github

<details open> mtmd: support Qwen3-TTS (note: breaking change to llama-tts binary) (#26254) * convert text model * main model load ok * convert encoder ok * speaker encoder loading ok * speaker enc graph * adapt vocab for backbone (with some tricks) * add suppress_tokens

<details open> models : fix dflash wo_a reshape on load (#26577) </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10269/llama-b10269-bin-macos-arm64.tar.gz) - macOS Apple Sili

<details open> ci: fix pre-built binaries no longer working on macOS 15 and below (#26375) * ci: fix pre-built binaries no longer working on macOS 15 and below * ci: add macOS deployment target to disabled KleidiAI build </details> **Website:** - <https://llama.app> **macOS

<details open> speculative : refactor enabled configs common_speculative_init (#26510) This commit contains a suggestion to reduce some code duplication in common_speculative_init when adding the enabled speculative decoding configurations. No tests were added but the existing

<details open> sync : ggml </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10265/llama-b10265-bin-macos-arm64.tar.gz) - macOS Apple Silicon (arm64, KleidiAI enabled) [DISABLE

<details open> sycl: parallelize the non-contiguous concat kernel (#25852) * sycl: parallelize the non-contiguous concat kernel Launch geometry only: the non-contiguous concat kernel launched a single-lane work-group (1, 1, 1), now it will launch a (1, 1, SYCL_CONCAT_BLOCK_SIZ

<details open> Extended SYCL oneDNN SDPA to non-FP16 KV caches (Q4_0–Q8_0 and FP32) (#25874) * sycl: extend oneDNN SDPA to Q4_0-Q8_0 and F32 KV caches Extends the oneDNN SDPA path (PR #25222) to handle non-F16 KV caches by dequantizing or converting K/V to dense FP16 on-device

<details open> chat : add new template for DeepSeek V4 Flash 0731 (#26398) * common/chat: update DeepSeek V4 templates Align the DeepSeek V4 templates with the official encoders while keeping parser behavior out of this change. - Default drop_thinking for DeepSeek V4 history

<details open> vendor : update cpp-httplib to 0.52.0 (#26485) </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10253/llama-b10253-bin-macos-arm64.tar.gz) - macOS Apple Silicon

<details open> vendor : update BoringSSL to 0.20260803.0 (#26523) </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10252/llama-b10252-bin-macos-arm64.tar.gz) - macOS Apple Sil

<details open> model : support MTP in GLM-4.7-Flash (#24868) </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10251/llama-b10251-bin-macos-arm64.tar.gz) - macOS Apple Silicon

<details open> tests: add model resolution test on synthetic repo listings (#26172) * tests: add model resolution test on synthetic repo listings Include download.cpp and arg.cpp inside a namespace with hf_cache monkey patched to serve hardcoded listings, so the resolution and

<details open> server: add get_info tool (#26522) * server: add get_info tool * fix --rpc in docs * server: harden get_info probe result handling Report the OS as unknown when the probe process fails to spawn or times out, so the diagnostic text from run() is never returned

<details open> vocab : validate default special token ids (#26506) </details> **Website:** - <https://llama.app> **macOS/iOS:** - [macOS Apple Silicon (arm64)](https://github.com/ggml-org/llama.cpp/releases/download/b10248/llama-b10248-bin-macos-arm64.tar.gz) - macOS Apple Si

<details open> ggml: use dynamic allocation for split graph inputs (#22789) * ggml: use dynamic allocation for split graph inputs Replace fixed-size GGML_SCHED_MAX_SPLIT_INPUTS arrays with dynamically allocated buffers in the backend scheduler. This fixes crashes when loading