FLUX.1 [Schnell]
🏎
5.09k
Generate images from text prompts with FLUX.1 schnell
Doonari OKuno
dooOkuno
AI & ML interests
None yet
Recent Activity
liked a Space 44 minutes ago
black-forest-labs/FLUX.1-schnell liked a model 10 days ago
empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF upvoted a changelog 13 days ago
Filter Models page by HardwareOrganizations
None yet
liked a
Space 44 minutes ago
liked a
model 10 days ago
upvoted a changelog 13 days ago
Hugging Face Changelog
Filter Models page by Hardware
• 205
liked a
model 27 days ago
reacted to dronefreak's post with 🔥 about 1 month ago
Post
3243
Excited to open-source the VisDrone Aerial Object Detection Model Zoo on Hugging Face.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome.
The collection includes multiple YOLO variants trained and evaluated on the VisDrone benchmark for aerial object detection, with accompanying documentation and performance metrics.
If you're working on drones, aerial surveillance, robotics, or small-object detection, I hope these models save you some time.
Model Zoo: https://huggingface.co/collections/dronefreak/visdrone-detection-model-zoo
Feedback, issues, and contributions are welcome.
- 13 replies
liked a
model about 1 month ago
reacted to Jaward's post with 🔥 about 1 month ago
Post
9182
Our preprint is out!
We attempt to model human teaching behaviors into agents yielding a unified framework that enables adaptive personalized learning experiences:
LectūraAgents addresses the prevailing limitations in current AI learning systems with three essential capabilities:
(1) a hierarchical multi-agent architecture modeled on academic standards. we observe that agents collaborating across hierarchies yield better personalized learning outcomes.
(2) an adaptive embodied teaching mechanism, in which the instructor agent executes visible and pedagogically motivated teaching actions (e.g. handwrite, highlight, circle etc) on contents in a teaching environment while speaking.
(3) to achieve this we propose a novel teaching action-speech alignment algorithm (TASA) that dynamically aligns speech with visual teaching actions: specifically, TASA temporally chops up speech segments into word-level tokens, performs salience heuristics analysis on learning contents (texts, images etc) then identifies relevant regions to apply pedagogical teaching actions that guide attention and augment understanding.
We conducted several experiments to assess these capabilities: starting with pedagogical evaluation of the various components under frontier models, comparative analysis with existing frameworks and an efficacy study with real students.
Results show consistent gains in standard instructional metrics (curated by expert educators) spanning lecture content quality, embodied teaching quality, assessment, and personalization over baseline systems, positioning LectūraAgents as a pedagogically grounded framework for personalized learning at scale.
Paper: LectūraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching (2606.16428)
Data: Jaward/lectura-agents-data
We attempt to model human teaching behaviors into agents yielding a unified framework that enables adaptive personalized learning experiences:
LectūraAgents addresses the prevailing limitations in current AI learning systems with three essential capabilities:
(1) a hierarchical multi-agent architecture modeled on academic standards. we observe that agents collaborating across hierarchies yield better personalized learning outcomes.
(2) an adaptive embodied teaching mechanism, in which the instructor agent executes visible and pedagogically motivated teaching actions (e.g. handwrite, highlight, circle etc) on contents in a teaching environment while speaking.
(3) to achieve this we propose a novel teaching action-speech alignment algorithm (TASA) that dynamically aligns speech with visual teaching actions: specifically, TASA temporally chops up speech segments into word-level tokens, performs salience heuristics analysis on learning contents (texts, images etc) then identifies relevant regions to apply pedagogical teaching actions that guide attention and augment understanding.
We conducted several experiments to assess these capabilities: starting with pedagogical evaluation of the various components under frontier models, comparative analysis with existing frameworks and an efficacy study with real students.
Results show consistent gains in standard instructional metrics (curated by expert educators) spanning lecture content quality, embodied teaching quality, assessment, and personalization over baseline systems, positioning LectūraAgents as a pedagogically grounded framework for personalized learning at scale.
Paper: LectūraAgents: A Multi-Agent Framework for Adaptive Personalized AI-Assisted Learning and Embodied Teaching (2606.16428)
Data: Jaward/lectura-agents-data
liked a
model about 2 months ago
reacted to RiverRider's post with 🔥 about 2 months ago
Post
2823
This is not the end of words. It is the end of pretending their meanings are determined.
Meaning Forks. SRT detects it.
Paste any text to identify contested terms
RiverRider/srt-introspect
Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer
RiverRider/srt-introspect
Repository
https://github.com/space-bacon/SRT
Paper
https://github.com/space-bacon/SRT/blob/main/paper_nla.md
Explainer
https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md
Meaning Forks. SRT detects it.
Paste any text to identify contested terms
RiverRider/srt-introspect
Try any prompt (attached link) to see exactly what an LLM is thinking at every meaningful step of its answer
RiverRider/srt-introspect
Repository
https://github.com/space-bacon/SRT
Paper
https://github.com/space-bacon/SRT/blob/main/paper_nla.md
Explainer
https://github.com/space-bacon/SRT/blob/main/docs/EXPLAINERS.md
liked a
dataset about 2 months ago
reacted to lbourdois's post with 🤗 about 2 months ago
Post
1102
New blog post!
An introduction to a little-known but highly effective model reduction method: 𝗧𝗿𝗶𝗺𝗺𝗶𝗻𝗴✂️
We show how to reduce model size (we went up to 87.24% reduction) while preserving its performance.
We applied this technique to 16 different model families across several modalities to illustrate that it works on any architecture (as long as the embedding layer is the last one of the model) and on any modality involving text.
From these 16 families, we generated over 𝟱,𝟱𝟬𝟬 𝗺𝗼𝗻𝗼𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝟭𝟮𝟰 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 🌍
Key takeaways from our experiments:
1️⃣ Trimming does not require a GPU. Our models were obtained on a CPU.
2️⃣ This method scales up to at least 4B parameters (we did not test beyond that).
3️⃣ Trimmed model is smaller than the original while preserving its performance. If you observe a slight performance drop, just fine-tuned to recover or even surpass the original performance.
4️⃣ For an equivalent compute budget, it is better to trim then fine-tune rather than fine-tuning the original model. Since the model is smaller, you can run more epochs/show more data and get in fine a better model than the original.
5️⃣ Trimming is a competitive alternative to distillation and quantization. E.g. we obtained our alternative to DistilBERT in 9 minutes on CPU vs. 90 hours of GPU for the latter.
6️⃣ Trimming could generate reasoning traces in the language of the trimmed model. This could be an alternative to generating traces in English and then translating them into the desired language.
And many other things (such as how much data are needed, the impact of the database used, the order in which it should be done, etc.) are available in the blogpost!
Blogpost: https://huggingface.co/blog/lbourdois/introduction-to-trimming
Models: alphaedge-ai/Trimming_models_search
An introduction to a little-known but highly effective model reduction method: 𝗧𝗿𝗶𝗺𝗺𝗶𝗻𝗴✂️
We show how to reduce model size (we went up to 87.24% reduction) while preserving its performance.
We applied this technique to 16 different model families across several modalities to illustrate that it works on any architecture (as long as the embedding layer is the last one of the model) and on any modality involving text.
From these 16 families, we generated over 𝟱,𝟱𝟬𝟬 𝗺𝗼𝗻𝗼𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗺𝗼𝗱𝗲𝗹𝘀 𝗶𝗻 𝟭𝟮𝟰 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀 🌍
Key takeaways from our experiments:
1️⃣ Trimming does not require a GPU. Our models were obtained on a CPU.
2️⃣ This method scales up to at least 4B parameters (we did not test beyond that).
3️⃣ Trimmed model is smaller than the original while preserving its performance. If you observe a slight performance drop, just fine-tuned to recover or even surpass the original performance.
4️⃣ For an equivalent compute budget, it is better to trim then fine-tune rather than fine-tuning the original model. Since the model is smaller, you can run more epochs/show more data and get in fine a better model than the original.
5️⃣ Trimming is a competitive alternative to distillation and quantization. E.g. we obtained our alternative to DistilBERT in 9 minutes on CPU vs. 90 hours of GPU for the latter.
6️⃣ Trimming could generate reasoning traces in the language of the trimmed model. This could be an alternative to generating traces in English and then translating them into the desired language.
And many other things (such as how much data are needed, the impact of the database used, the order in which it should be done, etc.) are available in the blogpost!
Blogpost: https://huggingface.co/blog/lbourdois/introduction-to-trimming
Models: alphaedge-ai/Trimming_models_search
- 4 replies
upvoted a changelog about 2 months ago
Hugging Face Changelog
Filter Models page by Base Models only
• 184
liked a
dataset 2 months ago
liked a
model 2 months ago
replied to PhysiQuanty's post 2 months ago
This is absolutely out of the box model.
reacted to PhysiQuanty's post with 🚀 2 months ago
Post
5154
❗ Dating apps do not allow us to control the profiles suggested to us based on our mutual search criteria ❗
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles
SpiceeChat/OkCupid-59k-Anonymized-Profiles
Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672
J'ai du breveté pour te trouver et on se trouvera bientôt !
🧬 If you want to see if your soulmate has already existed, I have published a dataset of 59k anonymized public profiles
SpiceeChat/OkCupid-59k-Anonymized-Profiles
Are you looking for a female ML engineer who is looking for a male ML engineer and you can't find it on the apps ?
You need to look for her, but more importantly, she needs to look for you.
Personally, I'm looking for a physicist I'm encountering the same problem. I can't find it
My answer : Paradox of choice of dating apps solved by patent ⚡ WO2026082672 ⚡
https://patentscope.wipo.int/search/en/detail.jsf?docId=WO2026082672
J'ai du breveté pour te trouver et on se trouvera bientôt !
- 9 replies
liked a
model 3 months ago
reacted to cihatyldz's post with 👍 3 months ago
Post
3619
Şifahane, a dual-inference medical classification demo, is now live on Spaces. It features side-by-side Turkish BERT and Qwen2.5 architectures for real-time evaluation of the "Classifier vs. LLM" trade-offs, all within a single space. The system utilizes a fine-tuned Turkish BERT for high-speed, cost-effective inference and the Qwen2.5-7B model for flexible multi-task reasoning, with support for department classification, condition analysis, urgency assessment, and rationale generation across 12 medical departments.
🧠 BERT model: https://lnkd.in/dCUUASqq
📊 Dataset: https://lnkd.in/dGK9y24w
🤗 Demo: https://lnkd.in/dtWjCCPF
🧠 BERT model: https://lnkd.in/dCUUASqq
📊 Dataset: https://lnkd.in/dGK9y24w
🤗 Demo: https://lnkd.in/dtWjCCPF
reacted to Crownelius's post with 🔥 3 months ago
Post
5964
My Huggingface journey has been a trip!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
I wanted to take the time to thank each and every one of you for using my dataset and getting it to go as far as it did. Believe it or not, some neanderthal was and maybe still is trending on huggingface.
Not only did my dataset reach number one, my fine-tuned qwen3.5 model did as well. Top 10. Honestly, ain't much left to do here.
Y'all have given me the desire, no... the craving for more. I am absolutely obsessed with AI now. I want to tweak it... I want to take it apart, just to see what makes everything tick. I want to put it together like Frankenstein and his monster.
The only thing that's stopping this guy is compute. I don't mind spending every penny I have on this. I desperately want to drive AI forward, even just a little bit.
I never knew the clanker hater from a year ago would be saying this.
Thank you all from the bottom of my heart.
Looking forward to showing you what I'm cooking up next. @CompactAI is your only hint!
- 3 replies
reacted to prithivMLmods's post with 🔥 4 months ago
Post
3163
Introducing QIE-Bbox-Studio! 🔥🤗
The QIE-Bbox-Studio demo is now live — more precise and packed with more options. Users can manipulate images with object removal, design addition, and even move objects from one place to another, all in just 4-step fast inference.
🤗 Demo: prithivMLmods/QIE-Bbox-Studio
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/QIE-Bbox-Studio
🚀 Models [LoRA] :
● QIE-2511-Object-Mover-Bbox: prithivMLmods/QIE-2511-Object-Mover-Bbox
● QIE-2511-Object-Remover-Bbox-v3: prithivMLmods/QIE-2511-Object-Remover-Bbox-v3
● QIE-2511-Outfit-Design-Layout: prithivMLmods/QIE-2511-Outfit-Design-Layout
● QIE-2509-Object-Remover-Bbox-v3: prithivMLmods/QIE-2509-Object-Remover-Bbox-v3
● QIE-2509-Object-Mover-Bbox: prithivMLmods/QIE-2509-Object-Mover-Bbox
🚀 Collection:
● Qwen Image Edit [Layout Bbox]: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox
To learn more, visit the app page or the respective model pages.
The QIE-Bbox-Studio demo is now live — more precise and packed with more options. Users can manipulate images with object removal, design addition, and even move objects from one place to another, all in just 4-step fast inference.
🤗 Demo: prithivMLmods/QIE-Bbox-Studio
🔗 GitHub: https://github.com/PRITHIVSAKTHIUR/QIE-Bbox-Studio
🚀 Models [LoRA] :
● QIE-2511-Object-Mover-Bbox: prithivMLmods/QIE-2511-Object-Mover-Bbox
● QIE-2511-Object-Remover-Bbox-v3: prithivMLmods/QIE-2511-Object-Remover-Bbox-v3
● QIE-2511-Outfit-Design-Layout: prithivMLmods/QIE-2511-Outfit-Design-Layout
● QIE-2509-Object-Remover-Bbox-v3: prithivMLmods/QIE-2509-Object-Remover-Bbox-v3
● QIE-2509-Object-Mover-Bbox: prithivMLmods/QIE-2509-Object-Mover-Bbox
🚀 Collection:
● Qwen Image Edit [Layout Bbox]: https://huggingface.co/collections/prithivMLmods/qwen-image-edit-layout-bbox
To learn more, visit the app page or the respective model pages.