funfunfunction

We've seen some pretty impressive results already, including training an HTML->JSON model that competes with frontier quality at 5% the cost called [schematron](https://huggingface.co/inference-net/Schematron-8B)

It's great for data extraction, chat, search, image classification, sentiment analysis, and more.

Trainings are free for the next 30 days as we test and experiment. We're also going to make it possible to launch and manage training runs on your own GPUs, so everything is private.

At the core of this project is a structured-extraction task using a custom Qwen 14B model, which we distilled from larger closed-source models. We needed a model we could run at scale on https://devnet.inference.net, which is comprised mostly of idle consumer-grade NVIDIA devices.

Embeddings were generated using SPECTER2, a transformer model from AllenAI specifically designed for scientific documents. The model processes each paper's title, executive summary, and research context to generate 768-dimensional embeddings optimized for semantic search over scientific literature.

The visualization uses UMAP to reduce the 768D embeddings to 3D coordinates, preserving local and global structure. K-Means clustering groups papers into ~100 clusters based on semantic similarity in the embedding space. Cluster labels are automatically generated using TF-IDF analysis of paper fields and key takeaways, identifying the most distinctive terms for each cluster.