Ant Group Releases Open Source Code Model CodeFuse-CodeLlama-34B in 4bits Quantized Version


Ant Group open-sourced Avernet, a multi-agent collaboration infrastructure. The community edition focuses on agent discovery, consensus, cross-team collaboration, and governance. While individual agent capabilities advance rapidly, system integration lags, posing the challenge of efficiently aggregating agent capabilities scattered across teams and systems.....
Ant Group's Robbyant opensources the LingBot-Vision model family, which achieves outstanding performance in dense space perception tasks through self-supervised vision Transformers and innovative boundary modeling. It surpasses large models with several times more parameters in multiple metrics, breaking the limitations of existing visual foundation models that focus heavily on object recognition, making precise perception of physical space by robots a reality.
Artificial intelligence is driving a transformation in business logic from manual user operations to intelligent agent execution. Intelligent agents have become the new entry points for search, decision-making, and shopping, but convenience and security in the payment process remain critical challenges. To address this, Ant Group has launched the Mobile Intelligent Agent Protocol (AMP) to build a trusted and efficient payment infrastructure for this emerging business model.
Ant Group first showcased its 'Data+AI' as the core, demonstrating a full-stack layout from underlying technology to industrial applications at the 9th Digital China Construction Summit, marking a new stage in its data strategy - the 'Intelligent and Trustworthy Data Flow'. By integrating large models into daily scenarios, AI tools are implemented in practice. The medical AI application 'Ant A Fu' has served over 100 million users, and has collaborated with the Fuzhou Health Commission.
Ant Group won the championship in the "Robustness Sample Testing in Complex Real-World Scenarios" and "Facial Enhancement Anomaly Detection" tracks at the CVPR 2026 NTIRE Challenge. This achievement helps enhance risk identification capabilities in scenarios such as payment, content review, and financial authentication. In response to the increasing challenges of deepfakes and misuse of AIGC, as well as the insufficiency of detection models in real-world scenarios and multi-modal large model iterations, this breakthrough provides important technical support.