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v1.12.0

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@github-actions github-actions released this 07 Nov 22:51
· 61 commits to main since this release
v1.12.0
f9d596d

v1.12.0 (2026-11-07)

Bug Fixes

  • Harden ownership, downloads and input validation (e6211e8)

  • autotune: Keep the embedding-wide purge from wiping other tasks (c298292)

  • autotune: Select without infinite sentinels under fast-math (d86e494)

  • build: Link implementation deps for Windows examples and benchmarks (1c62375)

  • build: Link the Python shared library on Windows, export macOS ggml backends (f60814d)

  • python: Honour LIBEMBEDDING_LIBRARY when loading the native library (88420d4)

  • python: Load one copy of each native dependency, warn on a stale library (cbeb176)

  • sparse: Bound batches by a token budget and sort by length (d7bd2c2)

  • sparse-gguf: Count the batches actually run in stats (6fda145)

Build System

  • Upgrade llama.cpp to v0.6.0 (ggml 0.26.0) (6247000)

  • cmake: Install an importable libembedding CMake package (6287feb)

  • onnxruntime: Upgrade to 1.31.0 (70a994f)

Chores

  • Add CLAUDE.md agent guide and GitHub funding link (5c91c40)

  • Repeat quantization benchmark runs, note build hygiene rules (740f371)

  • release: Sync version stamps to 1.12.0 [skip ci] (06f113c)

Continuous Integration

  • Build and test macOS arm64 on macos-15 (97fe645)

  • Run the PR checks on pull requests to dev (d26758e)

  • release: Build test_autotune_core before running ctest (dd845a8)

Documentation

  • Archive LE-9.4, LE-9.6 and LE-P5 sparse reports (e1cc664)

  • Archive LE-P5 llama.cpp v0.6.0 audit report (1d2ef88)

  • autotune: Document lembed_autotune_ex and the unified tuning engine (c2c4bdb)

  • sparse-gguf: Document token budget, workers and profiling tools (4473464)

Features

  • autotune: Run every tuner on one engine and add lembed_autotune_ex (e263a90)

  • cpu: Add CPU dispatch for ggml backends (f4a206a)

  • python: Expose objective, workload and constraints in autotune_unified (15efc6b)

  • sparse-gguf: Run batches on parallel workers sharing the weights (748620b)

Performance Improvements

  • dense: Cap padded tokens per ONNX batch at 512 (c90a975)

  • reranker: Length-sorted, token-budgeted batches; never pad jina v1 turbo (e03cc6f)

Testing

  • benchmarks: Add a before/after harness for the auto-tuners (12f9523)

  • benchmarks: Add the short and long text corpora used by sparse runs (7620dd5)

  • benchmarks: Compare Q8_0, F16 and F32 weights in the GEMM microbench (bd067d6)

  • sparse: ONNX vs GGUF fidelity tests and SciFact benchmark (76fd214)

  • sparse: Report effective parallelism and where weight deltas sit (4640be7)

  • sparse-gguf: Per-text latency mode in the profiler (107ec2b)

Detailed Changes: v1.11.0...v1.12.0

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