The Long Context RAG Capabilities of OpenAI o1 and Google Gemini
Retrieval Augmented Generation (RAG) is the top use case for Databricks customers who want to customize AI workflows on their own data. The...
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Long Context RAG Performance of LLMs
Retrieval Augmented Generation (RAG) is the most widely adopted generative AI use case among our customers. RAG enhances the accuracy of LLMs by...
How Long Should You Train Your Language Model?
How long should you train your language model? How large should your model be? In today's generative AI landscape, these are multi-million dollar...
LIMIT: Less Is More for Instruction Tuning
How should you finetune a large language model for general-purpose question answering? One intriguing approach is that of supervised finetuning on a small...
MosaicBERT: Pretraining BERT from Scratch for $20
With the MosaicBERT architecture + training recipe, you can now pretrain a competitive BERT-Base model from scratch on the MosaicML platform for $20...
Efficiently Estimating Pareto Frontiers with Cyclic Learning Rate Schedules
Benchmarking the tradeoff between model accuracy and training time is computationally expensive. Cyclic learning rate schedules can construct a tradeoff curve in a...