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building image safety-guard models

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prithivMLmods  updated a dataset 18 days ago
strangerguardhf/Caption3o-Opt-Abliterated-5K
prithivMLmods  published a dataset 18 days ago
strangerguardhf/Caption3o-Opt-Abliterated-5K
prithivMLmods  updated a dataset 18 days ago
strangerguardhf/Caption3o-Opt-Abliterated-5K
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prithivMLmods 
posted an update about 4 hours ago
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Build something cool with Nano Banana AIO [All-in-One] — draw to transform on canvas, image editing, and image generation in one place! 🍌🔥

✦︎ nano-banana-aio: https://nano-banana-aio-op72ohwdda-uw.a.run.app/

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⚠️Note: The app's health status is currently healthy, but this may change at any time. If you are facing network problems, kindly refresh the current app tab.
prithivMLmods 
posted an update 3 days ago
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Dropped the HeadshotX : a super-realistic headshot adapter for Qwen/Qwen-Image, an image generation model by Qwen. It is an advanced LoRA adaptation of the Qwen-Image model and an upgraded version of prithivMLmods/Qwen-Image-Studio-Realism, offering more precise portrait rendering with a strong focus on realism. The model was trained on diverse face types from across the world, labeled with florence2-en and caption-optimized using prithivMLmods/DeepCaption-VLA-7B. 11(types) × 5 different face types: Asian, Hispanic, Caucasian, Latina, Middle Eastern, etc.

⮞ Model🤗: prithivMLmods/Qwen-Image-HeadshotX

⮞ The Previous Adapter (LoRA): prithivMLmods/Qwen-Image-Studio-Realism

⮞ Collection: prithivMLmods/qwen-image-exp-lora-68a978fe11400bc3165b0c4d

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To know more about it, visit the app page or the respective model page!!
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prithivMLmods 
posted an update 4 days ago
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Comparing: DeepCaption-VLA-7B, built on Qwen2.5-VL-7B-Instruct, is tailored for image captioning and vision-language attribution, focusing on precise, descriptive captions of visual properties, object attributes, and scene details. In contrast, Qwen2.5-VL-7B-Abliterated-Caption-it is fine-tuned for abliterated captioning, generating highly detailed descriptions across diverse visual categories.

Models🤗
✦ DeepCaption-VLA-7B : prithivMLmods/DeepCaption-VLA-7B
✦ Qwen2.5-VL-7B-Abliterated-Caption-it : prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it

Spaces⛵
➜ VisionScope-R2 : prithivMLmods/VisionScope-R2
➜ Qwen2.5-VL-Outpost : prithivMLmods/Qwen2.5-VL-Outpost

Collection🗞️
DeepCaption attr. : prithivMLmods/deepcaption-attr-68b041172ebcb867e45c556a
VL Abliterated-Caption : prithivMLmods/vl-abliterated-caption-68a0443b63182e97a15c47a3
Multimodal VLMs - Until July'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027
Multimodal VLMs - Aug'25 : prithivMLmods/multimodal-vlms-until-july25-688312e6b840e1e156f13027

GitHub↗️
> DeepCaption-VLA-7B [4bit-notebook demo] : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/DeepCaption-VLA-7B%5B4bit%20-%20notebook%20demo%5D/DeepCaption-VLA-7B.ipynb
> Qwen2.5-VL-3B-Abliterated-Caption-it(caption) : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Qwen2.5-VL-3B-Abliterated-Caption-it(caption)/Qwen2_5_VL_3B_Abliterated_Caption_it.ipynb

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

To know more about it, visit the app page or the respective model page!!
prithivMLmods 
posted an update 7 days ago
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FastVLMs by Apple are the talk of the week for edge device VLMs and also for consumer-grade VLMs on the Hub. They have some impressive demos available on the Hub for live captioning and inference tasks. Meanwhile, I’m still exploring one of the coolest edge-device multimodal releases—Liquid AI’s LFM2-VL (450M and 1.6B). I’ve also made a live camera video inference demo, which is capable of running on Colab’s free-tier T4 GPU.

🤗Live Captioning Notebooks:
➠ LiquidAI LFM2 VL 1.6B Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LiquidAI-LFM2-VL-Live-Cam/LiquidAI_LFM2_VL_1_6B_Live_Cam.ipynb

➠ LiquidAI LFM2 VL 450M Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LiquidAI-LFM2-VL-Live-Cam/LiquidAI_LFM2_VL_450M_Live_Cam.ipynb

✨I also made a demo for the FastVLM Live Captioning Notebook.
➠ FastVLM 0.5B Live Cam: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/Apple-FastVLM-0.5B-Live-Cam/apple_FastVLM_0_5B_live_cam.ipynb

↗️For more notebooks, kindly visit the following repositories.
➠ Multimodal Outpost Notebooks: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks

Feel free to fork, modify, and explore!
prithivMLmods 
posted an update 12 days ago
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Introducing prithivMLmods/DeepCaption-VLA-7B, a multimodal VLM designed for reasoning with long-shot captions (Captioning and Vision-Language Attribution). It focuses on defining visual properties, object attributes, and scene details across a wide spectrum of images and aspect ratios, generating attribute-rich image captions. The model supports creative, artistic, and technical applications that require detailed descriptions. 🤗🔥

✦︎ Models: prithivMLmods/DeepCaption-VLA-7B, also includes prithivMLmods/DeepAttriCap-VLA-3B, an experimental model for vision-language attribution.

✦︎ Try the demo here: prithivMLmods/VisionScope-R2

✦︎ Try it now on Google Colab, with support for T4 GPUs in 4-bit quant_type: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/DeepCaption-VLA-7B%5B4bit%20-%20notebook%20demo%5D/DeepCaption-VLA-7B.ipynb

✦︎ Collection: prithivMLmods/deepcaption-attr-68b041172ebcb867e45c556a

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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 13 days ago
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OpenGVLab's InternVL3.5 is a new family of open-source multimodal models that have advanced versatility, reasoning, and efficiency. I have created 𝐝𝐞𝐦𝐨 𝐧𝐨𝐭𝐞𝐛𝐨𝐨𝐤𝐬 for models ranging from 1B to 4B parameters, available in multiple versions (MPO, Instruct, Pre-trained) and in both "thinking" and "non-thinking" settings, with experimental compatibility for 𝐓𝐞𝐬𝐥𝐚 𝐓𝟒 GPUs.

➠InternVL3_5_2B_MPO_Thinking: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3.5-Thinking/1_InternVL3_5_2B_MPO_Thinking/1_InternVL3_5_2B_MPO_Thinking.ipynb
➠InternVL3_5_1B_Instruct_Thinking: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3.5-Thinking/2_InternVL3_5_1B_Instruct_Thinking/2_InternVL3_5_1B_Instruct_Thinking.ipynb

➠InternVL3_5-1B-MPO: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3_5-MPO/InternVL3_5-1B-MPO/InternVL3_5_1B_MPO.ipynb
➠InternVL3_5-2B-MPO: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/tree/main/InternVL-3.5-Notebook/InternVL3_5-MPO/InternVL3_5-2B-MPO

➠InternVL3_5-1B-Instruct: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3_5-Instruct/InternVL3_5-1B-Instruct/InternVL3_5_1B_Instruct.ipynb
➠InternVL3_5-2B-Instruct: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3_5-Instruct/InternVL3_5-2B-Instruct/InternVL3_5_2B_Instruct.ipynb

➠InternVL3_5-1B-Pretrained: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3_5-Pretrained/InternVL3_5-1B-Pretrained/InternVL3_5_1B_Pretrained.ipynb
➠InternVL3_5-2B-Pretrained: https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/InternVL-3.5-Notebook/InternVL3_5-Pretrained/InternVL3_5-2B-Pretrained/InternVL3_5_2B_Pretrained.ipynb

no flash_attention
prithivMLmods 
posted an update 14 days ago
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OpenGVLab's InternVL3_5-2B-MPO [Mixed Preference Optimization (MPO)] is a compact vision-language model in the InternVL3.5 series. You can now experience it in the Tiny VLMs Lab, an app featuring 15+ multimodal VLMs ranging from 250M to 4B parameters. These models support tasks such as OCR, reasoning, single-shot answering with small models, and captioning (including ablated variants), across a broad range of visual categories. They are also capable of handling images with complex, sensitive, or nuanced content, while adapting to varying aspect ratios and resolutions.

✨ Space/App : prithivMLmods/Tiny-VLMs-Lab
🫙 Model : OpenGVLab/InternVL3_5-2B-MPO
↗️ Collection: OpenGVLab/internvl35-68ac87bd52ebe953485927fb
🗞️ Paper : https://arxiv.org/pdf/2508.18265
↗️ Multimodal Space Collection : prithivMLmods/multimodal-implementations-67c9982ea04b39f0608badb0

To learn more, visit the relevant spaces, collections, and model cards.
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prithivMLmods 
posted an update 16 days ago
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Dropping new adapters for Qwen-Image, including Qwen-Image-Studio-Realism, Qwen-Image-Anime-LoRA, Qwen-Image-Sketch-Smudge, Qwen-Image-Synthetic-Face, and Qwen-Image-Fragmented-Portraiture, with various style intermix compatibilities. For more details, visit the model card.

⤷ Studio Realism : prithivMLmods/Qwen-Image-Studio-Realism
⤷ Image Anime LoRA : prithivMLmods/Qwen-Image-Anime-LoRA
⤷ Sketch Smudge : prithivMLmods/Qwen-Image-Sketch-Smudge
⤷ Synthetic Face : prithivMLmods/Qwen-Image-Synthetic-Face
⤷ Fragmented Portraiture : prithivMLmods/Qwen-Image-Fragmented-Portraiture

Try it here at
✦︎ Qwen-Image-LoRA-DLC : prithivMLmods/Qwen-Image-LoRA-DLC
✦︎ Qwen-Image-Diffusion : prithivMLmods/Qwen-Image-Diffusion

Collection
✦︎ Qwen-Image-Exp-LoRA : prithivMLmods/qwen-image-exp-lora-68a978fe11400bc3165b0c4d
✦︎ Image Gen Apps (Diffusion) - LastUpdated 08/18 : prithivMLmods/image-gen-apps-diffusion-lastupdated-08-18-68a2f4c5ef3e5e394eacc20a

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To know more, visit the following spaces, collections, and model cards.
prithivMLmods 
updated a dataset 18 days ago
prithivMLmods 
published a dataset 18 days ago
prithivMLmods 
posted an update 23 days ago
prithivMLmods 
posted an update 24 days ago
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Excited to introduce the Tiny VLMs Lab App for experiencing 15+ multimodal VLMs, ranging from a 250M parameter model to a 4B parameter model, for tasks like OCR, reasoning, small models for single-shot answering, and captioning (abliterated), across a broad range of visual categories including images with complex, sensitive, or nuanced content, while handling varying aspect ratios and resolutions.🧪

🤗 Space/App: prithivMLmods/Tiny-VLMs-Lab

✦︎ Also introducing prithivMLmods/Qwen2.5-VL-3B-Abliterated-Caption-it, tailored for Abliterated Captioning / Uncensored Image Captioning. This release comes as a lighter alternative to the existing Qwen2.5-VL-7B-Abliterated-Caption-it prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it model, making it usable on mid-range GPUs and even experimental on T4 GPUs.

✦︎ Collection: prithivMLmods/vl-abliterated-caption-68a0443b63182e97a15c47a3
✦︎ GitHub: https://github.com/PRITHIVSAKTHIUR/Tiny-VLMs-Lab
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To know more about it, visit the app page or the respective model page!!
prithivMLmods 
posted an update 28 days ago
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Try Liquid AI's all-new multimodal models: LFM2-VL-1.6B & LFM2-VL-450M! Demo with the Gradio UI and ReportLab support and both models are runnable on T4 GPU!

↗ LFM2-VL-1.6B-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-1.6B-LiquidAI/LFM2-VL-1.6B_ReportLab.ipynb

↗ LFM2-VL-450M-LiquidAI : https://github.com/PRITHIVSAKTHIUR/Multimodal-Outpost-Notebooks/blob/main/LFM2-VL-450M-LiquidAI/LFM2-VL-450M_ReportLab.ipynb

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To know more about it, visit the multimodal outpost notebooks !!
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prithivMLmods 
posted an update about 1 month ago
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On the verge of releasing Poseidon-Reasoning-5M, a dataset built to excel in general thought processes, mathematics, and science across a diverse mixture of domains, I’m also dropping the Gargantua-R1-Compact dataset, a collection of over six million high-quality reasoning QA pair traces. 🤗🚀

✦ Gargantua-R1-Compact : prithivMLmods/Gargantua-R1-Compact

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Compact", split="train")

Additionally, I’m adding the mini version of Gargantua — the Gargantua-R1-Wee : prithivMLmods/Gargantua-R1-Wee

from datasets import load_dataset

dataset = load_dataset("prithivMLmods/Gargantua-R1-Wee", split="train")

The composition spans 73.93% core mathematical reasoning involving problems, proofs, and computational challenges, 12.11% across diverse scientific domains such as physics, chemistry, biology, and interdisciplinary topics, 11.35% in competitive coding covering algorithms and data structures, 1.37% in academic science focusing on research-level methodology, 0.95% in creative and analytical reasoning through logic puzzles and problem-solving tasks, 0.25% in specialized technical areas like MLOps, LLMs, diffusion models, and CUDA, and 0.06% involving data from graphs and charts converted into structured JSON formats. Designed with both rich contextual depth and formal structural clarity, Gargantua-R1-Compact is an optimal resource for advancing research in symbolic reasoning, interpretability, and high-precision question answering in mathematical domains.

✦ Collection : prithivMLmods/gargantua-r1-mod-6896bfd7834e82b89ad2b38b


To know more about it, visit the dataset card of the respective dataset. !!
prithivMLmods 
posted an update about 1 month ago
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I've added the demo of the openbmb/MiniCPM-V-4 model to the Hugging Face Space:
prithivMLmods/Multimodal-VLM-Thinking

✨ MiniCPM-V 4.0 is the latest efficient model in the MiniCPM-V series. The model is built based on SigLIP2-400M and MiniCPM4-3B, with a total of 4.1B parameters. It inherits the strong single-image, multi-image, and video understanding performance of MiniCPM-V 2.6 with largely improved efficiency.

✨ With only 4.1B parameters, MiniCPM-V 4.0 achieves an average score of 69.0 on OpenCompass, a comprehensive evaluation of 8 popular benchmarks. This performance surpasses GPT-4.1-mini-20250414, MiniCPM-V 2.6 (8.1B parameters, OpenCompass 65.2), and Qwen2.5-VL-3B-Instruct (3.8B parameters, OpenCompass 64.5). It also shows good performance in multi-image and video understanding.

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. !!
prithivMLmods 
posted an update about 1 month 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-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. !!
prithivMLmods 
posted an update about 1 month 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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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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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-v1.0

✦ 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 about 1 month 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 about 2 months ago
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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 : https://huggingface.co/spaces/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. !!