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llama.cpp updates LFM2 support; new TCAPI paper targets tensor network portability

By PulseAugur Editorial · [2 sources] ·

The llama.cpp project has released version b11767, which includes updates for handling LFM2 tiling parameters from GGUF metadata. This release also features improvements for compatibility across various operating systems including macOS, iOS, Linux, Android, and Windows, with specific optimizations for openEuler. Separately, a new paper introduces the Tensor Computing Application Programming Interface (TCAPI), a lightweight API designed for portable, high-performance tensor network computations. TCAPI aims to abstract tensor objects and provide a consistent set of core functions for C++ and Python, enabling framework-independent applications that can run efficiently on heterogeneous hardware. AI

IMPACT Improvements in tensor computation APIs and efficient model inference tools can accelerate AI research and deployment.

RANK_REASON The cluster contains a software release for a specific project (llama.cpp) and a research paper detailing a new API for tensor computations.

Read on llama.cpp — Releases →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

llama.cpp updates LFM2 support; new TCAPI paper targets tensor network portability

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The cluster contains a software release for a specific project (llama.cpp) and a research paper detailing a new API for tensor computations.
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COVERAGE [2]

  1. llama.cpp — Releases TIER_1 (SO) · github-actions[bot] ·

    b11767

    <details open=""> <p>mtmd : read LFM2 tiling params from GGUF metadata (<a class="issue-link js-issue-link" href="https://github.com/ggml-org/llama.cpp/pull/30840">#30840</a>)</p> <ul> <li>mtmd : read LFM2 tiling params from GGUF metadata</li> </ul> <p>Write min_tiles, max_tiles,…

  2. arXiv cs.LG TIER_1 English(EN) · Rong-Yang Sun, Tomonori Shirakawa, Hidehiko Kohshiro, D. N. Sheng, Seiji Yunoki ·

    Tensor computing application programming interface: An application-oriented, lightweight API for portable high-performance tensor network computations

    arXiv:2512.23917v2 Announce Type: replace-cross Abstract: Tensor networks (TNs) are a central computational tool in quantum science and artificial intelligence. However, the lack of a unified software interface across tensor-computing frameworks severely limits the portability of…

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