Dharma Field Extractor: Any Document In, Structured Fields in JSON Out
- ID
- 43059
- Status
- summarized
- Published
- 05 Nov 2026, 1:50 AM
- Fetched
- 05 Nov 2026, 3:10 AM
- Provider
- Hugging Face Blog
- Category
- developer-ai
- Original URL
- https://huggingface.co/blog/Dharma-AI/dharma-field-extractor
- Source URL
- https://huggingface.co/blog/feed.xml
Summary
- Score
- 5.5
- Created
- 05 Nov 2026, 3:10 AM
- Tags
- Audience
- developersai_ml_learnerssaas_founders
What happened
Dharma-AI published a Hugging Face blog post for Dharma Field Extractor, a 2-billion-parameter model that takes a document image plus either an open request or an exact schema and returns structured JSON — invoice line items as ordered lists, ID fields under your own column names, medical requisition fields ready to schedule — with one checkpoint and no per-type fine-tuning. It follows their April DharmaOCR-LITE OCR model, and the post frames it as the opposite tradeoff: OCR gives you the whole page faithfully, the Field Extractor gives you only the fields a downstream system consumes. The post claims it outperforms every frontier LLM they tested at a fraction of the cost and latency, but the excerpt contains no benchmark numbers, no pricing, no license, and only 11 upvotes on the post.
Why it matters
If you currently burn frontier-API calls to turn invoice, ID, or requisition images into typed JSON, a 2B checkpoint is plausibly cheap enough to self-host at batch volume — but this post gives you zero eval numbers, no price, and no license, so the only decision it supports today is 'put it on a test set', not 'migrate'. Build a 50–100 document eval from your own scans with your exact target schema and compare field-level accuracy and cost per document against your current prompt-based pipeline before touching production.
Discussion angle
Ask the room what they pay per document today for schema-constrained extraction and what accuracy bar a self-hosted 2B model would need to clear to replace it — then note that Dharma-AI's 'outperforms every frontier LLM we tested' claim comes with no published numbers, so anyone testing it should share their field-level results back.