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Fine-tuned Gemma 4 E4B for structured JSON extraction from regulatory docs - 75% to 94% accuracy, notebook + 432 examples included
Gemma 4 dropped this week so I fine-tuned E4B for a specific task: extracting structured JSON (doc type, obligations, key fields) from technical and regulatory documents. https://preview.redd.it/v7yg80prpetg1.png?width=1026 format=png auto=webp s=517fb50868405f90a94f60b54b04608bcedd2ced Results on held-out test set: - doc_type accuracy: 75% base → 94% fine-tuned - Hallucinated obligations: 1.25/doc → 0.59/doc - JSON validity: 100% - Field coverage: 100% Setup: - QLoRA 4-bit, LoRA r=16 alpha=16, Unsloth + TRL - 432 training examples across 8 doc types - 5 epochs on a single L4, ~10 min training time - Final train loss 1.04, eval loss 1.12 The whole thing is open: notebook, dataset, serve.py for FastAPI inference. https://github.com/spriyads-vault/gemma4-docparse Some things I learned the ha
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What I like about MATS and Research Management
Crossposted on my personal blog . This is post number 16 in my second attempt at doing Inkkaven in a day, i.e. to write 30 blogposts in a single day. MATS is an organization that pairs up-and-coming AI Safety researchers (who I call participants) with the world’s best (this is not an exaggeration) existing AI Safety researchers (called mentors), for a minimum of 3 months research experience, followed by 6 or 12 months of further time to pursue their research further if they meet a minimum standard. The most common role at MATS, called research manager but I prefer the term research coach, is all about providing 1-1 support to the participants. The participant-mentor relationship is purely based on the research: by default they meet weekly for 30 minutes and only discuss what research has h
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