Dharma-AI releases a 2-billion-parameter document field extraction model
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Dharma-AI releases the 2-billion-parameter Dharma Field Extractor document field extraction model. The model takes document images and an optional schema as input and outputs structured JSON; according to Dharma-AI, the same checkpoint can be used without fine-tuning per document type. On deployment cost, Dharma-AI states that the figures in the article are not sales prices, and that self-hosting costs depend on hardware, throughput, and the latency required for each deployment.
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- Hugging Face BlogFeaturedDharma Field Extractor: Any Document In, Structured Fields in JSON Out
Dharma-AI introduces the Dharma Field Extractor, a 2-billion-parameter model built on DharmaOCR-LITE v1.1 with a LoRA adapter trained on 43,384 examples, which takes a document image plus either an open request or an exact schema and returns typed JSON, including null for fields not present. On a 546-document, 1,638-case benchmark it scored 0.834 field-level F1, above every frontier configuration tested (Claude Opus 5 at 0.816 to GPT-5.6 Luna at 0.741), while running self-hosted on a single L40S at a cost the company reports as 6 to 147 times lower than the APIs compared. The company frames the result as evidence that a small model trained close to a bounded task can beat a general frontier model on that task.
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