CroCo: Cross-Lingual Contrastive Preference Tuning on Self-Generations
Paper β’ 2605.26293 β’ Published β’ 1
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jjzhaΒ
submitted a
paper to Daily Papers about 12 hours ago
mridul3301Β
authored 2
papers 2 days ago
prithivMLmodsΒ
posted an update 6 days ago
Post
5364
I've made 8 Spaces in the Qwen-Image-Edit series, and out of them, 5 Spaces reached βSpace of the Weekβ! A few Spaces are still topping the list even after many months.
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! π€β€οΈ
π Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
Cumulatively, the series has crossed 8.2 million+ ZeroGPU runs and nearly 4 million visitors overall.
Thanks for all the community support! π€β€οΈ
π Spaces: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
- 4 replies
Post
2703
ππ»ββοΈ Hey there folks ,
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
Turns out : if we predict π earth we can save a lot of time looking for interesting things and less time looking at things that we expect to see.
Sentinel-2 imagery π°οΈbasically takes a long time to download towards earth. so our "near real time" systems are quite far from that in practical terms.
meanwhile , if we "predict" what we will see , based on what we do see , we can send down much less data in a timely way , and prioritize π‘earth-bound response .
I'm talking about illegal fishing , logging , mining or building in nature reserves , the more of that we predict early the more we're able to stop it on time.
At least that's the concept !
check out the blog : https://huggingface.co/blog/Tonic/save-patagonia-by-predicting-earth
- Collection: https://huggingface.co/collections/NuTonic/earth-observation-with-temporal-and-general-understanding
- Code: https://github.com/Josephrp/Nutonic
- Dataset: NuTonic/sat-vl-sft-training-ready-v1
- Model: NuTonic/lspace
- Training: NuTonic/lspace-trackio
- Evals: NuTonic/Patagonia_Eval
- 2 replies
Cartinoe5930Β
authored 2
papers 15 days ago
Cartinoe5930Β
authored a
paper 16 days ago
prithivMLmodsΒ
posted an update 26 days ago
Post
5904
Multimodal-Edge Demo, a node-based inference canvas demo, is now live on Spaces. It features node-based Transformers for fast inference across 10+ edge-device multimodal models on the Hub, all within a single space. The series includes models from Qwen3.5, Qwen3-VL, Gemma 4, and the LFM 2.5 VL model series, with support for reasoning and grounding tasks.
π€ Demo: prithivMLmods/Multimodal-Edge-Node
π GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
β Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
π€ > To learn more, visit the app page or the respective model pages.
π€ Demo: prithivMLmods/Multimodal-Edge-Node
π GitHub: https://github.com/PRITHIVSAKTHIUR/Multimodal-Edge-Node
β Multimodal Apps Collections: https://huggingface.co/collections/prithivMLmods/hall-of-multimodal-apps
π€ > To learn more, visit the app page or the respective model pages.
Post
4256
ππ»ββοΈ Hey there folks,
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
since everyone liked my previous announcement post ( https://huggingface.co/posts/Tonic/338509028435394 ) so much , i'm back with more high quality proceedural datasets in the Geospacial domain for SFT training !
Check this one out :
NuTonic/sat-bbox-metadata-sft-v1
the goal is to be able to train vision models on multiple images for remote sensing analysis with one shot .
hope you like it ! π
- 2 replies
Post
3615
ππ»ββοΈ Hey there folks ,
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
I'm sharing huggingface's largest dataset of annotated statelite images today.
check it out here : NuTonic/sat-image-boundingbox-sft-full
I hope you like it , the idea is to be able to use this with small vision models π
prithivMLmodsΒ
posted an update about 1 month ago
Post
1903
Now, a collection of various compression schemes for Qwen3.6 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. π
π Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
π Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions
π€ > To learn more, visit the app page or the respective model pages.
π Qwen3.6-MoE: https://huggingface.co/collections/prithivMLmods/qwen36-35b-a3b-compressions
π Qwen3.6-27B Compressions: https://huggingface.co/collections/prithivMLmods/qwen36-27b-compressions
π€ > To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update about 1 month ago
Post
4204
HY-World-2.0 β A Multi-Modal World Model for Reconstructing, Generating, and Simulating 3D Worlds is now available on Spaces, and it works both as native Gradio components and in Gradio server mode.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
π€To learn more, visit the app page or the respective model pages.
> HY-World-2.0-Demo: prithivMLmods/HY-World-2.0-Demo
> HY-World-2.0 [Server Mode]: prithivMLmods/HY-World-2.0-Demo
> Featuring 3D reconstruction and Gaussian splats with the Rerun viewer, along with camera poses, depth maps, and surface normals.
> In Server Mode, Gradio is served via FastAPI, with FastAPI remaining the top-level server.
> Model: tencent/HY-World-2.0
> GitHub: https://github.com/PRITHIVSAKTHIUR/HY-World-2.0-Demo
π€To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update about 1 month ago
Post
6225
A new comparator on Spaces showcases Standard FLUX.2 Decoder vs. FLUX.2 Small Decoder. The Small Decoder is ~1.4Γ faster, uses ~1.4Γ less VRAM, and maintains near-identical image quality. It has ~28M parameters with narrower channels [96, 192, 384, 384] vs. [128, 256, 512, 512], and the demo supports sequence generation by running both decoders simultaneously and comparing the results side by side.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
π€ Comparator: https://huggingface.co/spaces/prithivMLmods/Flux.2-4B-Decoder-Comparator
π FLUX.2-small-decoder: black-forest-labs/FLUX.2-small-decoder
π GitHub: https://github.com/PRITHIVSAKTHIUR/Flux.2-4B-Encoder-Comparator
π Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π€ > App built on the Gradio SDK. To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update about 2 months ago
Post
4246
Now, a collection of various compression schemes for Gemma 4 and the abliterated version 1 of dense models is available on the Hub. Check it out via the links below. π
πGemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
πGemma 4 Uncensored [MAX] + Compression(s) - [`Ξ² ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
πGemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
πGemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf
π€ > To learn more, visit the app page or the respective model pages.
πGemma 4 Compression(s)- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions
πGemma 4 Uncensored [MAX] + Compression(s) - [`Ξ² ]- https://huggingface.co/collections/prithivMLmods/gemma-4-uncensored-max-compressions
πGemma 4 Compression(s) - MoE- https://huggingface.co/collections/prithivMLmods/gemma-4-compressions-moe
πGemma-4 F32 GGUF- https://huggingface.co/collections/prithivMLmods/gemma-4-f32-gguf
π€ > To learn more, visit the app page or the respective model pages.
prithivMLmodsΒ
posted an update about 2 months ago
Post
2344
Now the demo for image detection based on SAM3 and Gemma-4 (*Filter) is available on Spaces, using full-fledged Transformers inference with multimodal reasoning for processed images. It also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.
π€ Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
π₯½ SAM3: facebook/sam3
π gemma-4-E2B-it: google/gemma-4-E2B-it
To learn more, visit the app page or the respective model pages.
π€ Demo Space: prithivMLmods/SAM3-Gemma4-CUDA
π₯½ SAM3: facebook/sam3
π gemma-4-E2B-it: google/gemma-4-E2B-it
To learn more, visit the app page or the respective model pages.
- 1 reply
prithivMLmodsΒ
posted an update about 2 months ago
Post
4778
The demo for Image Detection (*Filter) based on SAM3 and Qwen-3.5 is now available on Hugging Face Spaces using Transformers inference, with multimodal reasoning for processed images, and it also supports video segmentation (mask), video segmentation (annotation), and image click segmentation.
π€ Demo Space: prithivMLmods/SAM3-Plus-Qwen3.5
π₯½ SAM3: facebook/sam3
π Qwen-3.5: Qwen/Qwen3.5-2B
To learn more, visit the app page or the respective model pages.
π€ Demo Space: prithivMLmods/SAM3-Plus-Qwen3.5
π₯½ SAM3: facebook/sam3
π Qwen-3.5: Qwen/Qwen3.5-2B
To learn more, visit the app page or the respective model pages.
- 5 replies
kenza-ilyΒ
authored a
paper about 2 months ago
prithivMLmodsΒ
posted an update 2 months ago
Post
5326
Flux-Klein-KV-Edit-Consistency demo is now available on Spaces. It preserves character identity and delivers high-quality, realistic results after edits. No need for any special prompts, just upload the image, type your prompt, and get the resulting image blazing fast.
π₯ Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
π€ Model: black-forest-labs/FLUX.2-klein-9b-kv
π€ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
β Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
β Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.
π₯ Demo Space: prithivMLmods/flux-klein-kv-edit-consistency
π€ Model: black-forest-labs/FLUX.2-klein-9b-kv
π€ Collection: https://huggingface.co/collections/prithivMLmods/image-generation-apps-collection
π Gradio Server Mode: https://www.gradio.app/main/guides/server-mode
β Built with Headless Gradio, an alternative to using gr.Blocks for creating the frontend and triggering events, powered by FastAPI + Gradio. You can now design the frontend however you want, with continued support for APIs, MCP, and ZeroGPU.
β Gradio Server Mode is now available from gradio@v6.10.0.
To learn more, visit the app page or the respective model pages.