The b10103 release of llama.cpp has been announced, featuring expanded platform support. Notably, ROCm 7.2 is now supported on Ubuntu x64, providing better performance for AMD GPU users. The update also includes Vulkan support for various platforms, enhancing the software's versatility. Although no new model architectures are introduced, the release focuses on broadening hardware compatibility, making llama.cpp more accessible to developers.
Read originalThe b10093 release of llama.cpp focuses on refining the DeepSeek4 template to ensure it behaves consistently with reference standards. This update introduces support for the DeepSeekv4 flag and integrates the DS3.2 parser for DS4, enhancing its functionality. Developers working on macOS, Linux, and Windows can benefit from improved performance, especially with Vulkan, ROCm, and CUDA technologies. The release also addresses tool result reordering and post-merge fixes, contributing to a more stable and reliable development environment. While not revolutionary, these enhancements make llama.cpp a more dependable choice for developers seeking robust AI model support.
The latest llama.cpp release, version b10094, refines how speculative type inference is handled for draft repositories. By automatically identifying sidecars and prioritizing them according to a set hierarchy, users can now resolve drafts more seamlessly without additional flags. This update streamlines the workflow for developers dealing with intricate model setups, ensuring the correct draft is chosen more effectively. The release continues to support multiple platforms, including macOS, Linux, Windows, and openEuler, making it a flexible tool for developers working in different environments.
Grabette is a new open-source system designed to simplify the collection of robot manipulation data. By using a handheld gripper equipped with cameras, it allows users to record tasks without needing a robot or lab setup. This democratizes data collection, enabling anyone to contribute to a large, collaborative dataset. The system is built on standard, easily accessible components, making it accessible for widespread use. This release aims to address the data bottleneck in robot learning by encouraging community participation in building diverse datasets.