Cool project. A team at work was building something similar to internal use.
I'm curious how this compares to just using Claude Code directly and giving it a dump of the agent traces? It seems like Claude could probably do some of the same diagnostics / trace grouping to identify failure patterns. Why use a custom harness?
Hi all, we built a super easy way to train small language models from your production data - install a gateway to save your request/response data from a frontier provider, and use the traces to train a open source model that can be hosted privately on your machine.
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.
We'll release the full data explorer soon, with more info.
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.
There is growing emphasis on efficiency as more companies adopt and scale with LLMs in their products.
Developers might be fine paying GPT-5-Super-AGI-Thinking-Max prices to use the very best models in Cursors, but (despite what some may think about Silicon Valley), businesses do care about efficiency.
And if you can fine-tune an 8b-parameter Llama model on GPT-5 data in < 48 hours and save $100k/mo, you're going to take that opportunity.