I loved Andrej’s talk about in his “Busy person’s intro to Large Language Models” video, so I decided to create a reading list to dive in deeper to a lot of the topics. I feel like he did a great job of describing the state of the art for anyone from an ML Researcher to any engineer who is interested in learning more.
The full talk can be found here: https://youtu.be/zjkBMFhNj_g?si=fPvPyOVmV-FCTFEx
Here’s the reading list: https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro-to-large-language-models-video/
Let me know if you have any other papers you would add!
Nice! Thank you for your work.
Regarding the video.
Q1) minute 14:14 Finetuning into an Assistant, when you have multiple tasks / datasets with diverse outputs how is training performed ? Are all datasets combined in a single training ? Or Is finetuning done over a previous finetuning ? Or the question is parsed and sent to a specific model ?
Q2) minute 27:43 Tool Use (Browser, Calculator, etc. ) Anyone has links for similar implementations for llama and how is done or what kind of tech/frameworks are used ?
You certainly can combine all the tasks and datasets into a single instruction fine tuning dataset. Then you would have a separate dataset for the reinforcement learning half where the model is learning human preferences.
The naive way is to use langchain, but that’s hit and miss for several reasons, and whatever you build will be held together by duct tape and prayers. Alternative frameworks include Haystack and Griptape.
I’ve found that for local models the best tool-usage you can get is by using an advanced control library. This gives you a lot of flexibility in organising the prompts and “helping” the local models a lot. Guidance and LMQL are two such libraries.
Thanks. Guidance seems a good fit I’ll start looking for more info.