Improving Multilingual Capabilities with Cultural and Local Knowledge in Large Language Models While Enhancing Native Performance
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1024mย
authored
a
paper
about 10 hours ago
Post
1960
Want AI that truly understands your country's culture? Public institutions are sitting on the next AI revolution - and here's the practical guide to unlock it.
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
I've had fascinating conversations recently about sovereign AI, with people trying to solve this recurring question: "How do we build AI that truly understands our culture?"
This guide by @evijit and @yjernite brings lots of insights about this question. It's not just about throwing data at models. It's about partnering cultural expertise with tech infrastructure in ways we're just starting to figure out.
An example? The National Library of Norway already has 150+ AI models on Hugging Face. They're not just digitizing books - they're building AI that thinks in Norwegian, understands Norwegian values, and serves Norwegian citizens.
This is sovereign AI in practice: technology that understands your culture, values, and languages.
Especially loved the practical examples on how to do this:
- Real examples from museums, libraries, and government agencies
- How to convert complex documents (PDFs, PowerPoints) into ML-ready formats
- Code templates for processing public data
- Technical recipes for sharing datasets on open platforms
The stakes? Citizens' ability to leverage their collective digital intelligence.
The technology is ready. The infrastructure exists. The guide shows exactly how to use it. What's needed is your cultural expertise to shape these tools.
Check it out: https://huggingface.co/blog/evijit/public-org-data-ai
P.s.: Building cool projects in a public institution? Share them in the comments for others to learn from!
Post
2701
Do chatbots lie about Cรฉline Dion? We now have answers, not speculation.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Cรฉline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
๐ Blog post: https://allenai.org/blog/olmotrace
๐ Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
Ai2 just released OLMoTrace and it's a game-changer for transparency. You can literally see where an AI's responses come from in its training data - in real time.
The demo shows results about Cรฉline. So I tried it out myself! Watch what happens in the video.
For journalists, researchers studying hallucinations and anyone who needs to trust their AI, this is like getting X-ray vision into AI systems. When the model made claims, I could instantly verify them against original sources. When it hallucinated, I could see why.
You can finally 1) understand how LLMs actually work and 2) verify if what they're saying is true. No more blind trust.
This pushes the open data movement to the next level.
๐ Blog post: https://allenai.org/blog/olmotrace
๐ Paper: https://www.datocms-assets.com/64837/1743890415-olmotrace.pdf
P.S.: A word of caution: never use a chatbot as a knowledge base. It's not Google. Better use it with a connection to the internet.
- 1 reply
1024mย
authored
a
paper
5 days ago
Post
3986
๐จ Designers, meet OmniSVG! This new model helps you create professional vector graphics from text/images, generate editable SVGs from icons to detailed characters, convert rasters to vectors, maintain style consistency with references, and integrate into your workflow.
@OmniSVG
@OmniSVG
- 2 replies
davanstrienย
posted
an
update
6 days ago
Post
1501
I've created a v1 dataset (
davanstrien/reasoning-required) and model (
davanstrien/ModernBERT-based-Reasoning-Required) to help curate "wild text" data for generating reasoning examples beyond the usual code/math/science domains.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
- I developed a "Reasoning Required" dataset with a 0-4 scoring system for reasoning complexity
- I used educational content from HuggingFaceFW/fineweb-edu, adding annotations for domains, reasoning types, and example questions
My approach enables a more efficient workflow: filter text with small models first, then use LLMs only on high-value content.
This significantly reduces computation costs while expanding reasoning dataset domain coverage.
Post
3574
I read the 456-page AI Index report so you don't have to (kidding). The wild part? While AI gets ridiculously more accessible, the power gap is actually widening:
1๏ธโฃ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM
2๏ธโฃ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio
The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.
Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
1๏ธโฃ The democratization of AI capabilities is accelerating rapidly:
- The gap between open and closed models is basically closed: difference in benchmarks like MMLU and HumanEval shrunk to just 1.7% in 2024
- The cost to run GPT-3.5-level performance dropped 280x in 2 years
- Model size is shrinking while maintaining performance - Phi-3-mini hitting 60%+ MMLU at fraction of parameters of early models like PaLM
2๏ธโฃ But we're seeing concerning divides deepening:
- Geographic: US private investment ($109B) dwarfs everyone else - 12x China's $9.3B
- Research concentration: US and China dominate highly-cited papers (50 and 34 respectively in 2023), while next closest is only 7
- Gender: Major gaps in AI skill penetration rates - US shows 2.39 vs 1.71 male/female ratio
The tech is getting more accessible but the benefits aren't being distributed evenly. Worth thinking about as these tools become more central to the economy.
Give it a read - fascinating portrait of where AI is heading! https://hai-production.s3.amazonaws.com/files/hai_ai_index_report_2025.pdf
- 3 replies
prithivMLmodsย
posted
an
update
9 days ago
Post
3187
Loaded some domain-specific downstream image classification content moderation models, which is essentially the practice of monitoring and filtering user-generated content on platforms, based on SigLIP-2 Base Patch16 with newly initialized trainable parameters. ๐ฅ
+ Age-Classification-SigLIP2 : prithivMLmods/Age-Classification-SigLIP2
[ Age range classification from 0 to 65+ years ]
+ Facial-Emotion-Detection-SigLIP2 : prithivMLmods/Facial-Emotion-Detection-SigLIP2
[ Designed to classify different facial emotions ]
+ Hand-Gesture-2-Robot : prithivMLmods/Hand-Gesture-2-Robot
[ Human Hand Gesture Classification for Robot Control ]
+ Mature-Content-Detection : prithivMLmods/Mature-Content-Detection
[ Mature [adult] or neutral content categories ]
+ Vit-Mature-Content-Detection : prithivMLmods/Vit-Mature-Content-Detection
[ Mature [adult] or neutral content categories ft. ViT]
+ Human-Action-Recognition : prithivMLmods/Human-Action-Recognition
[ Human actions including clapping, sitting, running, and more ]
+ Mirage-Photo-Classifier : prithivMLmods/Mirage-Photo-Classifier
[ Whether an image is real or AI-generated (fake) ]
+ Food-101-93M : prithivMLmods/Food-101-93M
[ Classify food images into one of 101 popular dishes ]
+ Hand-Gesture-19 : prithivMLmods/Hand-Gesture-19
[ Classify hand gesture images into different categories ]
+ Trash-Net : prithivMLmods/Trash-Net
[ Classification of trash into six distinct categories ]
+ Gender-Classifier-Mini : prithivMLmods/Gender-Classifier-Mini
[ Classify images based on gender [Male / Female] ]
๐กCollections :
+ SigLIP2 Content Filters : prithivMLmods/siglip2-content-filters-models-67f001055ec2bed56ca41f6d
+ Age-Classification-SigLIP2 : prithivMLmods/Age-Classification-SigLIP2
[ Age range classification from 0 to 65+ years ]
+ Facial-Emotion-Detection-SigLIP2 : prithivMLmods/Facial-Emotion-Detection-SigLIP2
[ Designed to classify different facial emotions ]
+ Hand-Gesture-2-Robot : prithivMLmods/Hand-Gesture-2-Robot
[ Human Hand Gesture Classification for Robot Control ]
+ Mature-Content-Detection : prithivMLmods/Mature-Content-Detection
[ Mature [adult] or neutral content categories ]
+ Vit-Mature-Content-Detection : prithivMLmods/Vit-Mature-Content-Detection
[ Mature [adult] or neutral content categories ft. ViT]
+ Human-Action-Recognition : prithivMLmods/Human-Action-Recognition
[ Human actions including clapping, sitting, running, and more ]
+ Mirage-Photo-Classifier : prithivMLmods/Mirage-Photo-Classifier
[ Whether an image is real or AI-generated (fake) ]
+ Food-101-93M : prithivMLmods/Food-101-93M
[ Classify food images into one of 101 popular dishes ]
+ Hand-Gesture-19 : prithivMLmods/Hand-Gesture-19
[ Classify hand gesture images into different categories ]
+ Trash-Net : prithivMLmods/Trash-Net
[ Classification of trash into six distinct categories ]
+ Gender-Classifier-Mini : prithivMLmods/Gender-Classifier-Mini
[ Classify images based on gender [Male / Female] ]
๐กCollections :
+ SigLIP2 Content Filters : prithivMLmods/siglip2-content-filters-models-67f001055ec2bed56ca41f6d
Post
2339
See that purple banner on the Llama 4 models? It's Xet storage, and this is actually huge for anyone building with AI models. Let's geek out a little bit ๐ค
Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.
Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.
Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.
This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! ๐งจ
Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Current problem: AI models are massive files using Git LFS. But with models getting bigger and downloads exploding, we needed something better.
Xet lets you version large files like code, with compression and deduplication, all Git-compatible. That means less bandwidth, faster sharing, and smoother collaboration.
Real numbers: ~25% deduplication on Llama 4 models, hitting ~40% for finetunes.
Scale matters here - the Hub served 2B model downloads in 30 days, Llama models alone at 60M. The upcoming Llama 4 Behemoth has 2T parameters! Xet's chunk-based system was built exactly for this.
This is the kind of engineering that makes the next wave of large models actually usable. Kudos to the team! ๐งจ
Check out the models collection: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
prithivMLmodsย
posted
an
update
10 days ago
Post
2097
ChatGPT-4oโs image generation goes wild for a weekโfeaturing everything from Studio Ghibli-style art and image colorization to style intermixing. Here are some examples showcasing the generation of highly detailed images from freestyle design templates. Want to know more? Check out the blog ๐
๐Blog : https://huggingface.co/blog/prithivMLmods/chatgpt-4o-image-gen
๐Blog : https://huggingface.co/blog/prithivMLmods/chatgpt-4o-image-gen
Post
2480
"Am I going to be replaced by AI?" - Crucial question, but maybe we're asking the wrong one.
๐ There's a statistic from my reads this week that stays with me: Tomer Cohen, LinkedIn's CPO, shares to Jeremy Kahn that 70% of skills used in most jobs will change by 2030. Not jobs disappearing, but transforming. And he calls out bad leadership: "If in one year's time, you are disappointed that your workforce is not 'AI native,' it is your fault."
๐ Apparently, the Great Recalibration has begun. We're now heading into an era where AI is fundamentally redefining the nature of work itself, by forcing a complete reassessment of human value in the workplace, according to a piece in Fast Company. But it might be driven more by "the need for humans to change the way they work" than AI.
โก The Washington Post draws a crucial parallel: We're facing an "AI shock" similar to manufacturing's "China shock" - but hitting knowledge workers. Especially entry-level, white-collar work could get automated. The key difference? "Winning the AI tech competition with other countries won't be enough. It's equally vital to win the battle to re-skill workers."
Digging into these big questions in this weekโs AI in the News: https://fdaudens.substack.com/publish/posts/detail/160596301
Also, I'm curious: how are you keeping up with this pace of change? What strategies are working for you?
๐ There's a statistic from my reads this week that stays with me: Tomer Cohen, LinkedIn's CPO, shares to Jeremy Kahn that 70% of skills used in most jobs will change by 2030. Not jobs disappearing, but transforming. And he calls out bad leadership: "If in one year's time, you are disappointed that your workforce is not 'AI native,' it is your fault."
๐ Apparently, the Great Recalibration has begun. We're now heading into an era where AI is fundamentally redefining the nature of work itself, by forcing a complete reassessment of human value in the workplace, according to a piece in Fast Company. But it might be driven more by "the need for humans to change the way they work" than AI.
โก The Washington Post draws a crucial parallel: We're facing an "AI shock" similar to manufacturing's "China shock" - but hitting knowledge workers. Especially entry-level, white-collar work could get automated. The key difference? "Winning the AI tech competition with other countries won't be enough. It's equally vital to win the battle to re-skill workers."
Digging into these big questions in this weekโs AI in the News: https://fdaudens.substack.com/publish/posts/detail/160596301
Also, I'm curious: how are you keeping up with this pace of change? What strategies are working for you?
Post
1747
๐ DeepGit Lite is live! ๐โจ
Hey folks!
Just launched DeepGit Lite โ a lighter version of DeepGit with fewer components under the hood.
It wonโt perform quite like the full powerhouse, but itโs great for a quick peek and first-hand feel! โ๏ธ๐
Give it a spin and tell us what you think!
๐ Try it here zamal/DeepGit-lite
#opensource #DeepGit #gradio #githubresearch
Hey folks!
Just launched DeepGit Lite โ a lighter version of DeepGit with fewer components under the hood.
It wonโt perform quite like the full powerhouse, but itโs great for a quick peek and first-hand feel! โ๏ธ๐
Give it a spin and tell us what you think!
๐ Try it here zamal/DeepGit-lite
#opensource #DeepGit #gradio #githubresearch
- 1 reply
Post
2227
Did we just drop personalized AI evaluation?! This tool auto-generates custom benchmarks on your docs to test which models are the best.
Most benchmarks test general capabilities, but what matters is how models handle your data and tasks. YourBench helps answer critical questions like:
- Do you really need a hundreds-of-billions-parameter model sledgehammer to crack a nut?
- Could a smaller, fine-tuned model work better?
- How well do different models understand your domain?
Some cool features:
๐ Generates custom benchmarks from your own documents (PDFs, Word, HTML)
๐ฏ Tests models on real tasks, not just general capabilities
๐ Supports multiple models for different pipeline stages
๐ง Generate both single-hop and multi-hop questions
๐ Evaluate top models and deploy leaderboards instantly
๐ฐ Full cost analysis to optimize for your budget
๐ ๏ธ Fully configurable via a single YAML file
26 SOTA models tested for question generation. Interesting finding: Qwen2.5 32B leads in question diversity, while smaller Qwen models and Gemini 2.0 Flash offer great value for cost.
You can also run it locally on any models you want.
I'm impressed. Try it out: yourbench/demo
Most benchmarks test general capabilities, but what matters is how models handle your data and tasks. YourBench helps answer critical questions like:
- Do you really need a hundreds-of-billions-parameter model sledgehammer to crack a nut?
- Could a smaller, fine-tuned model work better?
- How well do different models understand your domain?
Some cool features:
๐ Generates custom benchmarks from your own documents (PDFs, Word, HTML)
๐ฏ Tests models on real tasks, not just general capabilities
๐ Supports multiple models for different pipeline stages
๐ง Generate both single-hop and multi-hop questions
๐ Evaluate top models and deploy leaderboards instantly
๐ฐ Full cost analysis to optimize for your budget
๐ ๏ธ Fully configurable via a single YAML file
26 SOTA models tested for question generation. Interesting finding: Qwen2.5 32B leads in question diversity, while smaller Qwen models and Gemini 2.0 Flash offer great value for cost.
You can also run it locally on any models you want.
I'm impressed. Try it out: yourbench/demo
Post
2536
DeepGit: Your GitHub Gold Digger! ๐ฐ๐
Hey Hugging Face gang! Meet DeepGitโmy open-source sidekick that rips through GitHub to snag repos that fit you. Done with dead-end searches? Me too. Built it with LangGraph and some dope tricks:
Embeddings grab the good stuff (HF magic, baby!)
Re-ranking nails the best picks
Snoops docs, code, and buzz in one slick flow
Drops a clean list of hidden gems ๐
Unearth that sneaky ML lib or Python gemโrun python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love itโDockerโs in, HF vibes are strong. Drop a ๐ or a crazy ideaโIโm pumped to jam with you all! ๐ช
Hey Hugging Face gang! Meet DeepGitโmy open-source sidekick that rips through GitHub to snag repos that fit you. Done with dead-end searches? Me too. Built it with LangGraph and some dope tricks:
Embeddings grab the good stuff (HF magic, baby!)
Re-ranking nails the best picks
Snoops docs, code, and buzz in one slick flow
Drops a clean list of hidden gems ๐
Unearth that sneaky ML lib or Python gemโrun python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love itโDockerโs in, HF vibes are strong. Drop a ๐ or a crazy ideaโIโm pumped to jam with you all! ๐ช
Post
1968
๐ฅ DeepSeek vibe coding with DeepSite is going viral with awesome projects!
From games to stunning visualizations, 7 wild examples:
๐บ AI TV with custom channels and animations https://x.com/_akhaliq/status/1905747381951545647
๐ Earth to Moon spacecraft journey visualization
Watch this incredible Three.js space simulation with zero external assets:
https://x.com/_akhaliq/status/1905836902533451999
๐ฃ Minesweeper in 2.5 minutes! Built & deployed instantly on DeepSite. Zero setup needed:
https://x.com/cholf5/status/1906031928937218334
๐ฎ Asked for Game of Life, got a masterpiece. Simple prompt, complex features. See it in action: https://x.com/pbeyssac/status/1906304454824992844
๐ซ One-shot anime website with perfect UI. DeepSite turned a simple request into a fully-functional anime site: https://x.com/risphereeditor/status/1905961725028913264
๐ 10-minute World Indicators Dashboard. Just described what I wanted and got a full interactive dashboard! https://x.com/i/status/1906345214089785634
โจ Ready to build without coding? Imagine it. Build it. Share it! enzostvs/deepsite
From games to stunning visualizations, 7 wild examples:
๐บ AI TV with custom channels and animations https://x.com/_akhaliq/status/1905747381951545647
๐ Earth to Moon spacecraft journey visualization
Watch this incredible Three.js space simulation with zero external assets:
https://x.com/_akhaliq/status/1905836902533451999
๐ฃ Minesweeper in 2.5 minutes! Built & deployed instantly on DeepSite. Zero setup needed:
https://x.com/cholf5/status/1906031928937218334
๐ฎ Asked for Game of Life, got a masterpiece. Simple prompt, complex features. See it in action: https://x.com/pbeyssac/status/1906304454824992844
๐ซ One-shot anime website with perfect UI. DeepSite turned a simple request into a fully-functional anime site: https://x.com/risphereeditor/status/1905961725028913264
๐ 10-minute World Indicators Dashboard. Just described what I wanted and got a full interactive dashboard! https://x.com/i/status/1906345214089785634
โจ Ready to build without coding? Imagine it. Build it. Share it! enzostvs/deepsite
Post
2120
Want to vibecode with DeepSeek? Just spent 10 minutes with this space and created a full world indicators dashboard - literally just by describing what I wanted!
Anyone can now prototype and deploy projects instantly.
Try out the app: enzostvs/deepsite
My dashboard: fdaudens/world-indicators
Anyone can now prototype and deploy projects instantly.
Try out the app: enzostvs/deepsite
My dashboard: fdaudens/world-indicators
prithivMLmodsย
posted
an
update
16 days ago
Post
1857
Luna, the single-speaker text-to-speech model, features a Radio & Atcosim-style sound with a female voice. It offers authentic radio podcast noise and empathetic speech generation, fine-tuned based on Orpheus's Llama-based speech generation state-of-the-art model. ๐๏ธ
+ Model : prithivMLmods/Llama-3B-Mono-Luna
+ Collection : prithivMLmods/clean-radio-mono-voice-67e76fe1b3a87cc3bccef803
+ Reference ft : https://github.com/canopyai/Orpheus-TTS
+ Base Model : canopylabs/orpheus-3b-0.1-ft
I also tried some other clean-voice single-speaker models based on Orpheus. If you're interested, check out the collection.
๐Try the Mono Luna demo here: http://colab.research.google.com/drive/1K0AAIOKDE5XE0znxXaiiUJvPSpFveteK
+ Model : prithivMLmods/Llama-3B-Mono-Luna
+ Collection : prithivMLmods/clean-radio-mono-voice-67e76fe1b3a87cc3bccef803
+ Reference ft : https://github.com/canopyai/Orpheus-TTS
+ Base Model : canopylabs/orpheus-3b-0.1-ft
I also tried some other clean-voice single-speaker models based on Orpheus. If you're interested, check out the collection.
๐Try the Mono Luna demo here: http://colab.research.google.com/drive/1K0AAIOKDE5XE0znxXaiiUJvPSpFveteK
-
- 6 replies
Post
1975
Want to ramp up your AI skills and start breaking bigger stories? With the Journalists on Hugging Face community, we're launching our first learn-together course!
We'll build AI classifiers that process months of data in minutes. How?
- Work through an interactive version of an excellent course developed by Ben Welsh and Derek Willis
- Share findings and get help in our dedicated community channel
- Build working classifiers you can use in your reporting today
No coding background needed - if you can write a ChatGPT or Claude prompt, you can do this. Journalists are already using these techniques to break stories, from uncovering hidden real estate deals to tracking unusual campaign spending.
Join usโit might give you your next big story!
Thanks to Ben and Derek for letting me adapt their excellent course into this interactive version!
- Check out the course: JournalistsonHF/first-llm-classifier
- Join our Slack community to learn together: https://docs.google.com/forms/d/e/1FAIpQLSfyA7G6Y9q-5hDBSnGc3CFtg9H8fjqKCCuieptXuTqRudGNjQ/viewform
We'll build AI classifiers that process months of data in minutes. How?
- Work through an interactive version of an excellent course developed by Ben Welsh and Derek Willis
- Share findings and get help in our dedicated community channel
- Build working classifiers you can use in your reporting today
No coding background needed - if you can write a ChatGPT or Claude prompt, you can do this. Journalists are already using these techniques to break stories, from uncovering hidden real estate deals to tracking unusual campaign spending.
Join usโit might give you your next big story!
Thanks to Ben and Derek for letting me adapt their excellent course into this interactive version!
- Check out the course: JournalistsonHF/first-llm-classifier
- Join our Slack community to learn together: https://docs.google.com/forms/d/e/1FAIpQLSfyA7G6Y9q-5hDBSnGc3CFtg9H8fjqKCCuieptXuTqRudGNjQ/viewform
prithivMLmodsย
posted
an
update
20 days ago
Post
1696
Dropping some new Journey Art and Realism adapters for Flux.1-Dev, including Thematic Arts, 2021 Memory Adapters, Thread of Art, Black of Art, and more. For more details, visit the model card on Stranger Zone HF ๐ค
+ Black-of-Art-Flux : strangerzonehf/Black-of-Art-Flux
+ Thread-of-Art-Flux : strangerzonehf/Thread-of-Art-Flux
+ 2021-Art-Flux : strangerzonehf/2021-Art-Flux
+ 3d-Station-Toon : strangerzonehf/3d-Station-Toon
+ New-Journey-Art-Flux : strangerzonehf/New-Journey-Art-Flux
+ Casual-Pencil-Pro : strangerzonehf/Casual-Pencil-Pro
+ Realism-H6-Flux : strangerzonehf/Realism-H6-Flux
- Repository Page :
strangerzonehf
The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.
+ Black-of-Art-Flux : strangerzonehf/Black-of-Art-Flux
+ Thread-of-Art-Flux : strangerzonehf/Thread-of-Art-Flux
+ 2021-Art-Flux : strangerzonehf/2021-Art-Flux
+ 3d-Station-Toon : strangerzonehf/3d-Station-Toon
+ New-Journey-Art-Flux : strangerzonehf/New-Journey-Art-Flux
+ Casual-Pencil-Pro : strangerzonehf/Casual-Pencil-Pro
+ Realism-H6-Flux : strangerzonehf/Realism-H6-Flux
- Repository Page :
strangerzonehf
The best dimensions and inference settings for optimal results are as follows: A resolution of 1280 x 832 with a 3:2 aspect ratio is recommended for the best quality, while 1024 x 1024 with a 1:1 aspect ratio serves as the default option. For inference, the recommended number of steps ranges between 30 and 35 to achieve optimal output.
- 1 reply
prithivMLmodsย
posted
an
update
22 days ago
Post
2605
Dropping Downstream tasks using newly initialized parameters and weights ([classifier.bias & weights]) support domain-specific ๐ถ๐บ๐ฎ๐ด๐ฒ ๐ฐ๐น๐ฎ๐๐๐ถ๐ณ๐ถ๐ฐ๐ฎ๐๐ถ๐ผ๐ป. Based on siglip2-base-patch16-224 and DomainNet (single-domain, multi-source adaptation), with Fashion-MNIST & More for experimental testing. ๐งคโ๏ธ
Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet
Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers ๐ค.
Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782
Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754
Fashion-Mnist : prithivMLmods/Fashion-Mnist-SigLIP2
Mnist-Digits : prithivMLmods/Mnist-Digits-SigLIP2
Multisource-121 : prithivMLmods/Multisource-121-DomainNet
Painting-126 : prithivMLmods/Painting-126-DomainNet
Sketch-126 : prithivMLmods/Sketch-126-DomainNet
Clipart-126 : prithivMLmods/Clipart-126-DomainNet
Models are trained with different parameter settings for experimental purposes only, with the intent of further development. Refer to the model page below for instructions on running it with Transformers ๐ค.
Collection : prithivMLmods/domainnet-0324-67e0e3c934c03cc40c6c8782
Citations : SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features https://arxiv.org/pdf/2502.14786 & Moment Matching for Multi-Source Domain Adaptation : https://arxiv.org/pdf/1812.01754