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DeepNewz, mobile.
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ColPali is revolutionizing multimodal retrieval, with potential for further enhancement through domain-specific fine-tuning. A recent blog post by vanstriendaniel explores creating a fine-tuning dataset using Alibaba_Qwen's Qwen2-VL-7B-Instruct model. The approach is demonstrated through a Gradio Space that allows users to generate queries from an input document page image. This method aims to improve the effectiveness of Retrieval-Augmented Generation (RAG) systems.
Yesterday, I shared a blog post on generating data for fine-tuning ColPali using @Alibaba_Qwen's Qwen2-VL-7B-Instruct. To simplify testing this approach, I created a @gradio Space that lets you generate queries from an input document page image. https://t.co/UJwmVhyqUb https://t.co/wg73fGlo6U
Awesome blog post on how to use LLMs to generate a quality dataset from your own documents to fine-tune ColPali for your RAG use case 😍 https://t.co/H1KOi5oBRz
Ever wondered if a RAG (Retrieval-Augmented Generation) chatbot can actually enhance your documentation? Spoiler alert: It absolutely can! In our latest blog with @mlopscommunity, @vishakha041 shares how our experiment with a RAG-based Q&A chatbot for ApertureDB revealed gaps in… https://t.co/z1IAl8hJSa
