ViPO: Visual Preference Optimization at Scale
Paper • 2604.24953 • Published • 1
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limingcv
submitted 2
papers to Daily Papers about 6 hours ago
Post
3852
🙋🏻♂️ 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 ! 🚀
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 ! 🚀
- 2 replies
Post
428
Quietly baking Image → Music 🎵 v3 — now running on SOTA open-source models.
👉 fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image → prompt → audio | Early version, will evolve | Follow: @fffiloni
👉 fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image → prompt → audio | Early version, will evolve | Follow: @fffiloni
Post
3446
🙋🏻♂️ 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 🚀
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 🚀
Post
1788
🚀 RB-Modulation is back on Hugging Face Spaces!
This is an older project that recently broke due to dependency changes, but it’s now fixed and running again ✅
👉 What’s fixed:
- GroundingDINO & LangSAM installation
- compatibility with recent environments
- GPU inference running smoothly again
👉 Try it here:
fffiloni/RB-Modulation
Feel free to give it a try again — feedback welcome!
This is an older project that recently broke due to dependency changes, but it’s now fixed and running again ✅
👉 What’s fixed:
- GroundingDINO & LangSAM installation
- compatibility with recent environments
- GPU inference running smoothly again
👉 Try it here:
fffiloni/RB-Modulation
Feel free to give it a try again — feedback welcome!
PeterL1n
authored a
paper 12 days ago
Why doesn't anyone host llms in zerogpu spaces?
8
#172 opened 15 days ago
by 
Reality123b
Why doesn't anyone host llms in zerogpu spaces?
8
#172 opened 15 days ago
by Reality123b
limingcv
authored a
paper 16 days ago
sergiopaniego
posted an update 16 days ago
Post
1258
Earlier this month, Apple introduced Simple Self-Distillation: a fine-tuning method that improves models on coding tasks just by sampling from the model and training on its own outputs with plain cross-entropy
And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎
Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎
How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed
You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py
or benchmark a checkpoint with the eval script:
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py
One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help
Want to dig deeper?
Paper: Embarrassingly Simple Self-Distillation Improves Code Generation (2604.01193)
Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
And… it's already supported in TRL, built by Kashif Rasul. you can really feel the pace of development in the team 🐎
Paper by Ruixiang ZHANG, He Bai, Huangjie Zheng, Navdeep Jaitly, Ronan Collobert, Yizhe Zhang at Apple 🍎
How it works: the model generates completions at a training-time temperature (T_train) with top_k/top_p truncation, then fine-tunes on them with plain cross-entropy. no labels or verifier needed
You can try it right away with this ready-to-run example (Qwen3-4B on rStar-Coder):
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd.py
or benchmark a checkpoint with the eval script:
https://github.com/huggingface/trl/blob/main/trl/experimental/ssd/ssd_eval.py
One neat insight from the paper: T_train and T_eval compose into an effective T_eff = T_train × T_eval, so a broad band of configs works well. even very noisy samples still help
Want to dig deeper?
Paper: Embarrassingly Simple Self-Distillation Improves Code Generation (2604.01193)
Trainer docs: https://huggingface.co/docs/trl/main/en/ssd_trainer
shekkizh
authored a
paper 16 days ago
PeterL1n
authored a
paper 16 days ago
PeterL1n
submitted a
paper to Daily Papers 18 days ago
shekkizh
submitted a
paper to Daily Papers 18 days ago
shekkizh
authored a
paper 18 days ago
Post
3161
✨ PASD Magnify is back on Hugging Face Spaces
fffiloni/PASD
PASD isn’t recent, but still delivers strong results — worth restoring rather than replacing.
Getting it to run again wasn’t a simple dependency issue.
It relied on parts of diffusers that no longer exist, while moving to Gradio 6 forced a much newer HF stack — and I couldn’t modify the original source directly.
Recreating the old environment wasn’t practical.
So I patched the downloaded code at runtime before import and made it compatible with today’s stack.
That ended up being the only approach that held without forking or freezing everything to outdated versions.
If you’ve used it before (or are curious), feel free to give it another try.
fffiloni/PASD
PASD isn’t recent, but still delivers strong results — worth restoring rather than replacing.
Getting it to run again wasn’t a simple dependency issue.
It relied on parts of diffusers that no longer exist, while moving to Gradio 6 forced a much newer HF stack — and I couldn’t modify the original source directly.
Recreating the old environment wasn’t practical.
So I patched the downloaded code at runtime before import and made it compatible with today’s stack.
That ended up being the only approach that held without forking or freezing everything to outdated versions.
If you’ve used it before (or are curious), feel free to give it another try.
sergiopaniego
posted an update 22 days ago
Post
413
Great experience yesterday at PyTorch Conf Europe in Paris 🇫🇷
We (w/ @kashif ) talked about training LLMs through interaction, using trajectories across games, browsers, or simulators
Room was packed, a clear sign of interest in where RL post-training is heading.
sharing the slides! 🤓
https://drive.google.com/file/d/16k7YRnf5EJEo0XjXGlRJ_hVeLoFWKyNP/view?usp=sharing
We (w/ @kashif ) talked about training LLMs through interaction, using trajectories across games, browsers, or simulators
Room was packed, a clear sign of interest in where RL post-training is heading.
sharing the slides! 🤓
https://drive.google.com/file/d/16k7YRnf5EJEo0XjXGlRJ_hVeLoFWKyNP/view?usp=sharing
sergiopaniego
posted an update 29 days ago
Post
2826
Gemma 4 💎 is here and it’s strong!
to celebrate, we’re rolling out in TRL:
> support for multimodal tool responses for environments (OpenEnv)
> an example to train it in CARLA for autonomous driving with image-based tool calls
go check it out 🏎️🏎️
blog: https://huggingface.co/blog/gemma4
script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla_vlm_gemma.py
to celebrate, we’re rolling out in TRL:
> support for multimodal tool responses for environments (OpenEnv)
> an example to train it in CARLA for autonomous driving with image-based tool calls
go check it out 🏎️🏎️
blog: https://huggingface.co/blog/gemma4
script: https://github.com/huggingface/trl/blob/main/examples/scripts/openenv/carla_vlm_gemma.py
Post
2859
✅ Back up and running!
My TIGER app is now fully working again, with fixes and full compatibility with Gradio 6 🚀
It lets you:
- 🎙️ Separate multiple speakers from an audio file
- 🎬 Extract each speaker directly from a video
- 🎧 Split audio into dialog, music, and sound effects (DnR)
- 🎥 Apply DnR separation directly on videos
All powered by lightweight TIGER models for fast and efficient speech separation.
Try it here 👉 fffiloni/TIGER-audio-extraction
My TIGER app is now fully working again, with fixes and full compatibility with Gradio 6 🚀
It lets you:
- 🎙️ Separate multiple speakers from an audio file
- 🎬 Extract each speaker directly from a video
- 🎧 Split audio into dialog, music, and sound effects (DnR)
- 🎥 Apply DnR separation directly on videos
All powered by lightweight TIGER models for fast and efficient speech separation.
Try it here 👉 fffiloni/TIGER-audio-extraction