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How to think about creating a dataset for LLM fine-tuning evaluation
- msp26 2y agoFor tasks like data extraction, are people doing full finetunes or training a LoRA? Is it any different for classification?
- clarionbell 2y agoUnless you have GPUs available, LoRa is the only accessible option. That being said, if your task is simple enough, you can skip the problem entirely and just pick a small base model.
- swalsh 2y agoLoRA is perfect for that. Where it fails is when you want to teach a new domain.
- avgbusinessuser 2y agogreat series of posts, i went down a similar path recently for a slightly different use case - i did not use axolotl though, i was worried about missing out on understanding some details due to potential abstractions. it's great to see documentation on how others tackle similar problems, i documented the process i went through here - https://atredis.com/blog/2024/6/3/how-to-train-your-large-language-model https://atredis.com/blog/2024/6/3/how-to-train-your-large-la...
- strickvl 2y agoThere's a ton of abstraction in axolotl, for sure, but so far I haven't found that it gets in the way. The main competitor in that space seems to be Unsloth, but that only works with a single GPU machine, so didn't fit my purposes. I'll dive into your blogpost. Thanks for posting!
- avgbusinessuser 2y agoI used unsloth, I was only using a single GPU for testing - looking forward to follow up posts.
- hinkley 2y agoWhen you get good enough at filtering the dataset for training, do you still need an AI, or do you understand the problem domain and can use a deterministic system?