Ask HN: Guides, books or repos for LLM fine-tuning

Post by Eric_BB » Thu, Jul 06, 2023, 7:46 PM UTC

janalsncm wrote: post #10

Eric_BB wrote: post #9

Earlier quoted context omitted.

This is what I was thinking about using LLM for: 1. As a feature extractor. For example, given the text of misinformation agents, what are the characteristics? C1, C2, C3, etc. Then, do these characteristics appear in these new texts? Assign a label accordingly. 2. I'll give LLM the text on how they usually behave and ask if these new ones are behaving similarly. If so, label them accordingly. (There may also be the…

Those might work to some extent but keep in mind the model doesn’t have access to outside information, and it’s going to be nearly impossible to build a social graph given Twitter API limits. IMO the easiest way to fine tune your model would be to use something like BERT embeddings fine tuned with triplet loss i.e. (example, positive, negative) to train the model to minimize distance between similar examples and maxi…

Post by Eric_BB » Thu, Jul 06, 2023, 11:41 PM UTC

extasia wrote: post #12

I'd recommend huggingface's transformers library. You probably don't wanna be finetuning these LLMs yourself since that's a big endeavour, but secondly because they already have the NLU capabilities to solve your problem. You basically want to train a single classification layer on top of your models output. I believe it'll be something like "CausalLMForClassification" in the huggingface docs. As far as resources go…