High schooler by day, LLM builder by night. Driven by a deep love for both Physics and AI. Currently spending my runtime building on Hugging Face, experimenting with transformer architectures, and training custom LLMs.
Boris-2-75M: Trained on 26B tokens -- estimated to start training on August 12th. Boris-2-125M: Trained on 90B tokens -- Estimated to start training on August 20th. Boris-2-250M: Trained on 60B tokens -- Estimated to start training on September 10th.
Why does 125M get more tokens than 250M?
Well, the straight answer is time. It saves time, while still allowing the 250M model to exceed the 125M model.
Furthermore, we are attempting a unique architecture and layering scheme to hopefully end up around the strength of SmolLM2-135M. Fingers crossed!
We're building a dataset to study what humans actually consider AI slop.
SlopFinder shows you a random piece of AI-generated text and gives you one simple control: **how slop is it?** No categories. No complicated forms. Just vote and move on.
Every vote helps build the dataset. ๐งฉ
How does it work? Samples are pulled from existing datasets, shown anonymously, and collected into our annotation pool. After enough votes, they're exported to Hugging Face for everyone to use.
This is an early MVP, so the dataset is small and the system is still evolving.