@mahbubchula
You can just use the DOI or simply mention that this model is active here.
https://huggingface.co/prithivMLmods/DeepCaption-VLA-7B?doi=true
Prithiv Sakthi
prithivMLmods
AI & ML interests
computer vision, nlp, multimodality
@strangerzonehf @strangerguardhf
Recent Activity
updated
a model
about 6 hours ago
prithivMLmods/DeepCaption-VLA-7B
new activity
about 6 hours ago
prithivMLmods/DeepCaption-VLA-7B:update meta-data
updated
a model
about 6 hours ago
prithivMLmods/Demeter-LongCoT-Qwen3-1.7B
Organizations
replied to
their
post
1 day ago
reacted to
their
post with 🚀👍❤️🤗
2 days ago
Post
4552
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!
🤗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!
posted
an
update
2 days ago
Post
4552
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!
🤗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!
replied to
their
post
5 days ago
Yes, you can!
@mahbubchula
Try the simple workflow I’ve created below:
Here, I implemented https://huggingface.co/prithivMLmods/Behemoth-3B-070225-post0.1, which is close in functionality to DeepCaption-VLA-7B. I switched to this model because it better fits the VRAM usage on a T4 Colab instance. You can adapt the model according to your use cases, requirements, and available resources.
For detection functionality, refer to the following app: https://huggingface.co/spaces/sergiopaniego/vlm_object_understanding
| Demo UI | Image Inference |
|---|---|
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| Demo UI | Image Inference |
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reacted to
their
post with ❤️👀🤗
6 days ago
Post
3374
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
.
.
.
To know more about it, visit the model card of the respective model. !!
✦︎ 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
.
.
.
To know more about it, visit the model card of the respective model. !!
-
- 4 replies
posted
an
update
6 days ago
Post
3374
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
.
.
.
To know more about it, visit the model card of the respective model. !!
✦︎ 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
.
.
.
To know more about it, visit the model card of the respective model. !!
-
-
- 4 replies
reacted to
their
post with 👍❤️🤗
8 days ago
Post
1213
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
➠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
posted
an
update
8 days ago
Post
1213
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
➠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
reacted to
their
post with 🔥❤️🚀🤗
9 days ago
Post
5134
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.
✨ 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.
- 1 reply
posted
an
update
9 days ago
Post
5134
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.
✨ 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.
-
- 1 reply