Fine-Tuning Llama 3 on a Custom Dataset: Training LLM for a RAG Q&A Use Case on a Single GPU 

Full-text tutorial (requires MLExpert Pro): www.mlexpert.io/bootcamp/fine-tuning-llama-3-llm-for-rag What performance did you get with your fine-tuned model?

I trained it with 2 epochs and the result was amazing! Nice explanation btw!!

Thank you in have watch several videos on llama fine tuning With lots of differences you are the best ! Especially anout dataset and how you formatted it

Great stuff as usual. Very useful info!

I’m trying to fine-tune the LLaMA 3.1 8B model without quantization, but when I try to use the pipeline with the unquantized model, I encounter this error: Trying to set a tensor of shape torch.Size([128256, 4096]) in 'weight' (which has shape torch.Size([128264, 4096])), this looks incorrect. Do you know why this is happening? maybe i'm using the wrong pad token idk. Thanks in advance

can you share the notebook?

If we apply the same approach to a dataset with another language like France or Arabic, will the approach change? Assume same columns structure, and names like you showed in the video.

Im looking for this ❣️

can we follow the same steps to finetuning another model LMM like Mistral AI ?

Дали base model-a ще може да се тренира с двойки въпрос-отговор на Български?

Can we fine tune to 2bit model ?

Amazing video