Building in 2026 https://t.co/w8K4JRzqhD

Building in 2026 https://t.co/w8K4JRzqhD

trying a ralph loop to write my rag ebook with more material https://t.co/cYSStTWa3m

one is mapped to cursor composer one is mapped to push to transcribe one is mapped to start a loom video https://t.co/J6MRlyzWqb

👏🏻Congratulations！Step3-VL-10B was selected for HuggingFace Daily Papers today. Check it out here：https://t.co/zro7vQUqzh https://t.co/VVxMVGL0P9

👏🏻Congratulations！Step3-VL-10B was selected for HuggingFace Daily Papers today. Check it out here：https://t.co/zro7vQUqzh https://t.co/VVxMVGL0P9

V-DPM 4D Video Reconstruction with Dynamic Point Maps https://t.co/TdBRHDzrsd

discuss: https://t.co/DTXKcIwk84

app: https://t.co/roiCHYhEx6

HeartMuLa A Family of Open Sourced Music Foundation Models https://t.co/TJzg6eMEXZ

paper: https://t.co/2ALsoeOEji

model: https://t.co/W7HamtcMzs

STEP3-VL-10B Technical Report https://t.co/TiSlflEB58

SeedFold Scaling Biomolecular Structure Prediction https://t.co/XFVd625BEW

Transition Matching Distillation for Fast Video Generation https://t.co/YXFty6ul0W

discuss: https://t.co/YWnc9zFtNO

RigMo Unifying Rig and Motion Learning for Generative Animation https://t.co/FkionIZZLr

discuss: https://t.co/doYeEa9MO3

What makes preference data truly effective for LLM alignment? 🤔 Introducing AIR: A systematic framework that deconstructs preference datasets into 3 core components (Annotations, Instructions, Response Pairs) and reveals evidence-based optimization principles. No more trial and error! 🤗 Paper: https://t.co/dFxRHSux2W 📄 arXiv: https://t.co/QJl1T1uGyi Why it matters: 1️⃣ Simplicity Wins in Annotations: Basic point-wise scoring with generative models (like Llama-3.1-70B-Instruct) + greedy decoding outperforms complex methods. Less is more—excessive design introduces noise rather than clarity. 2️⃣ Smart Instruction Filtering: Select instructions with low response variance across LLMs. This forces models to learn fine-grained preferences (like logical rigor) rather than relying on obvious differences. 3️⃣ Balanced Response Pairs: Optimal pairs combine moderate score gaps (Δ=2-3), high absolute quality (≥8), and 1:1 On/Off-Policy mixing—achieving clear contrast without overfitting. The results? +5.3 average gain across 6 benchmarks (WildBench, Arenahard, etc) with just 14k curated pairs from 17 open-source LLMs covering coding, math, and chat tasks. AIR transforms preference learning from "scale blindly" to component-aware design—a blueprint for building smarter, more aligned AI systems. 🚀 #AI #LLM #RLHF #PreferenceLearning #Alignment

Alterbute Editing Intrinsic Attributes of Objects in Images https://t.co/BGBFK5w6eE

Tiny model, big orbit. https://t.co/zssZcL2T8C

Tiny model, big orbit. https://t.co/zssZcL2T8C

Download weights: @kaggle → https://t.co/DfOtwrIBiS @huggingface ↓ https://t.co/iteHS5eolu

Download weights: @kaggle → https://t.co/DfOtwrIBiS @huggingface ↓ https://t.co/iteHS5eolu

Built on Gemma 3, TranslateGemma was trained on data generated by Gemini – effectively transferring its intelligence into a smaller package. This means developers can build low-latency translation tools that run entirely on-device. Try it now on @huggingface and @Kaggle ↓ https://t.co/M7wJmngSCk

Today we're releasing our first official LoRA: INFL8 Trained on the incredible @Alibaba_Qwen Image Edit 2511 model, it can inflate anything! Links to the model and @huggingface space below so you can try it out for yourself ⬇️ https://t.co/sT2E83vsXi

Finally! We (the community + @OpenAIDevs + @huggingface ) bring you an open standard for inference. It's called 'Open Responses' it's based on Responses and it's perfect for agent workloads. Fewer special cases, more consistency, faster shipping. Excited for what this unlocks. Below is a deep dive blog post, we’ll look at how Open Responses works and why the open source community should use Open Responses.

Crazy ! 🤯 I was sceptical so I had to check the dataset See for yourself, the truth is here: there are indeed white spaces indicating the true answer in some examples (thanks to the AI assistant on @huggingface for the SQL query) https://t.co/5nXLgHCn3f

TranslateGemma is out https://t.co/jgoHfqjiJH

TranslateGemma is out https://t.co/jgoHfqjiJH

Benchmark Leaderboards seen in the wild on @huggingface 👀🏆 https://t.co/nsHvwDCHwy

Benchmark Leaderboards seen in the wild on @huggingface 👀🏆 https://t.co/nsHvwDCHwy