The more I'm seeing of A.I. advancement, I'm coming to the concluding that better isn't always better, and the real battleground isn't so much which is the best when many of the better A.I. models are so close to each other in quality, the real battleground for me is the quality for the size, so a bit like we do for hardware, performance per watt, but in this case, performance per billion parameters, if you can maintain or have better quality at a smaller size, that is a major advantage, especially if it's open source and can run locally on your hardware. So as good as the big A.I. models are, they are too tightly controlled and very limited in how you can run them, in most cases online because of how big they are, the real game changer I think is with the smaller open source models that you can run locally, the advantage they've got is that they can be fully integrated and specialised in the OS, apps and games, they also have the advantage of less privacy, security and other concerns like that. If the current advancements of A.I. models continues and hardware continues to progress, I suspect the online big models are not going to matter that much as the smaller ones we can run locally will be able to do most of the things we want, and that's when things get really interesting as A.I. gets far more integrated into our daily lives, something that's really limited with these online centralised A.I. models and for countless reasons. At the end of the day, what's going to win out isn't going to be the best, good enough will do for most of us, what will really win out is what is smaller, capable and can be run locally, which basically rules out the big online A.I. services as there are too many privacy and security concerns with them, especially as A.I. becomes more capable and integrated into our lives.
hello sir !!!! wonderful contribution!!! can you practically train the model on the data so that we can learn . I am new to this field and your channel is amazing. thanks
Nice . Can we run this in our local machine and what config needed to run in local mackbook. Or colab is preferred please let me know.Also can you suggest is mackbook good for handling LLMS
Training should be conducted on a CUDA device, but the resulting model can be used on MPS devices (MacBook M series) and CPUs. For fine-tuning models on Mac using MLX-a powerful, open-source array framework for Apple silicon-there's a vibrant community supporting it.
Is it normal that the fined tuned version response with the ### Instruction, ### Input, ### Response pattern. Do I have a alternative in the training section, when i want only the response?
nice video but as most of the other in the same topic use an all ready dataset... i would prefer to see a video juat for a basic construction of a custom alpaca dataset... I think is what is missing from the most of the same kind tutorials.. the logic and the method to create your own alpaca dataset, what if a question has more than one answer? what if a simple question need to be clarified by the user depending of two probabilities ? and then follows the answer based on the clarification user inputs etc ....
Could you make a video on how to create a training set to fine-tune a model? I want to fine-tune a model like LLAMA-3.1 that creates YAML sections for different tasks similar to ansible. For example when I prompt: "Create a user alice" it should generate a YAML in a specific format like user: action: create username: alice Can you show how we can create such a training set. I can't create thousands of training data manually.
@@engineerprompt Could you give an example? You mean like explain the format of the YAML file, make an example and e.g. write "Whenever I create an user, output this YAML file"?
This is so funny, been looking for this yesterday and today now. Maybe I'm just now realizing after 20 years of google searching experience that I'm bad at googling.
When I am using this code "model.push_to_hub_merged("My_Modal_Path", tokenizer, save_method="merged_16bit")" it shows this error "TypeError: argument of type 'NoneType' is not iterable". All files are saved successfully, but when unsloth trying to upload it shows this error.
