:47 Routine for staying updated with AI research and industry news 01:05:33 Deciding which AI developments to invest time in 01:10:37 Request for collaboration and data set construction for long context