Models & Labs
MAI-Code-1-Flash Now Available for GitHub Copilot
GitHub Changeloghigh confidence
Why it matters
- →MAI-Code-1-Flash enhances coding efficiency with fast responses.
- →It supports high-volume, iterative coding workflows in enterprise settings.
- →The model's availability underlines GitHub's focus on customizable enterprise solutions.
Microsoft AI has released the MAI-Code-1-Flash model for GitHub Copilot Business and Enterprise users. This coding model is optimized for fast, low-latency responses, catering to high-volume and iterative coding workflows. It is available under usage-based billing, and administrators must enable it in Copilot settings. This release aims to enhance coding efficiency for enterprise users, aligning with GitHub's strategy to provide tailored solutions for business environments.
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llama.cpp b10955 release addresses heap corruption
The latest release of llama.cpp, b10955, tackles a critical issue of heap corruption by disabling the ggml-cpu precompiled header and fixing CACHE_LINE_SIZE ambiguity. This update ensures consistent CACHE_LINE_SIZE values across C++ kernels and C work-buffer sizing code, preventing heap-buffer-overflow and subsequent crashes. By restoring the natural include order and removing the std::hardware_destructive_interference_size branch, the update makes the value deterministic and include-order independent. This release is a technical fix that stabilizes the runtime environment for developers using llama.cpp.
llama.cpp Releases
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llama.cpp b10956 release enhances SYCL backend
The latest llama.cpp release, b10956, introduces significant improvements to the SYCL backend, particularly for handling large k values in TOP_K operations. By implementing a radix select method, the update allows for efficient GPU-resident processing, avoiding previous limitations that forced operations to fall back to the CPU. This change enhances performance, especially in scenarios requiring large k values, such as qwen4exp's sparse-attention indexer. The update ensures that operations are more efficient and scalable, providing a notable boost in processing speed without regressing any measured shapes.
