OWG (ONNXConfig for all)

posted an update 3 days ago

Post

1597

The demo for DREX-062225-exp (Document Retrieval and Extraction eXpert ~ experimental) / typhoon-ocr-3b (a bilingual document parsing model built specifically for real-world documents) / VIREX-062225-exp (Video Information Retrieval and Extraction eXpert ~ experimental) / olmOCR-7B-0225-preview (the document parsing model based on Qwen2VL). 🤗

✦ Demo : prithivMLmods/Doc-VLMs-OCR ~ ( with .md canvas )

⤷ DREX-062225-exp : prithivMLmods/DREX-062225-exp
⤷ typhoon-ocr-3b : scb10x/typhoon-ocr-3b
⤷ VIREX-062225-exp : prithivMLmods/VIREX-062225-exp
⤷ olmOCR-7B-0225-preview : allenai/olmOCR-7B-0225-preview

⤷ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
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To know more about it, visit the model card of the respective model. !!

2 replies

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prithivMLmods

posted an update 4 days ago

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2588

Updated the docscopeOCR-7B-050425-exp with the DREX-062225-exp, with improved preciseness in table structure and line spacing in the markdown used on the document page. And though this is still an experimental one, it's expected to perform well in the defined DREX use cases [ Document Retrieval and Extraction eXpert – experimental ocr ]. 💻

⤷ Model : prithivMLmods/DREX-062225-exp
⤷ Demo : prithivMLmods/Doc-VLMs-OCR

⤷ Collection : prithivMLmods/doc-vl-685839064a863e1cd23be3f1
⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
⤷ Git : https://github.com/PRITHIVSAKTHIUR/DREX.git
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To know more about it, visit the model card of the respective model. !!

prithivMLmods

posted an update 7 days ago

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1755

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. ] 🤗.

✦ Try the demo here : prithivMLmods/Multimodal-OCR2

⤷ MonkeyOCR Recognition : echo840/MonkeyOCR
⤷ Nanonets-OCR-s : nanonets/Nanonets-OCR-s
⤷ SmolDocling-256M-preview : ds4sd/SmolDocling-256M-preview
⤷ typhoon-ocr-7b : scb10x/typhoon-ocr-7b

⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

⤷ Github : https://github.com/PRITHIVSAKTHIUR/Multimodal-OCR2

The community GPU grant was given by Hugging Face — special thanks to them. 🤗🚀

To know more about it, visit the model card of the respective model. !!

2 replies

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louisbrulenaudet

posted an update 7 days ago

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922

🌐 Clinical Trials Dataset now available on Hugging Face! 🧬

I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗

I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.

Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?

louisbrulenaudet/clinical-trials

prithivMLmods

posted an update 10 days ago

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3733

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. !!

prithivMLmods

posted an update 27 days ago

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5671

OpenAI, Google, Hugging Face, and Anthropic have released guides and courses on building agents, prompting techniques, scaling AI use cases, and more. Below are 10+ minimalistic guides and courses that may help you in your progress. 📖

⤷ Agents Companion : https://www.kaggle.com/whitepaper-agent-companion
⤷ Building Effective Agents : https://www.anthropic.com/engineering/building-effective-agents
⤷ Guide to building agents by OpenAI : https://cdn.openai.com/business-guides-and-resources/a-practical-guide-to-building-agents.pdf
⤷ Prompt engineering by Google : https://www.kaggle.com/whitepaper-prompt-engineering
⤷ Google: 601 real-world gen AI use cases : https://cloud.google.com/transform/101-real-world-generative-ai-use-cases-from-industry-leaders
⤷ Prompt engineering by IBM : https://www.ibm.com/think/topics/prompt-engineering-guide
⤷ Prompt Engineering by Anthropic : https://docs.anthropic.com/en/docs/build-with-claude/prompt-engineering/overview
⤷ Scaling AI use cases : https://cdn.openai.com/business-guides-and-resources/identifying-and-scaling-ai-use-cases.pdf
⤷ Prompting Guide 101 : https://services.google.com/fh/files/misc/gemini-for-google-workspace-prompting-guide-101.pdf
⤷ AI in the Enterprise by OpenAI : https://cdn.openai.com/business-guides-and-resources/ai-in-the-enterprise.pdf

by HF🤗 :
⤷ AI Agents Course by Huggingface : https://huggingface.co/learn/agents-course/unit0/introduction
⤷ Smol-agents Docs : https://huggingface.co/docs/smolagents/en/tutorials/building_good_agents
⤷ MCP Course by Huggingface : https://huggingface.co/learn/mcp-course/unit0/introduction
⤷ Other Course (LLM, Computer Vision, Deep RL, Audio, Diffusion, Cookbooks, etc..) : https://huggingface.co/learn

2 replies

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prithivMLmods

posted an update 29 days ago

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2289

Just made a demo for Cosmos-Reason1, a physical AI model that understands physical common sense and generates appropriate embodied decisions in natural language through long chain-of-thought reasoning. Also added video understanding support to it. 🤗🚀

✦ Try the demo here : prithivMLmods/DocScope-R1

⤷ Cosmos-Reason1-7B : nvidia/Cosmos-Reason1-7B
⤷ docscopeOCR-7B-050425-exp : prithivMLmods/docscopeOCR-7B-050425-exp
⤷ Captioner-Relaxed : Ertugrul/Qwen2.5-VL-7B-Captioner-Relaxed

⤷ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

⤷ GitHub :
• https://github.com/PRITHIVSAKTHIUR/Cosmos-x-DocScope
• https://github.com/PRITHIVSAKTHIUR/Nvidia-Cosmos-Reason1-Demo.

To know more about it, visit the model card of the respective model. !!

AtAndDev

posted an update 29 days ago

Post

2784

deepseek-ai/DeepSeek-R1-0528

This is the end

1 reply

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hazemessam

authored 2 papers 30 days ago

Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations

Paper • 2505.20052 • Published May 26

Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction

Paper • 2505.20036 • Published May 26

prithivMLmods

posted an update about 1 month ago

Post

2362

Got access to Google's all-new Gemini Diffusion a state-of-the-art text diffusion model. It delivers the performance of Gemini 2.0 Flash-Lite at 5x the speed, generating over 1000 tokens in a fraction of a second and producing impressive results. Below are some initial outputs generated using the model. ♊🔥

Gemini Diffusion Playground ✦ : https://deepmind.google.com/frontiers/gemini-diffusion

Get Access Here : https://docs.google.com/forms/d/1aLm6J13tAkq4v4qwGR3z35W2qWy7mHiiA0wGEpecooo/viewform?edit_requested=true

🔗 To know more, visit: https://deepmind.google/models/gemini-diffusion/

1 reply

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prithivMLmods

posted an update about 1 month ago

Post

2336

The more optimized explicit content filters with lightweight 𝙜𝙪𝙖𝙧𝙙 models trained based on siglip2 patch16 512 and vit patch16 224 for illustration and explicit content classification for content moderation in social media, forums, and parental controls for safer browsing environments. this version fixes the issues in the previous release, which lacked sufficient resources. 🚀

⤷ Models :
→ siglip2 mini explicit content : prithivMLmods/siglip2-mini-explicit-content [recommended]
→ vit mini explicit content : prithivMLmods/vit-mini-explicit-content

⤷ Building image safety-guard models :

strangerguardhf

⤷ Datasets :
→ nsfw multidomain classification : strangerguardhf/NSFW-MultiDomain-Classification
→ nsfw multidomain classification v2.0 : strangerguardhf/NSFW-MultiDomain-Classification-v2.0

⤷ Collection :
→ Updated Versions [05192025] : prithivMLmods/explicit-content-filters-682aaa4733e378561925ca2b
→ Previous Versions : prithivMLmods/siglip2-content-filters-042025-final-680fe4aa1a9d589bf2c915ff

Find a collections inside the collection.👆

To know more about it, visit the model card of the respective model.

1 reply

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Aurelien-Morgan

posted an update about 1 month ago

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402

Hey, I'll be presenting @retrain-pipelines and almighty function-calling at the Hugging Face Paris HQ, you guys.
Monday evening. Lightning-talk style. With AI Tinkerers.

Come hang !

https://paris.aitinkerers.org/p/ai-tinkerers-paris-ai21-labs-takeover-on-may-19th

https://huggingface.co/blog/Aurelien-Morgan/the-almighty-function-caller

prithivMLmods

posted an update about 1 month ago

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2721

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.

prithivMLmods

posted an update about 1 month ago

Post

2047

Dropping some image classification models for content moderation and classifiers trained with datasets available on the Hub. All are fine-tuned on the siglip2 backbone, (competitions AIOrNot, Imagenette, and Driver-Drowsiness). Models and datasets are listed below:

🤗Models :
AI or Not : prithivMLmods/AIorNot-SigLIP2
Driver Drowsiness Detection : prithivMLmods/DOZE-GUARD-RLDD
Subset 10 ImageNet : prithivMLmods/IMAGENETTE

🥊Datasets :
+ competitions/aiornot
+ akahana/Driver-Drowsiness-Dataset
+ frgfm/imagenette

🔗Collection :
[The previous collection of models is also listed in the same collection, so you can find more models focused on image classification tasks.]

- prithivMLmods/multiclass-image-classification-05142025-68234c8010a9350a4d6739b5

Find a collections inside the collection.🤪👆

To know more about it, visit the model card of the respective model.

lewtun

authored a paper about 2 months ago

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

Paper • 2504.11354 • Published Apr 15 • 6

prithivMLmods

posted an update about 2 months ago

Post

3557

Dropping some image classification models for content moderation, balancers, and classifiers trained on synthetic datasets—along with others based on datasets available on the Hub. Also loaded a few low-rank datasets for realistic gender portrait classification and document-type classifiers, all fine-tuned on the SigLIP-2 Patch-16 224 backbone. Models and datasets are listed below:

🤗Models & Datasets :

Realistic Gender Classification : prithivMLmods/Realistic-Gender-Classification
⎙ prithivMLmods/Realistic-Portrait-Gender-1024px
Document Type Detection : prithivMLmods/Document-Type-Detection
⎙ prithivMLmods/Document-Type-Detection
Face Mask Detection : prithivMLmods/Face-Mask-Detection
⎙ DamarJati/Face-Mask-Detection
Alzheimer Stage Classifier : prithivMLmods/Alzheimer-Stage-Classifier
⎙ SilpaCS/Augmented_alzheimer
Bone Fracture Detection : prithivMLmods/Bone-Fracture-Detection
⎙ Hemg/bone-fracture-detection
GiD Land Cover Classification : prithivMLmods/GiD-Land-Cover-Classification
⎙ jonathan-roberts1/GID

🤗Collection : prithivMLmods/siglip2-05102025-681c2b0e406f0740a993fc1c

To know more about it, visit the model card of the respective model.

Nymbo

posted an update about 2 months ago

Post

2849

Haven't seen this posted anywhere - Llama-3.3-8B-Instruct is available on the new Llama API. Is this a new model or did someone mislabel Llama-3.1-8B?

1 reply

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prithivMLmods

posted an update about 2 months ago

Post

3286

Well, here’s the updated version with the 20,000+ entry sampled dataset for Watermark Filter Content Moderation models incl. [Food25, Weather, Watermark, Marathi/Hindi Sign Language Detection], post-trained from the base models: sigLip2 patch16 224 — now with mixed aspect ratios for better performance and reduced misclassification. 🔥

Models :
➮ Watermark-Detection : prithivMLmods/Watermark-Detection-SigLIP2
⌨︎ Watermark Detection & Batch Image Processing Experimentals, Colab Notebook : https://colab.research.google.com/drive/1mlQrSsSjkGimUt0VyRi3SoWMv8OMyvw3?usp=drive_link
➮ Weather-Image-Classification : prithivMLmods/Weather-Image-Classification
➮ TurkishFoods-25 : prithivMLmods/TurkishFoods-25
➮ Marathi-Sign-Language-Detection : prithivMLmods/Marathi-Sign-Language-Detection
➮ Hindi-Sign-Language-Detection : prithivMLmods/Hindi-Sign-Language-Detection

Datasets :
Watermark : qwertyforce/scenery_watermarks
Weather : prithivMLmods/WeatherNet-05-18039
Turkish Foods 25 : yunusserhat/TurkishFoods-25
Marathi Sign Language : VinayHajare/Marathi-Sign-Language
Hindi Sign Language : Vedant3907/Hindi-Sign-Language-Dataset

Collection : prithivMLmods/content-filters-siglip2-vit-68197e3357d4de18fb3b4d2b

prithivMLmods

posted an update about 2 months ago

Post

1200

The new versions of Midjourney Mix adapters have been dropped in stranger zone hf. These adapters excel in studio lighting portraits and painterly styles, trained using the style of strangerzonehf/Flux-Midjourney-Mix2-LoRA. They leverage 24-bit colored synthetic images generated form midjourney v6 to achieve high-quality image reproducibility and support adaptable aspect ratios, using Flux.1 as the base model. 🥳

Models [ ⌗ ]

> Flux-Midjourney-Painterly-LoRA : strangerzonehf/Flux-Midjourney-Painterly-LoRA
> Flux-Midjourney-Studio-LoRA : strangerzonehf/Flux-Midjourney-Studio-LoRA

> Collection : strangerzonehf/midjourney-mix-3-ft-flux1-dev-68165d58a2a08025852d63f3

> Space : prithivMLmods/FLUX-LoRA-DLC2

The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.

ONNXConfig for all

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Ankh3: Multi-Task Pretraining with Sequence Denoising and Completion Enhances Protein Representations

Beyond Simple Concatenation: Fairly Assessing PLM Architectures for Multi-Chain Protein-Protein Interactions Prediction

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

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