deepseek-ai/DeepSeek-V3.1
Text Generation
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AI & ML interests
computer vision, nlp , machine learning and deeplearning
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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: https://huggingface.co/spaces/prithivMLmods/Tiny-VLMs-Lab
✦︎ Also introducing https://huggingface.co/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 https://huggingface.co/prithivMLmods/Qwen2.5-VL-7B-Abliterated-Caption-it model, making it usable on mid-range GPUs and even experimental on T4 GPUs.
✦︎ Collection: https://huggingface.co/collections/prithivMLmods/vl-abliterated-caption-68a0443b63182e97a15c47a3
✦︎ GitHub: https://github.com/PRITHIVSAKTHIUR/Tiny-VLMs-Lab
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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!!
🤗 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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Image-to-Prompt⚡
ovi054/image-to-prompt
Extract text prompt from image. And you can reuse the prompt to generate similar images!
Useful for prompt engineering, studying image-to-text alignment, making training datasets, or recreating similar outputs.
Powered by: Gradio, Florence 2
👉 Try it now: ovi054/image-to-prompt
ovi054/image-to-prompt
Extract text prompt from image. And you can reuse the prompt to generate similar images!
Useful for prompt engineering, studying image-to-text alignment, making training datasets, or recreating similar outputs.
Powered by: Gradio, Florence 2
👉 Try it now: ovi054/image-to-prompt
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After so many requests, I want to update everyone on the status of Ark, i.e., doing robotics in Python. First, thanks a lot for the amazing and impressive interest we got in it. We are now @ 1.13 K downloads, which is beyond my wildest expectations (https://pepy.tech/projects/ark-robotics?timeRange=threeMonths&category=version&includeCIDownloads=true&granularity=daily&viewType=line&versions=0.1.1%2C0.1%2C0.0.1)!
0. First and foremost, we are pip installable (https://pypi.org/project/ark-robotics/)
1. We are currently working on supporting more robots: G1 and Drones are in the works with a cool set of amazing, fantastic colleagues. Those are coming.
2. We have support for multiple sensors and interfaces (https://github.com/Robotics-Ark/ark_interfaces)
3. We also now have support for machine learning via diffusion policies (https://github.com/Robotics-Ark/ark_diffusion_policies_on_franka)
4. We have a set of tutorials that detail each step (https://arkrobotics.notion.site/ARK-Home-22be053d9c6f8096bcdbefd6276aba61)
You can read the paper here: https://robotics-ark.github.io/ark_robotics.github.io/static/images/2506.21628v2.pdf
Have fun building robotics with Python people! Please star our repo (https://github.com/Robotics-Ark/ark_framework), so we can continue our open-sourcing endeavour!
0. First and foremost, we are pip installable (https://pypi.org/project/ark-robotics/)
1. We are currently working on supporting more robots: G1 and Drones are in the works with a cool set of amazing, fantastic colleagues. Those are coming.
2. We have support for multiple sensors and interfaces (https://github.com/Robotics-Ark/ark_interfaces)
3. We also now have support for machine learning via diffusion policies (https://github.com/Robotics-Ark/ark_diffusion_policies_on_franka)
4. We have a set of tutorials that detail each step (https://arkrobotics.notion.site/ARK-Home-22be053d9c6f8096bcdbefd6276aba61)
You can read the paper here: https://robotics-ark.github.io/ark_robotics.github.io/static/images/2506.21628v2.pdf
Have fun building robotics with Python people! Please star our repo (https://github.com/Robotics-Ark/ark_framework), so we can continue our open-sourcing endeavour!
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𝗣𝗮𝗽𝗲𝗿𝟮𝗣𝗥𝘀
Lately, we've been experimenting with recommending arXiv papers based on the context of what we're building in AI.
At the same time, we're using an agent to help automate the building and testing of Docker Images.
Check out the example here:
https://hub.docker.com/repository/docker/remyxai/2507.20613v1/general
Next, we're tasking our #ExperimentOps agent to open PRs in a target repo, to evaluate the core concepts from a new research paper in the context of your application and your kpis.
Operationalize your Experimentation!
Find Your Frontier!
#BeAnExperimenter
Lately, we've been experimenting with recommending arXiv papers based on the context of what we're building in AI.
At the same time, we're using an agent to help automate the building and testing of Docker Images.
Check out the example here:
https://hub.docker.com/repository/docker/remyxai/2507.20613v1/general
Next, we're tasking our #ExperimentOps agent to open PRs in a target repo, to evaluate the core concepts from a new research paper in the context of your application and your kpis.
Operationalize your Experimentation!
Find Your Frontier!
#BeAnExperimenter
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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: https://huggingface.co/spaces/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. !!
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: https://huggingface.co/spaces/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. !!
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🫡 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
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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. !!
⤷ 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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Qwen just released Qwen3-30B-A3B-Instruct-2507 🔥 an upgrade to the non-thinking mode model
Qwen/Qwen3-30B-A3B-Instruct-2507
✨ 30B MoE / 3.3B active - Apache 2.0
✨ Strong gains in reasoning, math, coding, & multilingual tasks
✨ Native support for 256K long-context inputs
Qwen/Qwen3-30B-A3B-Instruct-2507
✨ 30B MoE / 3.3B active - Apache 2.0
✨ Strong gains in reasoning, math, coding, & multilingual tasks
✨ Native support for 256K long-context inputs
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