Notable changes to vla.cpp. Format loosely follows Keep a Changelog.

• vla-cli: one-shot inference from the command line (image + tokens to action), no server needed.
• scripts/quantize_gguf.py: repack LM weights to Q8_0/Q4_0. The loader runs quantized GGUFs directly (Q8_0 roughly halves the LM, near-lossless).

• One shared GGUF reader across the model loaders, replacing the per-arch copies.
• Cap inbound message size (256 MiB) and image count (16) on vla-server and vlm-server.
• Scale CPU threads to the machine core count across all loaders instead of a fixed 4.
• Read GGUF file offsets as 64-bit and reject non-float embedding tensors in row-fetch.

• Reject out-of-range language tokens in OpenVLA-OFT and VLA-Adapter.
• Zero the padded action dimensions so only real action dims carry values.
• Reject images that do not match the model input size in VLA-Adapter and OpenVLA-OFT (out-of-bounds read on a smaller view).
• Validate Evo-1 action dims at load so a client-supplied noise buffer cannot underrun.
• Only enable the BitVLA CUDA path once every device buffer allocates.

First tagged release. One self-contained GGUF per model (vision tower + LM + action expert + dataset stats), CPU or CUDA, no external mmproj and no patch to llama.cpp.

• Seven VLA policies auto-detected from the GGUF: SmolVLA, pi0, BitVLA, Evo-1, GR00T N1.5/N1.6/N1.7.
• In-tree vision towers (SigLIP, BitSigLIP, InternViT, RADIO) on stable public ggml/llama APIs.
• ZeroMQ + protobuf vla-server; a separate vlm-server for VLM chat.
• BitVLA 1.58-bit custom ternary CUDA kernels.
• Per-arch HuggingFace -> GGUF converters and mmproj-merge helpers (scripts/).
• Robot eval harness for LIBERO, SimplerEnv, and ALOHA (eval/), with device benchmark reports.
• Minimal CI: pixel-shuffle unit test, converter-remap test, CPU build gate.
• pyproject.toml for the Python tooling and a CUDA Dockerfile for vla-server.

• llama.cpp is fetched + pinned via CMake FetchContent (tag b9866); bumping is a one-line GIT_TAG change. Removed the patches/ fetch script.