v0.4.9

Changelog

• Updated the llama.cpp base from c06f84160 to d327bf1f0 and ggml to 0.27.0. Inherited changes include Flint loading/conversion and Jinja hexadecimal escapes, Kolibri-1, SenseNova U1, Nemotron embedding/vision updates, Granite Switch and H2O-Lightning decision models, Soprano TTS and Voxtral realtime audio; multi-GPU MoE caching, LiLiCorr and probabilistic DFlash2 drafting, speculative logprobs, live draft state in disk slot saves, chat-session APIs, server TTS/realtime transcription, and backend, conversion, and Web UI updates. Bee's RPC protocol is now 9.0.0 because the upstream TOP_K_MASK insertion shifts fork operation numbers; matching Bee RPC builds are required.
• Moved structured KVarN mid-prompt checkpoints to the nonzero descriptor-group boundary at or before each user-message start (PR #186). Retained seam anchors sit less than one group before their user boundary; checkpoint spacing and per-slot FIFO limits still apply, so this is not a bound on total follow-up replay.
• Extended Bee's transactional slot restore to disk files, preparing target, owned draft, speculative continuation, and prompt/checkpoint metadata before publication. Malformed or incompatible appendices leave the live slot unchanged, shared draft KV is never restored independently, and legacy target-only files with an owned draft require full prompt reevaluation. Added llama_state_seq_prepare_file for file-backed restore preparation.
• Fixed historical prompt-cache selection and accounting. MTP checkpoints stay within the restored continuation boundary, RAM candidates without a usable checkpoint must fit recurrent rollback limits, and cached/planned token counts and cumulative cache metrics follow the actual committed boundary after descriptor-aligned rollback. Ordinary body-backed historical truncation and explicit reduced-tail coverage remain unchanged.
• Fixed Qwen4Exp QSA precision-tail masking. Sparse selection now applies to both cache-body and exact-tail attention, while the ordinary mask remains available when needed for tail planning. Added a numerical graph regression.

macOS:

• macOS Apple Silicon

Linux:

• Ubuntu x64 CPU
• Ubuntu arm64 CPU
• Ubuntu x64 CUDA 12.4
• Ubuntu x64 CUDA 13.3
• Ubuntu x64 Vulkan
• Ubuntu x64 ROCm 7.2
• Ubuntu x64 SYCL

Windows:

• Windows x64 CPU
• Windows x64 Vulkan
• Windows x64 SYCL
• Windows x64 CUDA 12.4 - DLLs
• Windows x64 CUDA 13.3 - DLLs
• Windows x64 HIP

Docker:

• CPU: docker pull ghcr.io/anbeeld/beellama.cpp:server-cpu-v0.4.9
• CUDA: docker pull ghcr.io/anbeeld/beellama.cpp:server-cuda-v0.4.9
• CUDA 12: docker pull ghcr.io/anbeeld/beellama.cpp:server-cuda12-v0.4.9
• CUDA 13.3: docker pull ghcr.io/anbeeld/beellama.cpp:server-cuda13-v0.4.9
• ROCm: docker pull ghcr.io/anbeeld/beellama.cpp:server-rocm-v0.4.9
• Vulkan: docker pull ghcr.io/anbeeld/beellama.cpp:server-vulkan-v0.4.9
• SYCL: docker pull ghcr.io/anbeeld/beellama.cpp:server-sycl-v0.4.9

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