Chinese-Vicuna (Chinese-Vicuna)

posted an update 1 day ago

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Qwen Image – The Latest Image Generation Model🔥

Below are some samples generated using the Qwen Image Diffusion Model. Qwen-Image, a 20B MMDiT model for next-generation text-to-image generation, preserves typographic details, layout coherence, and contextual harmony with stunning accuracy. It is especially strong at creating stunning graphic posters with native text. The model is now open-source. [ 𝚀𝚠𝚎𝚗-𝙸𝚖𝚊𝚐𝚎 : Qwen/Qwen-Image ]

⤷ Try the Qwen Image demo here: prithivMLmods/Qwen-Image-Diffusion, Qwen/Qwen-Image & more ...

⤷ Qwen-Image Technical Report : Qwen-Image Technical Report (2508.02324)
⤷ Qwen Image [GitHub] : https://github.com/QwenLM/Qwen-Image

Even more impressively, it demonstrates a strong ability to understand images. The model supports a wide range of vision-related tasks such as object detection, semantic segmentation, depth and edge (Canny) estimation, novel view synthesis, and image super-resolution. While each task is technically distinct, they can all be viewed as advanced forms of intelligent image editing driven by deep visual understanding. Collectively, these capabilities position Qwen-Image as more than just a tool for generating appealing visuals, it serves as a versatile foundation model for intelligent visual creation and transformation, seamlessly blending language, layout, and imagery.

Qwen-Image uses a dual-stream MMDiT architecture with a frozen Qwen2.5-VL, VAE encoder, RMSNorm for QK-Norm, LayerNorm elsewhere, and a custom MSRoPE scheme for joint image-text positional encoding.

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prithivMLmods

posted an update 4 days ago

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Introducing Camel-Doc-OCR-080125(v2), a document content-structure retrieval VLM designed for content extraction and summarization. This is the second model in the Camel Doc OCR VLM series, following Camel-Doc-OCR-062825(v1). The new version fixes formal table reconstruction issues in both en and zh language, achieving optimal performance for long-context inferences.🤗🐪

⤷ Camel-Doc-OCR(v2) : prithivMLmods/Camel-Doc-OCR-080125
⤷ Camel-Doc-OCR(v1) : prithivMLmods/Camel-Doc-OCR-062825
⤷ Demo : prithivMLmods/core-OCR

Multimodal Model Collections and Spaces:

➝ Camel-Doc-OCR : prithivMLmods/camel-doc-ocr-080125-688c0c61c5dba648756f31f8
➝ Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
➝ Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
➝ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027

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prithivMLmods

posted an update 6 days ago

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1049

Exciting to bring the explicitly grounded experimental reasoning model, Lumian-VLR-7B-Thinking, built on top of Qwen2.5-VL, featuring reasoning-aware trajectories with enhanced spatial perception. Along with this, we’ve also added a demo for the model while bringing some of the latest and most interesting models available on the hub to make full use of the remaining resources.

✨ Multimodal-VLM-Thinking : prithivMLmods/Multimodal-VLM-Thinking
✨ Multimodal-VLM-OCR : prithivMLmods/Multimodal-VLM-OCR

✦ Models used in these spaces:

✨ Lumian-VLR-7B-Thinking : prithivMLmods/Lumian-VLR-7B-Thinking
✨ Enesidaon-VLR-7B-no-Thinking : prithivMLmods/Enesidaon-VLR-7B-no-Thinking
✨ GLM-4.1V-9B-Thinking : zai-org/GLM-4.1V-9B-Thinking
✨ DREX-062225-exp : prithivMLmods/DREX-062225-exp & more ...

✦ Multimodal Model Collections and Spaces:

✨ Vision-Language (VLr) : prithivMLmods/vision-language-for-reasoning-vlr-6889b3f45917352b5e3a6f7a
✨ Multimodal Spaces : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0
✨ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027

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prithivMLmods

posted an update 10 days ago

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Explore OCR, Captioning, and Visual Understanding with Cutting-Edge Models on Hugging Face. 🤗🧪

I’ve put together a collection of Google Colab notebooks to experiment with some of the most exciting models available on the Hugging Face Hub focused on OCR, image captioning, and visual understanding tasks. [Image-to-Text] / [Image-Text-to-Text]

> 📖 OCR-ReportLab-Notebooks : prithivMLmods/OCR-ReportLab-Notebooks

These notebooks are built for quick prototyping and run on free T4 GPUs, making them perfect for experimentation, testing ideas, or just exploring what’s possible with modern vision-language models.

Note: The experimental notebooks are compiled with models that fit within the T4 GPU (free-tier) limits. More models along with their notebooks will be added over time.

prithivMLmods

posted an update 12 days ago

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2361

Excited to introduce the new experimental model "Qwen2.5-VL-7B-Abliterated-Caption-it", which is performing exceptionally well on image captioning tasks. This variant is specifically tailored for Abliterated Captioning and Uncensored Image Captioning. It is designed to generate highly detailed and descriptive captions across a broad range of visual categories including images with complex, sensitive, or nuanced content while handling varying aspect ratios and resolutions.🧪🤗

✨ Try the demo here : prithivMLmods/Qwen2.5-VL
✨ Qwen2.5-VL-7B-Abliterated-Caption-it : prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it
✨ Multimodal VLMs : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
✨ Multimodal Implementations : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

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prithivMLmods

posted an update 13 days ago

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olmOCR [Allen AI] just got an upgrade! 📈🧑‍🍳

The allenai/olmOCR-7B-0725 — fine-tuned with allenai/olmOCR-mix-0225 on top of Qwen/Qwen2.5-VL-7B-Instruct, pushing the boundaries of OCR technology. It takes a single document image as input, with the longest side resized to 1288 pixels. High-quality, openly available approach to parsing pdfs and other complex documents optical character recognition.

Try the demo here: prithivMLmods/Multimodal-OCR

✨ Model: allenai/olmOCR-7B-0725
✨ Model [fp8]: allenai/olmOCR-7B-0725-FP8
✨ Multimodal Implementations Space Collection: prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

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prithivMLmods

posted an update 17 days ago

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Upgraded the step-by-step notebook for fine-tuning SigLIP2 on domain-specific image classification tasks. The notebook supports both datasets with predefined train/test splits and those with only a train split, making it suitable for low-resource, custom, and real-world classification scenarios. 📢👉

➺ FineTuning-SigLIP2-Notebook : prithivMLmods/FineTuning-SigLIP2-Notebook

➺ GitHub : https://github.com/PRITHIVSAKTHIUR/FineTuning-SigLIP-2

➺ In the first, datasets include predefined train and test splits, enabling conventional supervised learning and generalization evaluation : prithivMLmods/FineTuning-SigLIP2-Notebook (.ipynb)

➺ In the second scenario, only a training split is available; in such cases, the training set is either partially reserved for validation or reused entirely for evaluation : prithivMLmods/FineTuning-SigLIP2-Notebook (.ipynb)

This flexibility supports experimentation in constrained or domain-specific settings, where standard test annotations may not exist.

prithivMLmods

posted an update 18 days ago

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Dropping the general-purpose reasoning dataset Poseidon-Reasoning-5M, which supports general thought processes, math, and science — featuring a diverse mixture of domains 🌊 : prithivMLmods/Poseidon-Reasoning-5M

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Poseidon-Reasoning-5M", split="data")

The compact version is as follows — Poseidon-Reasoning-Mini-300K : prithivMLmods/Poseidon-Reasoning-Mini-300K

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Poseidon-Reasoning-Mini-300K", split="train")

Collection : prithivMLmods/poseidon-reasoning-6879ca98e118b307c781a9ba

prithivMLmods

posted an update 22 days ago

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2173

Open Omega Ω (Forge, Atom, Explora):
A Fusion of Math, Science, and Coding 🧪🤗

Datasets :
⌯⌲ Open-Omega-Forge-1M [Mathematics, Coding, and Science]: prithivMLmods/Open-Omega-Forge-1M
⌯⌲ Open-Omega-Atom-1.5M [Mathematics and Science]: prithivMLmods/Open-Omega-Atom-1.5M
⌯⌲ Open-Omega-Explora-2.5M [Forge + Atom]: prithivMLmods/Open-Omega-Explora-2.5M
⌯⌲ Others [Subordinate portion] - Curated and blended modular dataset.

Models :
> Omega-Qwen3-Atom-8B : prithivMLmods/Omega-Qwen3-Atom-8B
> Omega-Qwen2.5-Coder-3B : prithivMLmods/Omega-Qwen2.5-Coder-3B

Dataset Collection: prithivMLmods/open-omega-a-fusion-of-math-science-and-coding-68756c37769fa39c4055cc0e

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For more information, refer to the dataset card(s).

prithivMLmods

posted an update 24 days ago

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Excited to bring the new models that are performing exceptionally well in document OCR, image captioning, and visual understanding tasks. Megalodon-OCR and Perseus-Doc-VL have both demonstrated significant improvements across key areas. You can explore live demos on Hugging Face Spaces to compare their performance with other top-tier models available on the hub. 🤗📄

Models & Spaces :
> Megalodon-OCR (3B) : prithivMLmods/Megalodon-OCR-Sync-0713
> Perseus-Doc-vl (7B): prithivMLmods/Perseus-Doc-vl-0712
> Doc-VLMs-OCR : prithivMLmods/Multimodal-VLM-OCR
> core-OCR : prithivMLmods/core-OCR

Datasets Caption Mix :
> Corvus-OCR-Caption-Mix : prithivMLmods/Corvus-OCR-Caption-Mix
> Corvus-OCR-Caption-Mini-Mix : prithivMLmods/Corvus-OCR-Caption-Mini-Mix

Collections :
> Corvus OCR Caption Mix: prithivMLmods/corvus-ocr-caption-mix-687349bfaceffbd10976f0cc
> Captioning / OCR / DocTable : prithivMLmods/captioning-ocr-doctable-687382e1da822008bb5c06f2

GitHub :
> OCR-ReportLab : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab/blob/main/Megalodon-OCR-Sync-0713-ColabNotebook/Megalodon_OCR_Sync_0713_ReportLab.ipynb

Others Spaces :
> Multimodal-OCR : prithivMLmods/Multimodal-OCR
> Multimodal-VLMs : https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR-Outpost
> Multimodal-OCR2 : prithivMLmods/Multimodal-OCR2
> Florence-2-Image-Caption : prithivMLmods/Florence-2-Image-Caption
> VisionScope-R2 : prithivMLmods/VisionScope-R2
> DocScope-R1 : prithivMLmods/DocScope-R1

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

prithivMLmods

posted an update 28 days ago

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Demo of OCR & Math QA using multi-capable VLMs like MonkeyOCR-pro-1.2B, R1-One-Vision, VisionaryR1, Vision Matters-7B, and VIGAL-7B, all running together with support for both image and video inference. 🪐

✦ Demo Spaces :
⤷ Multimodal VLMs : https://huggingface.co/spaces/prithivMLmods/Multimodal-OCR-Outpost

✦ Models :
⤷ Visionary R1 : maifoundations/Visionary-R1
⤷ MonkeyOCR [1.2B] : echo840/MonkeyOCR-pro-1.2B
⤷ ViGaL 7B : yunfeixie/ViGaL-7B
⤷ Lh41-1042-Magellanic-7B-0711 : prithivMLmods/Lh41-1042-Magellanic-7B-0711
⤷ Vision Matters 7B : Yuting6/Vision-Matters-7B
⤷ WR30a-Deep-7B-0711 : prithivMLmods/WR30a-Deep-7B-0711

✦ MonkeyOCR-pro-1.2B Colab T4 Demo [ notebook ]
⤷ MonkeyOCR-pro-1.2B-ReportLab : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab/blob/main/MonkeyOCR-0709/MonkeyOCR-pro-1.2B-ReportLab.ipynb

✦ GitHub : https://github.com/PRITHIVSAKTHIUR/OCR-ReportLab

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

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Facico

authored a paper about 1 month ago

Selecting and Merging: Towards Adaptable and Scalable Named Entity Recognition with Large Language Models

Paper • 2506.22813 • Published Jun 28 • 7

prithivMLmods

posted an update about 1 month ago

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3565

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-Notebooks

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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prithivMLmods

posted an update about 1 month ago

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The bunch of comparable demos for Multimodal VLMs (excels in OCR, cinematography understanding, spatial reasoning, etc.) now up on the Hub 🤗 — max recent till Jun'25.

✦ Demo Spaces —

> [Nanonets-OCR-s, MonkeyOCR, Typhoon-OCR-7B, SmolDocling] : prithivMLmods/Multimodal-OCR2
> [GLM-4.1v, docscopeOCR-7B, MonkeyOCR, coreOCR-7B] : prithivMLmods/core-OCR
> [Camel-Doc-OCR, ViLaSR-7B, OCRFlux-3B, ShotVL-7B] : prithivMLmods/Multimodal-VLM-OCR
> [SkyCaptioner-V1, SpaceThinker-3B, coreOCR-7B, SpaceOm-3B] : prithivMLmods/VisionScope-R2
> [RolmOCR-7B, Qwen2-VL-OCR-2B, Aya-Vision-8B, Nanonets-OCR-s] : prithivMLmods/Multimodal-OCR
> [DREX-062225-7B, Typhoon-OCR-3B, olmOCR-7B-0225, VIREX-062225-7B] : prithivMLmods/Multimodal-VLM-Thinking
> [Cosmos-Reason1-7B, docscopeOCR-7B, Captioner-7B, visionOCR-3B] : prithivMLmods/DocScope-R1

✦ Space Collection : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

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prithivMLmods

posted an update about 1 month ago

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The demo for Camel-Doc-OCR-062825 (exp) is optimized for document retrieval and direct Markdown (.md) generation from images and PDFs. Additional demos include OCRFlux-3B (document OCR), VilaSR (spatial reasoning with visual drawing), and ShotVL (cinematic language understanding). 🐪

✦ Space : https://huggingface.co/spaces/prithivMLmods/Doc-VLMs-v2-Localization

Models :
⤷ camel-doc-ocr-062825 : prithivMLmods/Camel-Doc-OCR-062825
⤷ ocrflux-3b : ChatDOC/OCRFlux-3B
⤷ vilasr : AntResearchNLP/ViLaSR
⤷ shotvl : Vchitect/ShotVL-7B

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

The community GPU grant was given by Hugging Face — special thanks to them. This space supports the following tasks: (image inference, video inference) with result markdown canvas and object detection/localization. 🤗🚀

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

prithivMLmods

posted an update about 1 month ago

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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 : https://huggingface.co/spaces/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. !!

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prithivMLmods

posted an update about 1 month ago

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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 : https://huggingface.co/spaces/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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prithivMLmods

posted an update about 2 months ago

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1945

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. !!

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prithivMLmods

posted an update about 2 months ago

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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. !!