Turning Database Grading into RL Rewards: Post-Training Agents with ThinkingBox
AI summary ThinkingBox-Training uses database state checks as rewards for post-training agents, improving success rates.
Latest news and updates in one fast feed
The latest from Hugging Face Blog, part of our AI & ML coverage. Updated automatically as new posts are published.
AI summary ThinkingBox-Training uses database state checks as rewards for post-training agents, improving success rates.
AI summary Hugging Face released OlmoEarth v1.3, a satellite embedding model with precomputed global data and a browser tool for exploration.
AI summary IBM Granite time-series models use an ensemble approach for forecasting, combining four architectures for better accuracy and flexibility.
A Blog post by NVIDIA on Hugging Face
AI summary Specializing model vocabulary can reduce inference costs by 77% without changing the base model, as shown in a production essay grader.
AI summary This article explains system-level profiling on AWS Trainium using TorchNeuron, focusing on analyzing inference performance and using tools like Neuron Explorer.
AI summary This article explains system-level profiling on AWS Trainium using TorchNeuron, focusing on analyzing inference performance and using tools like Neuron Explorer.
AI summary Hugging Face announces updates to IBM's Granite Speech collection, including faster and more accurate models for transcription and translation.
AI summary NVIDIA Kumo Forecast combines a pretrained Transformer with historical data retrieval for time-series forecasting and interpretability.
AI summary Hugging Face launches SimReady Central, a library of 2,000 simulation-ready 3D assets for robotics and AI factories.
A Blog post by NVIDIA on Hugging Face
A Blog post by Ai2 on Hugging Face
A Blog post by IBM Research on Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
AI summary TorchNeuron provides a new PyTorch backend for AWS Trainium, making it easier to run and share models on the Hugging Face hub.
AI summary Hugging Face introduces a 2.8 trillion token pretraining corpus for 22 Indian languages, along with an open data pipeline.
AI summary Granite Switch 4.1 allows efficient switching between chat, decisions, and speech tasks from one endpoint with fast performance.
A Blog post by Swiss AI Initiative on Hugging Face
AI summary Muse Spark Open is a large multimodal model from Meta with 816 billion parameters, supporting text, images, audio, and video inputs.
AI summary FLUX.3 Image Klein is now available in Diffusers, offering text-to-image, image-to-image, and layout control features.
AI summary HiRO-ACEv2 simulates extreme weather at 3 km resolution with high accuracy and lower cost than existing models.
AI summary LFM2.5-24B-A2B is a 24B-parameter model with 2B active parameters, designed for fast, private on-device agents.
AI summary Ai2 improved GPU scheduling by replacing a priority system with a budget-based approach, increasing transparency and fairness in resource allocation.
AI summary A user created several models using ML Intern, resulting in smaller, efficient versions of existing models.
AI summary Hugging Face released two open decision models, d1-3B and d1-omni-600M, built on Liquid Foundation Models for edge and GPU inference.
A Blog post by Technology Innovation Institute on Hugging Face
AI summary Nemotron was fine-tuned to achieve gold-level results in IOI and IMO competitions, showing its adaptability.