MinimalGPT

While open-sourcing generative models - LLaMa, GPT4all, FreedomGPT, etc have paved the way to kickstart GPT locally in ordinary CPUs, they are still far from the idea of 'Minimalist' tiny-GPT models.

GPT4 can take up to 32k tokens input to generate the next probable output token. It has been trained for over > 600 G datasets for months on supercomputing processing capabilities.

MinimalGPT approaches a totally opposite question: minimal resources are required to train a GPT model. With the MinimalGPT framework- the creation of GPT models (including vectorization), saving the data, and loading back from saved backup data for re-training/finetuning or inferencing, becomes a matter of single-line command prompt.