everyone is sleeping on this new JSON extraction model!
- open-weights
- matches Gemini Flash 3.5
- while being 3x faster
pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON.
lift, from @datalabto, does all of you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema.
the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers.
here's what you get with lift:
- 90.2% field accuracy on 11,000 fields
- 9B model, nearly matches Gemini Flash 3.5 at 3x the speed
- built not to hallucinate; returns null if missing
- self-hostable with vLLM or Hugging Face
GitHub repo: github.com/datalab-to/lift
(don't forget to star 🌟)
- open-weights
- matches Gemini Flash 3.5
- while being 3x faster
pulling structured data out of documents usually takes a chain of OCR, an LLM, and extra code to repair broken JSON.
lift, from @datalabto, does all of you give lift a PDF or image along with a JSON schema, and get back a JSON object that matches that schema.
the model reads every page of a document in one pass, so values that span multiple pages still come out right. any document you can describe in a schema works, from invoices to research papers.
here's what you get with lift:
- 90.2% field accuracy on 11,000 fields
- 9B model, nearly matches Gemini Flash 3.5 at 3x the speed
- built not to hallucinate; returns null if missing
- self-hostable with vLLM or Hugging Face
GitHub repo: github.com/datalab-to/lift
(don't forget to star 🌟)
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@akshay_pachaar @datalabto OCR + LLM + JSON repair chain is exactly the pipeline I keep rebuilding. What's the eval here, actual table/field extraction accuracy? 'matches Gemini Flash' on what set, with what schema complexity?
Oct 4, 2026 · 3:18 PM UTC
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