prithivMLmods/docscopeOCR-7B-050425-exp
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The demo for smoldocling / nanonets ocr / typhoon ocr / monkey ocr explores the document OCR capabilities of various newly released multimodal VLMs in a single space. And if you're experiencing or demoing long document image OCR, kindly use the Smoldocling 256M preview [ Smoldocling is back in demo here. ] š¤.
ā¦ Try the demo here : https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR2
ā¤· MonkeyOCR Recognition : https://huggingface.co/echo840/MonkeyOCR/tree/main/Recognition
ā¤· Nanonets-OCR-s : https://huggingface.co/nanonets/Nanonets-OCR-s
ā¤· SmolDocling-256M-preview : https://huggingface.co/ds4sd/SmolDocling-256M-preview
ā¤· typhoon-ocr-7b : https://huggingface.co/scb10x/typhoon-ocr-7b
ā¤· Multimodal Implementations : https://huggingface.co/collections/prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
ā¤· Github : https://github.com/PRITHIVSAKTHIUR/Multimodal-OCR2
The community GPU grant was given by Hugging Face ā special thanks to them. š¤š
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Best dataset for highest DocVQA
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#6 opened 7 months ago
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supercharge19
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you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM š«°š» also with gradient accumulation simulated batch size is 16 āØ
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work š https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
you can fine-tune SmolVLM on an L4 with batch size of 4 and it will only take 16.4 GB VRAM š«°š» also with gradient accumulation simulated batch size is 16 āØ
I made a notebook that includes all the goodies: QLoRA, gradient accumulation, gradient checkpointing with explanations on how they work š https://github.com/huggingface/smollm/blob/main/finetuning/Smol_VLM_FT.ipynb
Any Idea When This Will Be Supported in TGI?
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#3 opened 11 months ago
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