Models & Labs
Hugging Face Migrates Repositories to Xet Storage
Hugging Face Bloghigh confidence
Why it matters
- →Xet storage offers more efficient data handling through content-defined chunking.
- →The migration demonstrates Hugging Face's commitment to improving AI development infrastructure.
- →This shift could significantly enhance collaboration and iteration on large datasets.
Hugging Face has migrated its first model and dataset repositories from LFS to Xet storage, marking a significant advancement in storage efficiency. The new system uses content-defined chunking to deduplicate data at the byte level, allowing for faster and more efficient uploads. This migration has already shifted 6% of the Hub's download traffic to Xet, validating its capability to handle large-scale data transfers. The transition is part of Hugging Face's ongoing efforts to improve collaboration and iteration for AI developers working with massive datasets.
Read originalMore from Hugging Face Blog
More in Models & Labs
Models & Labsmodels
Llama.cpp adds GLM-5.2 speculative decoding support
Llama.cpp's latest update introduces speculative decoding support for GLM-5.2, enhancing its capabilities with NextN/MTP features. This addition allows for more efficient tensor loading and context management, particularly benefiting models using the GLM_DSA architecture. The update also includes options for exporting models with or without the MTP feature, providing flexibility for developers. This release marks a step forward in optimizing model performance and adaptability, especially for those leveraging the GLM-5.2 framework.
llama.cpp Releases
Models & Labsmodels
Llama.cpp b10178 Release Adds Trace Logging
The b10178 release of llama.cpp enhances its server capabilities by adding trace logging for slot similarity checking, offering developers detailed insights into prompt cache slot selection processes. This update includes specifics on skip reasons and similarity calculations, which can aid in performance optimization. While no new model architectures are introduced, the release continues to support a wide array of platforms, such as macOS with KleidiAI, Ubuntu with ROCm 7.2, and Windows with CUDA 12 and 13. This makes llama.cpp a more versatile tool for developers working on different systems, reinforcing its position as a comprehensive inference runtime.
