Multimodal VLMs 5x
🪐
vision matters / ovr / vigal 7b / vlm r1 math / visionary r
Prithiv Sakthi
prithivMLmods
AI & ML interests
computer vision, nlp, multimodality
@strangerzonehf @strangerguardhf
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prithivMLmods/Multimodal-OCR2:why run time error is
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prithivMLmods/Multimodal-VLMs-5x
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fix ~ pipeline tag
#1 opened about 3 hours ago
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prithivMLmods
why run time error is
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#4 opened 11 days ago
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aniket2498
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Variable Demo for Two Image-Text-to-Text Multimodals 🌠
📜Space: prithivMLmods/Multimodal-OCR
By default, it will use:
prithivMLmods/Qwen2-VL-OCR-2B-Instruct or
prithivMLmods/Qwen2-VL-OCR2-2B-Instruct
To trigger Aya-Vision's 8B by @aya-vision , use the prompt:
https://huggingface.co/CohereForAI/aya-vision-8b
📜Space: prithivMLmods/Multimodal-OCR
By default, it will use:
prithivMLmods/Qwen2-VL-OCR-2B-Instruct or
prithivMLmods/Qwen2-VL-OCR2-2B-Instruct
To trigger Aya-Vision's 8B by @aya-vision , use the prompt:
https://huggingface.co/CohereForAI/aya-vision-8b
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Luna, the single-speaker text-to-speech model, features a Radio & Atcosim-style sound with a female voice. It offers authentic radio podcast noise and empathetic speech generation, fine-tuned based on Orpheus's Llama-based speech generation state-of-the-art model. 🎙️
+ Model : prithivMLmods/Llama-3B-Mono-Luna
+ Collection : prithivMLmods/clean-radio-mono-voice-67e76fe1b3a87cc3bccef803
+ Reference ft : https://github.com/canopyai/Orpheus-TTS
+ Base Model : canopylabs/orpheus-3b-0.1-ft
I also tried some other clean-voice single-speaker models based on Orpheus. If you're interested, check out the collection.
🔉Try the Mono Luna demo here: http://colab.research.google.com/drive/1K0AAIOKDE5XE0znxXaiiUJvPSpFveteK
+ Model : prithivMLmods/Llama-3B-Mono-Luna
+ Collection : prithivMLmods/clean-radio-mono-voice-67e76fe1b3a87cc3bccef803
+ Reference ft : https://github.com/canopyai/Orpheus-TTS
+ Base Model : canopylabs/orpheus-3b-0.1-ft
I also tried some other clean-voice single-speaker models based on Orpheus. If you're interested, check out the collection.
🔉Try the Mono Luna demo here: http://colab.research.google.com/drive/1K0AAIOKDE5XE0znxXaiiUJvPSpFveteK
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Models for detecting images generated by diffusion models (Flux.1, SDXL, ..) are trained or fine-tuned using image classification models for content moderation. These models use datasets available on the Hub. For identifying AI-generated images or moderating visual content, the recommended model is OpenSDI-Flux.1-SigLIP2.😺🧨
Models : prithivMLmods/OpenSDI-Flux.1-SigLIP2 [Best approach for AI [Diffusion Generated] vs. real image classification] prithivMLmods/OpenSDI-SD2.1-SigLIP2 prithivMLmods/OpenSDI-SD3-SigLIP2 prithivMLmods/OpenSDI-SD1.5-SigLIP2 prithivMLmods/OpenSDI-SDXL-SigLIP2
Datasets : nebula/OpenSDI_test madebyollin/megalith-10m
Collection : prithivMLmods/opensdi-diffusion-generated-image-classification-682488a3a3e5be7083db3383
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
Models : prithivMLmods/OpenSDI-Flux.1-SigLIP2 [Best approach for AI [Diffusion Generated] vs. real image classification] prithivMLmods/OpenSDI-SD2.1-SigLIP2 prithivMLmods/OpenSDI-SD3-SigLIP2 prithivMLmods/OpenSDI-SD1.5-SigLIP2 prithivMLmods/OpenSDI-SDXL-SigLIP2
Datasets : nebula/OpenSDI_test madebyollin/megalith-10m
Collection : prithivMLmods/opensdi-diffusion-generated-image-classification-682488a3a3e5be7083db3383
Find a collections inside the collection.👆
To know more about it, visit the model card of the respective model.
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The demo for the MonkeyOCR Recognition model, which adopts a Structure-Recognition-Relation (SRR) triplet paradigm & Nanonets-OCR-s a powerful, state-of-the-art image-to-markdown OCR model that goes far beyond traditional text extraction and other experimental document OCR models, is combined into a single space.
✦ Try the demo here : prithivMLmods/core-OCR
✦ Try Nanonets-OCR-s demo here : prithivMLmods/Multimodal-OCR
⤷ MonkeyOCR Recognition : echo840/MonkeyOCR
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ coreOCR-7B-050325-preview : prithivMLmods/coreOCR-7B-050325-preview
⤷ Nanonets-OCR-s : nanonets/Nanonets-OCR-s
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
Also, include a sample OCR test using the VisionOCR-3B-061125 model and the Qwen2-VL-OCR-2B-Instruct model.
⤷ Blog : https://huggingface.co/blog/prithivMLmods/visionocr-3b-061125-vs-qwen2-vl-ocr-2b-instruct
To know more about it, visit the model card of the respective model. !!
✦ Try the demo here : prithivMLmods/core-OCR
✦ Try Nanonets-OCR-s demo here : prithivMLmods/Multimodal-OCR
⤷ MonkeyOCR Recognition : echo840/MonkeyOCR
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ coreOCR-7B-050325-preview : prithivMLmods/coreOCR-7B-050325-preview
⤷ Nanonets-OCR-s : nanonets/Nanonets-OCR-s
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
Also, include a sample OCR test using the VisionOCR-3B-061125 model and the Qwen2-VL-OCR-2B-Instruct model.
⤷ Blog : https://huggingface.co/blog/prithivMLmods/visionocr-3b-061125-vs-qwen2-vl-ocr-2b-instruct
To know more about it, visit the model card of the respective model. !!
updated
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1 day ago
published
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model
1 day ago
upvoted
3
papers
2 days ago
Energy-Based Transformers are Scalable Learners and Thinkers
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WebSailor: Navigating Super-human Reasoning for Web Agent
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HalluSegBench: Counterfactual Visual Reasoning for Segmentation Hallucination Evaluation
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Multimodal OCR with ReportLab? On Colab T4? (Nanonets OCR, Monkey OCR, OCRFlux 3B, Typhoo OCR 3B?) .. Yeah, it’s possible. I’ve made a dedicated Colab notebook to experiment with these models (all built on top of Qwen2.5 VL). 🤗🚀
Download notebooks here :
✦︎ NanonetsOCR : https://colab.research.google.com/drive/1VvA-amvSVxGdWgIsh4_by6KWOtEs_Iqp
✦︎ MonkeyOCR : https://colab.research.google.com/drive/1vPCojbmlXjDFUt06FJ1tjgnj_zWK4mUo
✦︎ OCRFluxOCR : https://colab.research.google.com/drive/1TDoCXzWdF2hxVLbISqW6DjXAzOyI7pzf
✦︎ TyphoonOCR : https://colab.research.google.com/drive/1_59zvLNnn1kvbiSFxzA1WiqhpbW8RKbz
🜲 Github : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab
What does it do?
1. Performs OCR on the input image
2. Generates a DOCX or PDF file with the input image and the extracted text
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To know more about it, visit the model card of the respective model. !!
Download notebooks here :
✦︎ NanonetsOCR : https://colab.research.google.com/drive/1VvA-amvSVxGdWgIsh4_by6KWOtEs_Iqp
✦︎ MonkeyOCR : https://colab.research.google.com/drive/1vPCojbmlXjDFUt06FJ1tjgnj_zWK4mUo
✦︎ OCRFluxOCR : https://colab.research.google.com/drive/1TDoCXzWdF2hxVLbISqW6DjXAzOyI7pzf
✦︎ TyphoonOCR : https://colab.research.google.com/drive/1_59zvLNnn1kvbiSFxzA1WiqhpbW8RKbz
🜲 Github : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab
What does it do?
1. Performs OCR on the input image
2. Generates a DOCX or PDF file with the input image and the extracted text
.
.
.
To know more about it, visit the model card of the respective model. !!
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