Models & Labs
llama.cpp adds MiniCPM5 tokenizer support
llama.cpp Releaseshigh confidence
Why it matters
- →Expands llama.cpp's compatibility with more AI models.
- →Enhances platform support, particularly for macOS and Windows.
- →Strengthens its role as a versatile tool for AI developers.
Llama.cpp has released an update adding support for the MiniCPM5 tokenizer. This enhancement includes a new pre-tokenizer hash and hardcoded regex handling, consistent with other BPE pre-tokenizers. The update also broadens platform compatibility, particularly for macOS Apple Silicon and Windows systems. This development solidifies llama.cpp's utility as a flexible tool for developers working with various AI models.
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Llama.cpp b9329 Release Enhances CUDA Performance
The b9329 release of llama.cpp brings a notable performance enhancement with the integration of a fast Walsh-Hadamard transform for CUDA, which is set to improve computational efficiency. This update also includes optimizations such as unrolling and changes from size_t to int, aimed at boosting processing speed. The release is compatible with platforms like macOS, Linux, Windows, and openEuler, ensuring developers can leverage these improvements across different environments. While there are no new models introduced, the emphasis on performance optimization makes this update significant for those working with CUDA and other supported systems.
llama.cpp Releases
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llama.cpp b9330 release improves model performance
The b9330 release of llama.cpp resolves a key issue by correctly tagging the ffn_latent operation as MUL_MAT, aligning it with the backend's operational expectations. This correction ensures that weights and their matrix multiplications remain on the GPU, avoiding unnecessary CPU fallback and graph splitting. As a result, performance on the Nemotron 3 Super 120B Q5_K_M model has significantly improved, with throughput increasing from 64.9 to 103.22 tokens per second. This update reflects llama.cpp's dedication to enhancing AI model performance across different computing environments, including macOS with KleidiAI and Ubuntu with ROCm 7.2. By maintaining efficient GPU processing, llama.cpp continues to optimize AI model execution, ensuring robust performance on platforms like CUDA 12 and CUDA 13.
