Lore behind this project: i just had learned about llm and how to develop from ground up. and as anybody who is eager to try it hands on, i wanted to fine-tune an llm for a specific purpose. but it is not computationally feasible to train a model on large dataset to have a correct response without gpu.

however, i just wanted to stop coding different logic for each step in fine-tuning pipeline (data schema handling, sanitization, tokenization and for training experimentation adjust hyperparameters) and focus on quick experimentations. and thats the reason i built upasak package to fine-tune llm over ui, since this idea was also feasible in terms of implementation.

To summarize: you can fine-tune llm over ui, select the model, select or upload the dataset, sanitize your data, use lora for parameter efficient fine-tuning, adjust hyperparameters, monitor training, save model and push it hugging face hub if you want to make if publicly available.

For detailed explanation: https://github.com/shrut2702/upasak/blob/main/README.md

i have tried to make a one stop platform for fine-tuning llms without any extra overhead so users can focus on more experimentations or quick shipping. but still i'm aware of the issues and currently working to fix it. it is pre-release as of now, but after getting you guys' feedback (open to brutal feedbacks about package's performance and features) and having assured it works stable, i will make it major release. also, since it is open-source your contributions are welcomed.