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clefourrier  authored a paper about 1 month ago
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
rtetley  updated a Space about 1 month ago
fr-gouv-coordination-ia/llm_leaderboard_fr
rtetley  new activity about 1 month ago
fr-gouv-coordination-ia/llm_leaderboard_fr:traduction
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fdaudens 
posted an update about 6 hours ago
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Honored to be named among their 12 pioneers and power players in the news industry in the 2025 Tech Trends Report from Future Today Strategy Group.

Incredible group to be part of - each person is doing groundbreaking work at the intersection of AI and journalism. Worth following them all: they're consistently sharing practical insights on building the future of news.

Take the time to read this report, it's packed with insights as always. The news & information section's #1 insight hits hard: "The most substantive economic impact of AI to date has been licensing payouts for a handful of big publishers. The competition will start shifting in the year ahead to separate AI 'haves' that have positioned themselves to grow from the 'have-nots.'"

This AI-driven divide is something I've been really concerned about. Now is the time to build more than ever!

👉 Full report here: https://ftsg.com/wp-content/uploads/2025/03/FTSG_2025_TR_FINAL_LINKED.pdf
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fdaudens 
posted an update 3 days ago
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AI will bring us "a country of yes-men on servers" instead of one of "Einsteins sitting in a data center" if we continue on current trends.

Must-read by @thomwolf deflating overblown AI promises and explaining what real scientific breakthroughs require.

https://thomwolf.io/blog/scientific-ai.html
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fdaudens 
posted an update 9 days ago
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What if AI becomes as ubiquitous as the internet, but runs locally and transparently on our devices?

Fascinating TED talk by @thomwolf on open source AI and its future impact.

Imagine this for AI: instead of black box models running in distant data centers, we get transparent AI that runs locally on our phones and laptops, often without needing internet access. If the original team moves on? No problem - resilience is one of the beauties of open source. Anyone (companies, collectives, or individuals) can adapt and fix these models.

This is a compelling vision of AI's future that solves many of today's concerns around AI transparency and centralized control.

Watch the full talk here: https://www.ted.com/talks/thomas_wolf_what_if_ai_just_works
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fdaudens 
posted an update 11 days ago
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Is this the best tool to extract clean info from PDFs, handwriting and complex documents yet?

Open source olmOCR just dropped and the results are impressive.

Tested the free demo with various documents, including a handwritten Claes Oldenburg letter. The speed is impressive: 3000 tokens/second on your own GPU - that's 1/32 the cost of GPT-4o ($190/million pages). Game-changer for content extraction and digital archives.

To achieve this, Ai2 trained a 7B vision language model on 260K pages from 100K PDFs using "document anchoring" - combining PDF metadata with page images.

Best part: it actually understands document structure (columns, tables, equations) instead of just jumbling everything together like most OCR tools. Their human eval results back this up.

👉 Try the demo: https://olmocr.allenai.org

Going right into the AI toolkit: JournalistsonHF/ai-toolkit
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fdaudens 
posted an update 13 days ago
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🚀 Just launched: A toolkit of 20 powerful AI tools that journalists can use right now - transcribe, analyze, create. 100% free & open-source.

Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.

Some highlights I've tested personally:
- Private, on-device transcription with speaker ID in 100+ languages using Whisper
- Website scraping that just works - paste a URL, get structured data
- Local image editing with tools like Finegrain (impressive results)
- Document chat using Qwen 2.5 72B (handles technical papers well)

Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!

👉 JournalistsonHF/ai-toolkit
fdaudens 
posted an update 16 days ago
fdaudens 
posted an update 19 days ago
fdaudens 
posted an update 21 days ago
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Will we soon all have our own personalized AI news agents? And what does it mean for journalism?

Just built a simple prototype based on the Hugging Face course. It lets you get customized news updates on any topic.

Not perfect yet, but you can see where things could go: we'll all be able to build personalized AI agents that curate & analyze news for each of us. And users who could decide to build custom news products for their needs, such as truly personalized newsletters or podcasts.

The implications for both readers & news organizations are significant. To name a few:
- Will news articles remain the best format for informing people?
- What monetization model will work for news organizations?
- How do you create an effective conversion funnel?

👉 Try it here: fdaudens/my-news-agent (Code is open-source)
👉 Check out the course: https://huggingface.co./learn/agents-course/unit0/introduction
fdaudens 
posted an update 23 days ago
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🔊 Meet Kokoro Web - Free, ML speech synthesis on your computer, that'll make you ditch paid services!

28 natural voices, unlimited generations, and WebGPU acceleration. Perfect for journalists and content creators.

Test it with full articles—sounds amazingly human! 🎯🎙️

Xenova/kokoro-web
fdaudens 
posted an update 24 days ago
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⭐️ The AI Energy Score project just launched - this is a game-changer for making informed decisions about AI deployment.

You can now see exactly how much energy your chosen model will consume, with a simple 5-star rating system. Think appliance energy labels, but for AI.

Looking at transcription models on the leaderboard is fascinating: choosing between whisper-tiny or whisper-large-v3 can make a 7x difference. Real-time data on these tradeoffs changes everything.

166 models already evaluated across 10 different tasks, from text generation to image classification. The whole thing is public and you can submit your own models to test.

Why this matters:
- Teams can pick efficient models that still get the job done
- Developers can optimize for energy use from day one
- Organizations can finally predict their AI environmental impact

If you're building with AI at any scale, definitely worth checking out.

👉 leaderboard: https://lnkd.in/esrSxetj
👉 blog post: https://lnkd.in/eFJvzHi8

Huge work led by @sasha with @bgamazay @yjernite @sarahooker @regisss @meg
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fdaudens 
posted an update 28 days ago
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🔥 Video AI is taking over! Out of 17 papers dropped on Hugging Face today, 6 are video-focused - from Sliding Tile Attention to On-device Sora. The race for next-gen video tech is heating up! 🎬🚀
fdaudens 
posted an update about 1 month ago
clefourrier 
authored a paper about 1 month ago
fdaudens 
posted an update about 1 month ago
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2392
📊 R1 just built its own download dashboard!

Some fresh stats: +6M downloads for 800+ derivative models vs 2M for originals. Watch the numbers grow here: fdaudens/deepseek-download-stats
fdaudens 
posted an update about 1 month ago
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🎯 Kokoro TTS just hit v1.0! 🚀

Small but mighty: 82M parameters, runs locally, speaks multiple languages. The best part? It's Apache 2.0 licensed!
This could unlock so many possibilities ✨

Check it out: hexgrad/Kokoro-82M
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fdaudens 
posted an update about 1 month ago
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💪 The open-source community is really unstoppable:

+5M total downloads for DeepSeek models on @hf .co
+4M are from the 700 models created by the community
That's 30% more than yesterday!
fdaudens 
posted an update about 1 month ago
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🚀 The open source community is unstoppable: 4M total downloads for DeepSeek models on Hugging Face, with 3.2M coming from the +600 models created by the community.

That's 30% more than yesterday!
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fdaudens 
posted an update about 1 month ago
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Yes, DeepSeek R1's release is impressive. But the real story is what happened in just 7 days after:

- Original release: 8 models, 540K downloads. Just the beginning...

- The community turned those open-weight models into +550 NEW models on Hugging Face. Total downloads? 2.5M—nearly 5X the originals.

The reason? DeepSeek models are open-weight, letting anyone build on top of them. Interesting to note that the community focused on quantized versions for better efficiency & accessibility. They want models that use less memory, run faster, and are more energy-efficient.

When you empower builders, innovation explodes. For everyone. 🚀

The most popular community model? @bartowski 's DeepSeek-R1-Distill-Qwen-32B-GGUF version — 1M downloads alone.
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fdaudens 
posted an update about 2 months ago
fdaudens 
posted an update about 2 months ago
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Reminder: Don’t. Use. ChatGPT. As. A. Calculator. Seriously. 🤖

Loved listening to @sasha on Hard Fork—it really made me think.

A few takeaways that hit home:
- Individual culpability only gets you so far. The real priority: demanding accountability and transparency from companies.
- Evaluate if generative AI is the right tool for certain tasks (like search) before using it.

Curious about the full conversation? https://www.nytimes.com/2025/01/17/podcasts/hardfork-tiktok-rednote-environment.html. Give it a listen—it’s worth it! 🌍
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