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madstuntman11
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Reacted to merve's post with ❀️ about 1 month ago
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If you have documents that do not only have text and you're doing retrieval or RAG (using OCR and LLMs), give it up and give ColPali and vision language models a try πŸ€—

Why? Documents consist of multiple modalities: layout, table, text, chart, images. Document processing pipelines often consist of multiple models and they're immensely brittle and slow. πŸ₯²

How? ColPali is a ColBERT-like document retrieval model built on PaliGemma, it operates over image patches directly, and indexing takes far less time with more accuracy. You can use it for retrieval, and if you want to do retrieval augmented generation, find the closest document, and do not process it, give it directly to a VLM like Qwen2-VL (as image input) and give your text query. 🀝

This is much faster + you do not lose out on any information + much easier to maintain too! πŸ₯³

Multimodal RAG merve/multimodal-rag-66d97602e781122aae0a5139 πŸ’¬
Document AI (made it way before, for folks who want structured input/output and can fine-tune a model) merve/awesome-document-ai-65ef1cdc2e97ef9cc85c898e πŸ“–
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Reacted to merve's post with ❀️ about 1 month ago
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I have put together a notebook on Multimodal RAG, where we do not process the documents with hefty pipelines but natively use:
- vidore/colpali for retrieval πŸ“– it doesn't need indexing with image-text pairs but just images!
- Qwen/Qwen2-VL-2B-Instruct for generation πŸ’¬ directly feed images as is to a vision language model with no processing to text!
I used ColPali implementation of the new 🐭 Byaldi library by @bclavie πŸ€—
https://github.com/answerdotai/byaldi
Link to notebook: https://github.com/merveenoyan/smol-vision/blob/main/ColPali_%2B_Qwen2_VL.ipynb
upvoted an article about 1 month ago
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upvoted an article 2 months ago
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πŸ€— PEFT welcomes new merging methods

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upvoted an article 4 months ago
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ColPali: Efficient Document Retrieval with Vision Language Models πŸ‘€

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Reacted to smangrul's post with ❀️ 7 months ago
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πŸš€ Exciting news from πŸ€— PEFT!

We are introducing new merging methods for LoRA adapters. These methods allow for retaining the unique capabilities of individual LoRAs while enabling them to combine their strengths: https://huggingface.co/blog/peft_merging

We explored the application of merging LoRA adapters in the context of personal code copilot before πŸš€πŸ‘Ύβœ¨. Please go through the below thread on it: https://x.com/sourab_m/status/1718008115726283004?s=20

New merging methods ties, dare, and magnitude_prune introduced alongside existing methods cat, linear, and svd. Blogpost details each method. These methods can be applied on-the-fly during inference time instead of merging offline enabling great developer UX. ✨

How do I merge my LoRA adapters?
Easy, use class method add_weighted_adapter(). For example, below you can see how we can combine three LoRA adapters using ties method. We can observe that merged adapter can retain the capabilities of individual adapters!

Now that we have seen they can retain individual LoRAs, how about use cases wherein we require the capabilities from multiple LoRAs being merged/combined? Below is an application of it in text-to-image domain. πŸ–ΌοΈ

Kudos to @prateeky2806 (TIES author) and Le Yu (DARE author) for their kind and generous guidance on the PRs! Also, if you want to explore full model merging, refer to super cool projects like https://github.com/arcee-ai/mergekit/tree/main, https://github.com/Gryphe/BlockMerge_Gradient and https://github.com/yule-BUAA/MergeLM/tree/main.

Excited to see what the community creates on top of this! πŸš€βœ¨ #LetsBuildTogether