Models & Labs
llama.cpp adds support for Gemma4ForCausalLM
llama.cpp Releaseshigh confidence
Why it matters
- →Expands llama.cpp's support to include Gemma4ForCausalLM, broadening its utility for developers.
- →Enhances performance and flexibility in AI applications using the Gemma4 architecture.
- →Addresses technical issues, improving the tool's usability and integration.
llama.cpp has released an update that includes support for the Gemma4ForCausalLM architecture. This development allows users to utilize the Gemma4 architecture for causal language modeling, broadening the tool's applicability. The update also addresses indentation issues, improving the overall user experience. This enhancement is part of llama.cpp's ongoing efforts to expand its support for various AI models.
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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
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llama.cpp b10175 Release Expands Platform Support
The latest b10175 release of llama.cpp continues its trend of broadening platform compatibility, making it a versatile tool for developers across different systems. Notably, this update includes support for ROCm 7.2 on Ubuntu x64, which is significant for AMD GPU users seeking alternatives to NVIDIA's CUDA. The release also maintains a wide array of builds for Windows, macOS, and Linux, ensuring that developers can leverage llama.cpp's capabilities regardless of their hardware setup. While there are no groundbreaking new features, the consistent expansion of platform support solidifies llama.cpp's position as a flexible inference runtime option.
