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William Suffill

wsuff

wsuff

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reacted to MikeDoes's post with 🔥 8 days ago

🚀 We are quite excited to announce the Ai4Privacy Python library! 🎉 pip install ai4privacy to anonymize short english text with OpenPII Masking 500k labels 📊 Day 5/7 of PII Masking 1M announcements complete! ⏰

reacted to m-ric's post with ❤️ 8 days ago

🚀 DeepSeek R1 moment has come for GUI agents: Rule-based Reinforcement Learning gives better results than SFT with 500x smaller datasets! Traditionally (by which I mean "in the last few months"), GUI agents have been trained with supervised fine-tuning (SFT). This meant, collecting huge datasets of screen captures from people using computers, and using these to fine-tune your model. 📚 👉 But last week, a new paper introduced UI-R1, applying DeepSeek's R1-style rule-based reinforcement learning (RL) specifically to GUI action prediction tasks. This is big news: with RL, maybe we could build good agents without the need for huge datasets. UI-R1 uses a unified reward function that evaluates multiple responses from models, optimizing via policy algorithms like Group Relative Policy Optimization (GRPO). Specifically, the reward function assesses: 🎯 Action type accuracy: Does the predicted action match the ground truth? 📍 Coordinate accuracy (specifically for clicks): Is the predicted click within the correct bounding box? 📑 Output format: Does the model clearly articulate both its reasoning and final action? Using just 136 carefully selected mobile tasks—compared to 76,000 tasks for larger models like OS-Atlas—UI-R1 shows significant efficiency and improved performance: 📈 Boosted action prediction accuracy from 76% to 89% on AndroidControl. 🌐 Outperformed larger, SFT-trained models (e.g., OS-Atlas-7B), demonstrating superior results with vastly fewer data points (136 tasks vs. 76K). 🔍 Enhanced adaptability and generalization, excelling even in out-of-domain scenarios. The paper tests this RL-based method only in low-level GUI tasks. Could it generalize to more complex interactions? 🧐 Read the full paper here 👉 https://huggingface.co/papers/2503.21620

reacted to zamal's post with 👍 8 days ago

DeepGit: Your GitHub Gold Digger! 💰🚀 Hey Hugging Face gang! Meet DeepGit—my 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 💎 Unearth that sneaky ML lib or Python gem—run python app.py or langgraph dev and boom! Peek it at https://github.com/zamalali/DeepGit. Fork it, tweak it, love it—Docker’s in, HF vibes are strong. Drop a 🌟 or a crazy idea—I’m pumped to jam with you all! 🪂

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upvoted 2 collections 4 months ago

Recommended small models

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This is everything recent smaller than ~25B parameters that are high quality/reputable • 19 items • Updated Nov 30, 2024 • 87

🤖 Agents

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21 items • Updated Dec 31, 2024 • 148

upvoted a collection 5 months ago

Qwen2.5

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Qwen2.5 language models, including pretrained and instruction-tuned models of 7 sizes, including 0.5B, 1.5B, 3B, 7B, 14B, 32B, and 72B. • 46 items • Updated Feb 26 • 582

upvoted 2 collections 8 months ago

Open-source AI Releases - August '24

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8 items • Updated Aug 22, 2024 • 5

Hermes 3

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The Hermes 3 Series of Models • 12 items • Updated Feb 13 • 114

upvoted a collection 11 months ago

Phi-3

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Phi-3 family of small language and multi-modal models. Language models are available in short- and long-context lengths. • 26 items • Updated Jan 8 • 566