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LLM recommendations sought for document intelligence on bank statements

By PulseAugur Editorial · [1 sources] ·

A user on the r/LocalLLaMA subreddit is seeking recommendations for a language model suitable for document intelligence tasks, specifically for processing bank statements. They plan to use OCR to convert PDFs to Markdown and then feed these into a text model for categorizing expenses. The user is asking for advice on which models, such as Qwen, Gemma, or Llama-family models, would be most accurate for this purpose, considering constraints on total model size (under 80B) or active parameters (under 6B for MoE architectures). AI

RANK_REASON This is a user query on a specific subreddit asking for model recommendations, not a news event or a significant industry development.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM recommendations sought for document intelligence on bank statements

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Meme
This is a user query on a specific subreddit asking for model recommendations, not a news event or a significant industry development.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/StupidScaredSquirrel ·

    Best sub 80b total or 6b active MoE for document intelligence?

    <!-- SC_OFF --><div class="md"><p>I'd like to make a pipeline capable of understanding pdfs of bank statements and then group expenses by categories etc. My thought was to first parse them with ocr into .md files and then feed that to a text model. I think it's more lightweight a…

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