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Fine-tuning LLMs for longer context and better RAG systems

Blog post from Anyscale

Post Details
Company
Date Published
Author
Artur Niederfahrenhorst, Kourosh Hakhamaneshi
Word Count
2,847
Company Posts That Month
1
Language
English
Hacker News Points
1
Post removed?
No
Summary

Anyscale Endpoints and Private Endpoints are now available as part of the Anyscale Platform, offering a cost-effective solution for fine-tuning models with long context lengths. The "Needle In A Haystack" benchmark has been refined to make it more challenging and relevant to RAG applications. A generalizable and scalable procedure for creating synthetic fine-tuning datasets using Anyscale Endpoints has been demonstrated, enabling the creation of custom fine-tuning datasets for specific use cases. Fine-tuned models have been benchmarked against popular alternatives, showcasing the effectiveness of the dataset and fine-tuning procedure in achieving competitive performance while reducing costs. The study highlights the importance of considering cost and accuracy when choosing a model for production use-cases.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
AI Model Fine-tuning 21 488 102 67 +10%
LLM 10 2,642 331 143 -5%
RAG 6 1,170 162 61 -17%
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