Post
95
Well, it took just 2 hours for
openai/gpt-oss-120b to hit #1 on Hugging Face. Don’t remember seeing anything rise that fast!
AI & ML interests
Tools for creating and exploring datasets
alielfilali01
posted
an
update
about 11 hours ago
Post
118
Guys WTH is "yofo-*" ???
Most OpenAI staff associated with the openai/gpt-oss-68911959590a1634ba11c7a4 release are affiliated to dozens of yofo orgs ...
i.e
yofo-wildflower
Some HF folks as well 👀
Most OpenAI staff associated with the openai/gpt-oss-68911959590a1634ba11c7a4 release are affiliated to dozens of yofo orgs ...
i.e
yofo-wildflower
Some HF folks as well 👀
prithivMLmods
posted
an
update
about 22 hours ago
Post
1740
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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To know more about it, visit the model card of the respective 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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To know more about it, visit the model card of the respective model. !!
Post
3017
🫡 I am the first and only one to like the French Tax Code Dataset
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
that's it , that's the post
find the dataset here : louisbrulenaudet/code-impots
follow : @louisbrulenaudet
- 2 replies
prithivMLmods
posted
an
update
4 days ago
Post
3095
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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To know more about it, visit the model card of the respective model. !!
⤷ 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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To know more about it, visit the model card of the respective model. !!
- 2 replies
prithivMLmods
posted
an
update
6 days ago
Post
1048
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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To know more about it, visit the model card of the respective model. !!
✨ 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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To know more about it, visit the model card of the respective model. !!
prithivMLmods
posted
an
update
9 days ago
Post
4801
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.
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
Post
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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To know more about it, visit the model card of the respective model. !!
✨ 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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To know more about it, visit the model card of the respective model. !!
prithivMLmods
posted
an
update
13 days ago
Post
2364
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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To know more about it, visit the model card of the respective model. !!
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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To know more about it, visit the model card of the respective model. !!
Post
684
👋 Hey there folks,
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
just submitted my plugin idea to the G-Assist Plugin Hackathon by @nvidia . Check it out, it's a great way to use a local SLA model on a windows machine to easily and locally get things done ! https://github.com/NVIDIA/G-Assist
prithivMLmods
posted
an
update
17 days ago
Post
5093
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.
➺ 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.
Post
532
🙋🏻♂️ Hey there folks ,
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
Yesterday , Nvidia released a reasoning model that beats o3 on science, math and coding !
Today you can try it out here : Tonic/Nvidia-OpenReasoning
hope you like it !
prithivMLmods
posted
an
update
18 days ago
Post
4071
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
The compact version is as follows — Poseidon-Reasoning-Mini-300K : prithivMLmods/Poseidon-Reasoning-Mini-300K
Collection : prithivMLmods/poseidon-reasoning-6879ca98e118b307c781a9ba
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
Post
2134
AudioRAG is becoming real! Just built a demo with ColQwen-Omni that does semantic search on raw audio, no transcription needed.
Drop in a podcast, ask your question, and it finds the exact chunks where it happens. You can also get a written answer.
What’s exciting: it skips transcription, making it faster and better at capturing emotion, ambient sound, and tone, surfacing results text search would miss.
- Demo: fdaudens/colqwen-omni-demo
- Blog post from ColQwen team: https://huggingface.co/blog/manu/colqwen-omni-omnimodal-retrieval
Drop in a podcast, ask your question, and it finds the exact chunks where it happens. You can also get a written answer.
What’s exciting: it skips transcription, making it faster and better at capturing emotion, ambient sound, and tone, surfacing results text search would miss.
- Demo: fdaudens/colqwen-omni-demo
- Blog post from ColQwen team: https://huggingface.co/blog/manu/colqwen-omni-omnimodal-retrieval
- 1 reply
Post
2514
You might not have heard of Moonshot AI — but within 24 hours, their new model Kimi K2 shot to the top of Hugging Face’s trending leaderboard.
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
So… who are they, and why does it matter?
Had a lot of fun co-writing this blog post with @xianbao , with key insights translated from Chinese, to unpack how this startup built a model that outperforms GPT-4.1, Claude Opus, and DeepSeek V3 on several major benchmarks.
🧵 A few standout facts:
1. From zero to $3.3B in 18 months:
Founded in March 2023, Moonshot is now backed by Alibaba, Tencent, Meituan, and HongShan.
2. A CEO who thinks from the end:
Yang Zhilin (31) previously worked at Meta AI, Google Brain, and Carnegie Mellon. His vision? Nothing less than AGI — still a rare ambition among Chinese AI labs.
3. A trillion-parameter model that’s surprisingly efficient:
Kimi K2 uses a mixture-of-experts architecture (32B active params per inference) and dominates on coding/math benchmarks.
4. The secret weapon: Muon optimizer:
A new training method that doubles efficiency, cuts memory in half, and ran 15.5T tokens with zero failures. Big implications.
Most importantly, their move from closed to open source signals a broader shift in China’s AI scene — following Baidu’s pivot. But as Yang puts it: “Users are the only real leaderboard.”
👇 Check out the full post to explore what Kimi K2 can do, how to try it, and why it matters for the future of open-source LLMs:
https://huggingface.co/blog/fdaudens/moonshot-ai-kimi-k2-explained
prithivMLmods
posted
an
update
22 days ago
Post
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).
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).
Post
284
AI is reshaping everything—how we work, how we feel, even how nations compete.
Today’s reads cut across power, emotion, and disruption.
Here’s what stood out and why it matters 👇
AI might “solve” loneliness, but this could be a problem, as the discomfort of loneliness shapes us in important ways. 💔 https://t.co/k2Q9le6G0P
A new study warns of significant risks in using AI therapy chatbots, highlighting issues like stigmatization and inappropriate responses. 🤖 https://t.co/EFyW0RbYVl
AI is already showing signs of slashing job openings in the UK, particularly in roles exposed to the technology, suggesting a labor market slowdown. 📉 https://t.co/hhs0BbqIMa
AI firms like OpenAI are poaching Wall Street quants with massive paydays, shifting the talent landscape for building artificial general intelligence. 💰 https://www.businessinsider.com/ai-talent-openai-wall-street-quant-trading-firms-2025-7
Speaking of which: Nvidia CEO Jensen Huang disagrees with Anthropic CEO Dario Amodei on whether AI will create more jobs—or trigger a “white-collar apocalypse.” Huang believes AI will create vastly more, and better, jobs. ⚔️ https://t.co/YHWhY7qvSq
Can Nvidia convince governments to pay for “sovereign AI”? Politicians are warming to the idea of national AI systems, but it might not reduce dependence on US tech. 🌍 https://t.co/htQDzJAIDu
Today’s reads cut across power, emotion, and disruption.
Here’s what stood out and why it matters 👇
AI might “solve” loneliness, but this could be a problem, as the discomfort of loneliness shapes us in important ways. 💔 https://t.co/k2Q9le6G0P
A new study warns of significant risks in using AI therapy chatbots, highlighting issues like stigmatization and inappropriate responses. 🤖 https://t.co/EFyW0RbYVl
AI is already showing signs of slashing job openings in the UK, particularly in roles exposed to the technology, suggesting a labor market slowdown. 📉 https://t.co/hhs0BbqIMa
AI firms like OpenAI are poaching Wall Street quants with massive paydays, shifting the talent landscape for building artificial general intelligence. 💰 https://www.businessinsider.com/ai-talent-openai-wall-street-quant-trading-firms-2025-7
Speaking of which: Nvidia CEO Jensen Huang disagrees with Anthropic CEO Dario Amodei on whether AI will create more jobs—or trigger a “white-collar apocalypse.” Huang believes AI will create vastly more, and better, jobs. ⚔️ https://t.co/YHWhY7qvSq
Can Nvidia convince governments to pay for “sovereign AI”? Politicians are warming to the idea of national AI systems, but it might not reduce dependence on US tech. 🌍 https://t.co/htQDzJAIDu
prithivMLmods
posted
an
update
24 days ago
Post
3840
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. !!
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. !!
Post
3270
🙋🏻♂️ Normalize adding compute & runtime traces to your model cards
- 2 replies
prithivMLmods
posted
an
update
27 days ago
Post
2390
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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To know more about it, visit the model card of the respective model. !!
✦ 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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To know more about it, visit the model card of the respective model. !!