From 10d22e6627d1c0dd9c63cb5a1be2f875dcb0056b Mon Sep 17 00:00:00 2001 From: Jason Liu Date: Sun, 15 Oct 2023 16:43:32 -0400 Subject: [PATCH] correct title --- docs/blog/posts/distilation-part1.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/blog/posts/distilation-part1.md b/docs/blog/posts/distilation-part1.md index d15ca6b..226b43c 100644 --- a/docs/blog/posts/distilation-part1.md +++ b/docs/blog/posts/distilation-part1.md @@ -9,7 +9,7 @@ tags: - experimental --- -# Experimental: End to End Distillation +# Experimental: Finetuning with `Instructions` from `Instructor` The core philosophy with the `instructor` library is to make language models backwards compatible with existing code. By adding Pydantic in the mix we're able to easily work with LLMs without much worry. @@ -22,7 +22,7 @@ By the end of this article, you'll understand how to easily integrate the end to ## Why You Should Care -Traditionally, implementing a complex prompt chain involved linking multiple chains together. Each llm call might need [data validation](https://jxnl.github.io/instructor/reask_validation/), external validations, follow up prompts and more. This can be a tedious process, especially if you're working with a large number of functions. Instead we might want to finetune a model that can handle the entire chain end to end. +Traditionally, implementing a complex prompt chain involved linking multiple chains together. Each llm call might need [data validation](https://jxnl.github.io/instructor/reask_validation/), external validations, follow up prompts and more. This can be a tedious process, especially if you're working with a large number of functions. Instead we might want to finetune a model that can handle the entire chain end to ### Anatomy of a Complex Function