Post
4113
who's working on an NVFP4 version of Kimi-K3?
- 4 replies
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Post
5579
Introducing Inflect-v2, two exceptionally small, open-weight English TTS models at just 3.9M and 9.3M parameters. Both generate speech multiple times faster than real-time on CPU. Despite their size, Inflect-v2 delivers quality that is competitive with much larger lightweight TTS systems, including KittenTTS, Piper, and Supertonic-3.
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
CPU, CUDA, PyTorch, and ONNX are supported. Apache 2.0.
See it for yourselves:
owensong/Inflect-Micro-v2
owensong/Inflect-Nano-v2
Try the Demos:
Nymbo/Inflect-TTS (unlimited CPU usage)
owensong/Inflect-v2 (ultra-fast ZeroGPU usage)
- 6 replies
DmitryRyuminΒ
authored 2
papers 21 days ago
osansevieroΒ
authored a
paper about 1 month ago
osansevieroΒ
submitted a
paper to Daily Papers about 1 month ago
Post
2482
You can explore and try my Agentic Space Factory:
fffiloni/agentic-space-factory-UI-public
Be kind π€
fffiloni/agentic-space-factory-UI-public
Be kind π€
Post
1873
I made a Hugging Face Space for SCAIL-2 π€
Reference character + driving motion β animated result.
A simple demo to explore the paperβs core workflow with curated examples.
π fffiloni/SCAIL-2
Reference character + driving motion β animated result.
A simple demo to explore the paperβs core workflow with curated examples.
π fffiloni/SCAIL-2
- 1 reply
Post
836
β±οΈ Built a small Space for Visual Chronometer / Pulse of Motion.
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect βchronometric hallucinationβ in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
Upload a video and estimate its Physical FPS: the frame rate implied by visual motion, independent of metadata.
Useful to inspect βchronometric hallucinationβ in generated videos: clips that look smooth, but move with the wrong physical time scale.
Try it here: fffiloni/Pulse-of-Motion
- 1 reply
Post
1601
A few weeks ago, @victor opened the door: coding agents can now ship Hugging Face Spaces autonomously.
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didnβt just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
Itβs what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. π€
I pulled on that thread.
As someone who builds and ships Gradio demos regularly, I didnβt just want to reproduce the loop. I wanted to see what happens when that loop is plugged into the whole Hugging Face stack.
The interesting part is not only that an agent can ship a Space.
Itβs what happens when Space generation becomes a first-class Hugging Face workflow.
That became Agentic Space Factory.
More soon. π€
- 1 reply
eienmojikiΒ
posted an update about 2 months ago
Post
247
Hi everyone,
I've created a Gradio space for embedding and extracting invisible watermarks in images:
π eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
I've created a Gradio space for embedding and extracting invisible watermarks in images:
π eienmojiki/blind-watermark-studio
It supports hiding text, images, and bit arrays using the DWT-DCT-SVD algorithm.
Credits:
- Original library: https://github.com/guofei9987/blind_watermark
- Author: Guo Fei
:).
Post
4674
We trained an open-source Mythos like cybersecurity LLM for the Build Small Hackathon meet OpenMythos
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
π€ Demo β build-small-hackathon/OpenMythos
π§ Model β build-small-hackathon/OpenMythos
π¦ CVE Dataset β build-small-hackathon/CVE_Vulnerailities_Detailed
π ArXiv Dataset β himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks π
Trained in two stages: SFT on ~1.84K filtered ArXiv cs.CR papers + real CVE data, then RLVR using paired with past vulnerabilities GitHub repos with a verifier model checking outputs against ground truth.
Trained on: H100s from Modal
The RLVR stage made the biggest difference responses got more precise and less prone to confusing similar vulnerability classes.
Everything is open:
π€ Demo β build-small-hackathon/OpenMythos
π§ Model β build-small-hackathon/OpenMythos
π¦ CVE Dataset β build-small-hackathon/CVE_Vulnerailities_Detailed
π ArXiv Dataset β himanshu17HF/ArvixImport-Filtered-Final
Try it out and let us know where it breaks π
- 2 replies
alielfilali01Β
posted an update 2 months ago
Post
695
Plans in HTML > Plans in Markdown
Post
3419
Sharing how I built the LongCat-Video-Avatar 1.5 Space (+500k views on X) in one agent session. Gave a coding agent its own AI lab on ZeroGPU, framed the goal, walked away. It designed, deployed, tested against the live API, fixed, shipped.
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
Full recipe with the copy-paste prompt: https://huggingface.co/blog/victor/building-zerogpu-spaces-autonomously
- 1 reply
Post
3854
I built HF Radio on Hugging Face Spaces π»
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
fffiloni/HF-Radio
A live community radio for AI-generated songs, powered by tracks created with ACE-Step.
You can tune in, discover community-made songs in many languages, vote on what sounds good, and mark your real favorites as Bangers.
The more people listen, vote, and create, the better the station gets.
Under the hood, it connects a few Hugging Face pieces together:
Spaces for the live app, HF buckets for community tracks, OAuth for signed-in listeners, server-side streaming with ffmpeg, hourly playlist refreshes, moderation, jingles, and community feedback loops.
Itβs not just a playlist.
Itβs a shared taste experiment:
new songs get a shot every hour, and the community helps decide what deserves another spin.
Come listen.
Find weird gems.
Support the Bangers.
Shape the radio.
β> fffiloni/HF-Radio
Post
3191
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
Post
515
Great technical guide by Nico Martin on the Hugging Face blog, showing how to use Transformers.js inside a Chrome extension and run ONNX models from the Hub locally with WebGPU inside a Manifest V3 extension.
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page β with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
The interesting part: this is not just a chatbot in a side panel.
The article walks through the architecture behind a browser agent that can read open tabs, query webpages, search history, and highlight elements directly on the page β with models downloaded from the Hugging Face Hub, cached under the extension origin, and executed locally instead of being called through a remote API for every prompt.
A strong blueprint for building local-first web copilots, reading assistants, and AI-powered browsing workflows.
Article: https://huggingface.co/blog/transformersjs-chrome-extension
Post
355
Iβve been reading βWhat if AI systems werenβt chatbots?β
What if AI systems weren't chatbots? (2605.07896) π
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more βhumanβ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask β answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially β image, video, editing, animation β Iβm not sure βchatβ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction β not just better assistants, but better tools. π€
What if AI systems weren't chatbots? (2605.07896) π
The paper asks a simple but important question: what if the chatbot interface is not just a neutral wrapper around AI models, but part of the problem?
A chatbot can make a system feel more capable, more certain, and more βhumanβ than it really is. That matters, because interfaces shape how we trust, use, and delegate to AI systems.
When everything becomes: ask β answer
we can lose sight of the actual workflow:
- parameters
- alternatives
- uncertainty
- intermediate steps
- failure modes
- human control
For creative AI especially β image, video, editing, animation β Iβm not sure βchatβ should always be the default interface.
Sometimes we need a conversation.
But often we need a canvas, a timeline, sliders, masks, previews, comparisons, and visible pipelines.
This is also why I find many open ML demos interesting: Spaces, Gradio apps, visual tools, small focused interfaces.
They often explore another direction β not just better assistants, but better tools. π€
- 2 replies
Post
4410
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
702
Quietly baking Image β Music π΅ v3 β now running on SOTA open-source models.
π fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image β prompt β audio | Early version, will evolve | Follow: @fffiloni
π fffiloni/image-2-music-v3 | Feel free to test it and share feedback.
Just wiring together: merve/moondream3 * victor/ace-step-jam
Image β prompt β audio | Early version, will evolve | Follow: @fffiloni