Energy is a massive constraint for AI but do you even know what energy your chatGPT convos are using?
We're trying to change this by releasing ChatUI-energy, the first interface where you see in real-time what energy your AI conversations consume. Great work from @jdelavande powered by spaces & TGI, available for a dozen of open-source models like Llama, Mistral, Qwen, Gemma and more.
Today in Privacy & AI Tooling - introducing a nifty new tool to examine where data goes in open-source apps on ๐ค
HF Spaces have tons (100Ks!) of cool demos leveraging or examining AI systems - and because most of them are OSS we can see exactly how they handle user data ๐๐
That requires actually reading the code though, which isn't always easy or quick! Good news: code LMs have gotten pretty good at automatic review, so we can offload some of the work - here I'm using Qwen/Qwen2.5-Coder-32B-Instruct to generate reports and it works pretty OK ๐
The app works in three stages: 1. Download all code files 2. Use the Code LM to generate a detailed report pointing to code where data is transferred/(AI-)processed (screen 1) 3. Summarize the app's main functionality and data journeys (screen 2) 4. Build a Privacy TLDR with those inputs
It comes with a bunch of pre-reviewed apps/Spaces, great to see how many process data locally or through (private) HF endpoints ๐ค
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.
AI agents are transforming how we interact with technology, but how sustainable are they? ๐
Design choices โ like model size and structure โ can massively impact energy use and cost. โก๐ฐ The key takeaway: smaller, task-specific models can be far more efficient than large, general-purpose ones.
๐ Open-source models offer greater transparency, allowing us to track energy consumption and make more informed decisions on deployment. ๐ฑ Open-source = more efficient, eco-friendly, and accountable AI.
Huge week for xet-team as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.
Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.
We expect builders on the Hub to see even more improvements, helping power innovation across the community.
With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.
Thanks to the meta-llama team for launching on Xet!
reacted to giadap's
post with โค๏ธ๐ฅabout 1 month ago
We've all become experts at clicking "I agree" without a second thought. In my latest blog post, I explore why these traditional consent models are increasingly problematic in the age of generative AI.
I found three fundamental challenges: - Scope problem: how can you know what you're agreeing to when AI could use your data in different ways? - Temporality problem: once an AI system learns from your data, good luck trying to make it "unlearn" it. - Autonomy trap: the data you share today could create systems that pigeonhole you tomorrow.
Individual users shouldn't bear all the responsibility, while big tech holds all the cards. We need better approaches to level the playing field, from collective advocacy and stronger technological safeguards to establishing "data fiduciaries" with a legal duty to protect our digital interests.
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Exciting breakthrough in Retrieval-Augmented Generation (RAG): Introducing MiniRAG - a revolutionary approach that makes RAG systems accessible for edge devices and resource-constrained environments.
Key innovations that set MiniRAG apart:
Semantic-aware Heterogeneous Graph Indexing - Combines text chunks and named entities in a unified structure - Reduces reliance on complex semantic understanding - Creates rich semantic networks for precise information retrieval
Lightweight Topology-Enhanced Retrieval - Leverages graph structures for efficient knowledge discovery - Uses pattern matching and localized text processing - Implements query-guided reasoning path discovery
Impressive Performance Metrics - Achieves comparable results to LLM-based methods while using Small Language Models (SLMs) - Requires only 25% of storage space compared to existing solutions - Maintains robust performance with accuracy reduction ranging from just 0.8% to 20%
The researchers from Hong Kong University have also contributed a comprehensive benchmark dataset specifically designed for evaluating lightweight RAG systems under realistic on-device scenarios.
This breakthrough opens new possibilities for: - Edge device AI applications - Privacy-sensitive implementations - Real-time processing systems - Resource-constrained environments
The full implementation and datasets are available on GitHub: HKUDS/MiniRAG
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reacted to fdaudens's
post with โค๏ธ3 months ago
Reminder: Donโt. Use. ChatGPT. As. A. Calculator. Seriously. ๐ค
Loved listening to @sasha on Hard Forkโit 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.
๐ซ...And we're live!๐ซ Seasonal newsletter from ethicsy folks at Hugging Face, exploring the ethics of "AI Agents" https://huggingface.co/blog/ethics-soc-7 Our analyses found: - There's a spectrum of "agent"-ness - *Safety* is a key issue, leading to many other value-based concerns Read for details & what to do next! With @evijit , @giadap , and @sasha
