The article describes the implementation of the DeepSeek R1-zero style training for large language models (LLMs) using a single or multiple GPUs, with a focus on simplicity and efficiency. It highlights the capabilities of the nanoAhaMoment project, which includes full parameter tuning, multi-GPU support, and a full evaluation suite, while maintaining competitive performance with minimal complexity. The repository offers interactive Jupyter notebooks and scripts for training, complete with installation instructions and dependency management.

The paper presents BLIP3-o, a family of fully open unified multimodal models that enhance both image understanding and generation. It introduces a diffusion transformer for generating CLIP image features, advocates for a sequential pretraining strategy, and proposes a high-quality dataset, BLIP3o-60k, to improve performance across various benchmarks. The models, along with code and datasets, are open-sourced to foster further research.

HunyuanImage-3.0 has been released as an open-source image generation model, featuring a unified multimodal architecture that integrates text and image understanding. It boasts the largest Mixture of Experts model with 80 billion parameters, enabling superior image generation capabilities while supporting extensive customization through various checkpoints and performance optimizations.

DeerFlow is a community-driven deep research framework that integrates language models with specialized tools for web search, crawling, and Python code execution. It supports one-click deployment through Volcengine, features a modular multi-agent system for automated research tasks, and includes capabilities like text-to-speech and report generation. Users can explore its functionalities through a web UI and configure various search engines for tailored experiences.

RecML is a high-performance, open-source library designed for building and deploying large-scale deep learning recommender systems, optimized for Cloud TPUs and GPUs. It offers state-of-the-art model implementations, a user-friendly API, and flexible architecture to support massive datasets while addressing common challenges in recommendation tasks. Additionally, it emphasizes community collaboration and provides tools for efficient training, evaluation, and deployment.