Fine-tuning Tiny LLM on Your Data | Sentiment Analysis with TinyLlama and LoRA on a Single GPU 

Full text tutorial (requires MLExpert Pro): www.mlexpert.io/bootcamp/fine-tuning-tiny-llm-on-custom-dataset

Thank you very much for this wonderful video. Among other things, the details you give are really very useful!

Thanks for this totorial

Can u send notebook of this tutorial

Such an ontime tutorial! I'm working on some SLMs and need insights on Fine-tuning parameters. Your video is a huge help, thx for that! Couldn't find the colab for this project in the repository, any chance the colab is available? Btw I'm one of your mlexpert members.

Please explain one interesting moment: First you add special token and then enlage embedding dimension to take this new token into account. At that point the new embedding is initialized by random values. Later you apply to target modules and embedding layer is absent in that list. My questions: 1) when you will train new embedding you have just added? Original model is freezed, only LoRA layers will be trained by trainer(). 2) why you do not add ###Titel, ###Text and ###Prediction as special tokens and let'em be part of the text?

Hi thanks!! A question for a model in which I have more than 2,000 pdfs. Do you recommend improving the handling of vector databases? When do you recommend fine tunning and when do you recommend vector database

Thanks!

Thanks

thanks for the video. but ... is that a language model? idk a lot about Ai, but it looks like a multi class classification. LLM is supposed to be like chat gpt right ?

Hello. Thank you for this work! I don't see the jupyter notebook in the github repo.

getting NameError: DataCollatorForCompletionOnlyLM not found. I also checked the docs. I didn't find any Class named DataCollatorForCompletionOnlyLM

Kind of hard to tell if this is a close match to needs...where I can't see anything at all...