In this video I will answer your questions regarding my previous video where I showed you how to run the LLaMA and Alpaca model on your local computer. To do this, I will answer what the input/output token size of the models is and how to fine-tune the models. I will also show you in more detail how the Standford researchers generated the training data and how they fine-tuned the LLaMA model. I will also show you the Alpaca-LoRA repository, which is particularly useful if you want to train the model on a single GPU or fine-tune the model for multiple tasks. I'll also show you that the Alpaca model could be much better than you thought. I'll also give you an example of how to generate code using the Alpaca model and how to run the Alpaca LoRA repository code on a GPU using Google Colab. In addition, I'll also show you the RAM requirements for each LLaMa model variant and explain why the models work best in English. Enjoy! :-) Medium article where I answered even more questions: https://medium.com/p/b7e20b28e9e1 Colab notebook used for running the Alpaca-LoRA model: https://colab.research.google.com/drive/1ZfMTIjjSCUKIci_zglqX94JEKuoRTTdI?usp=sharing 00:00:00 Intro 00:00:45 How can the LLaMA and Alpaca models be fine-tuned (also input/output token limit)? 00:07:25 Can the models generate code (WATCH THIS if you are disappointed with the results of the Alpaca model)? 00:11:24 Do these models use the GPU? 00:11:49 What are the RAM requirements to run larger LLaMA variants? 00:12:07 Do the models work for languages other than English? 00:12:52 Can we use this to query a database with natural language? 00:13:29 More questions answered in my Medium article 00:14:03 Outro References: Dalai Repository: https://github.com/cocktailpeanut/dalai Stanford Alpaca Repository: https://github.com/tatsu-lab/stanford_alpaca Alpaca-LoRA Repository: https://github.com/tloen/alpaca-lora Alpaca-LoRA-13B Model Weights: https://huggingface.co/Draff/llama-alpaca-stuff/tree/main/Alpaca-Loras/13b-e3 LoRA Paper: https://arxiv.org/pdf/2106.09685v2.pdf LLaMA Paper: https://arxiv.org/pdf/2302.13971v1.pdf Llama.cpp Repository: https://github.com/ggerganov/llama.cpp https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022 https://github.com/tloen/alpaca-lora/pull/32 https://github.com/facebookresearch/llama/issues/148 https://crfm.stanford.edu/2023/03/13/alpaca.html Stay in Touch Medium https://medium.com/@martin-thissen LinkedIn https://linkedin.com/in/mthissen135/ YouTube Of course, feel free to subscribe to my channel! :-) https://patreon.com/MartinThissen (of course, financial support is completely voluntary, but I was asked for this)