An open source "prototype" AI model used for AI research.

Leaf is an "experimental" AI model, utilising PyTorch.

With leaf we've been testing many capabilities of what AI could do.

Starting with a simple "embedded" python dataset, leaf uses only 2700 steps for training (the more steps, the better it learns).

Training Data: {"this is a much longer text that will serve as a simple dataset for our tiny language model. The model will learn to predict the next character based on the previous characters in the sequence."} {"text": "This demonstrates the core idea behind training an autoregressive language model. The quick brown fox jumps over the lazy dog."} {"text": "A journey of a thousand miles begins with a single step. The early bird catches the worm. All that glitters is not gold. A stitch in time saves nine."} {"text": "Where there's a will, there's a way. Look before you leap. You can't make an omelette without breaking a few eggs. Practice makes perfect. Don't count your chickens before they hatch."}

However this result came with the following output:

Then we used JSONL databases from the community, and unfortunatly this was the output: