Get ready to dive deep into the world of fine-tuning task specific language models with Python functions. We'll explore how the instructor.instructions streamlines this process, making the task you want to distil more efficient and powerful while preserving its original functionality and backwards compatibility.
If you want to see the full example checkout examples/distillation
With the advent of large language models (LLM), retrieval augmented generation (RAG) has become a hot topic. However throughout the past year of helping startups integrate LLMs into their stack I've noticed that the pattern of taking user queries, embedding them, and directly searching a vector store is effectively demoware.
What is RAG?
Retrieval augmented generation (RAG) is a technique that uses an LLM to generate responses, but uses a search backend to augment the generation. In the past year using text embeddings with a vector databases has been the most popular approach I've seen being socialized.
So let's kick things off by examining what I like to call the 'Dumb' RAG Model—a basic setup that's more common than you'd think.
Language models have seen significant growth. Using them effectively often requires complex frameworks. This post discusses how Instructor simplifies this process using Pydantic.