Month: 2026-01 — Concise monthly summary for CodeLinaro/onnxruntime focusing on delivering a streamlined model optimization workflow and setting the stage for Olive-based improvements.

In Oct 2025, shipped major quantization and deployment enhancements across the Olive family, with new documentation, API usability improvements, GPU acceleration, and robust export/compatibility fixes. Deliveries span microsoft/Olive, microsoft/olive-recipes, and CodeLinaro/onnxruntime, enabling end-to-end quantization workflows for multiple models and more reliable model export across platforms.

September 2025 monthly summary focused on performance, stability, and deployment enablement across Olive and Olive Recipes. Delivered faster inference paths, refined precision controls, and updated QNN/AOT compatibility. Strengthened CI/docs hygiene and versioning for easier maintenance and faster onboarding of new models.

August 2025 — Microsoft Olive: Delivered quantization and ONNX/export improvements, hardened ONNX Runtime EP handling, advanced GenAI integration updates, and strategic codebase cleanup. These changes improve production performance, reliability, and cross-platform compatibility, enabling faster deployment cycles and more maintainable code.

July 2025 performance summary for Microsoft Olive and microsoft/onnxruntime-genai: Delivered quantization and runtime improvements with a focus on model efficiency, reliability, and developer productivity. Implemented GPTQ and quantization core enhancements with native components (QuantLinear, HfQuantizer, GPTQ Pass), improved precision/export paths (including SelectiveMP qkv) and per-channel quantization (desc_act). Strengthened CI stability and dependency compatibility, removed flaky tests, and improved GPU CI reliability. Cleaned up runtime integrations (Ort/Onnx) and model optimizer, reducing surface area and potential breakages. Expanded test coverage by updating UT datasets, and improved documentation with pipeline status visibility. Also delivered targeted 8-bit support and ONNX/HF config enhancements for broader model IO compatibility, BF16 support in ModelBuilder, and HF authentication workflow improvements. Repositories involved: olive and onnxruntime-genai.

June 2025 (2025-06) monthly summary for microsoft/Olive. The team delivered a set of targeted, high-impact improvements across dependency management, runtime standardization, and quantization readiness, while stabilizing CI and hardening common failure paths. Key changes include unpinning torch/transformers pins across documentation and requirements to enable installation of latest compatible versions, with updated Phi3.5 notes to reflect known-compatible configurations; introducing the SelectiveMixedPrecision pass in Olive with two initial algorithms to support downstream quantization (k_quant_mixed and k_quant_last) along with docs and unit tests; standardizing ONNX Runtime usage by removing variant-specific logic and addressing value-info handling to prevent model composition issues; stabilizing CI by re-enabling ort nightly in nightly Linux CPU example tests; and fixing a tokenizer save robustness issue by broadening exception handling to catch OSError and TypeError. These changes collectively reduce install conflicts, increase test reliability, accelerate downstream optimization, and simplify runtime maintenance.

Concise monthly summary for Microsoft Olive (May 2025): Delivered ONNX graph handling improvements with a refactor into OnnxDAG, enhanced graph traversal and correctness during model loading, plus precision fixes that preserve original model accuracy. Reorganized QNN optimization examples across models (BERT, CLIP, ResNet, ViT) with dedicated subdirectories and updated READMEs for clarity. Improved documentation and CLI usability with overrides, activation precision options, Windows notes, and fixed invalid links. CI pipeline streamlined for Windows compatibility and CPU test paths, with unnecessary steps removed and tests adjusted for unavailable dependencies. Code quality improvements addressing lint issues to raise maintainability across Olive. Business value includes more reliable model loading, clearer experimentation guidance, faster CI feedback loops, and a more maintainable codebase.

April 2025 (Month: 2025-04) highlights robust model handling, ONNX optimization, and broader QNN support in Olive. Delivered a graph surgery to replace MatMul followed by Add with a single Gemm operation, improving ONNX inference performance and compatibility across ND input shapes. Strengthened Olive's QNN integration with model/config JSON support and expanded coverage to BERT, CLIP, and transformer-compatible optimizers for QLoRA/LoRA. Fixed critical model handling issues in HfModelHandler to prevent runtime errors when dummy_inputs are single tensors and ensured reliable serialization by clearing raw_data after external data renaming. Corrected documentation path to the custom-model-evaluator to improve user access. Collectively these work items increase reliability, performance, and model coverage, delivering business value through faster inferences, easier experimentation, and clearer documentation.

March 2025 performance summary focused on quantization tooling, ONNX/ORT integration, GenAI readiness, and CI stability across intel/onnxruntime and Olive. Key outcomes include a more configurable quantization flow, improved ONNX quantization alignment with ORT, robust CI/test reliability, and expanded GenAI data handling and documentation maintenance. These efforts enhance production readiness, reproducibility, and the ability to scale GenAI/LLM workloads across hardware backends.

February 2025: Delivered substantive improvements across ONNX Runtime projects (intel/onnxruntime, microsoft/onnxruntime-genai) and Olive, focusing on model robustness, performance, and deployment reliability. Key outcomes include enhanced shape inference, configurable quantization, per-channel quantization support, and stability fixes across CI and data preprocessing, enabling more accurate, scalable inference for large models and GenAI workflows.

January 2025 performance summary for microsoft/Olive: Key features delivered focusing on robustness, data handling efficiency, and quantization quality, alongside improvements in model access and CLI flexibility. Delivered foundational enhancements enabling better deployment reliability and reduced compute requirements.

December 2024 monthly summary for Microsoft Olive: Delivered feature-driven improvements to model splitting and cost modeling, along with CI configuration cleanup. Key outcomes include preserving QDQ nodes during model splitting to maintain quantization integrity; integrating FLOPs into the memory-aware cost model and updating CSV headers to differentiate memory-intensive vs compute-intensive modules; and cleaning CI configuration by removing the ORT-stable pipeline YAML and status badge. No separate bug-fix items were recorded this month; the focus was on feature enhancements with clear business value: more accurate deployment cost estimates, better quantization fidelity, and streamlined CI maintenance. Technologies demonstrated include model transformation logic, quantization-aware splitting, FLOPs-aware cost modeling, CSV metadata handling, and CI configuration management.

November 2024 performance summary for the Microsoft Olive repository, focusing on delivering scalable model deployment capabilities, improving reliability across CI/CD, and hardening the quantization and ONNX pipelines. The team delivered functional model splitting with cost-model guidance and accelerator memory awareness, updated Llama2 tests to use open models for credentials-free CI, and expanded CI coverage with .NET tooling and versioning. Core quantization and ONNX/Model saving robustness improvements reduced production risk and improved model readiness for deployment.

Monthly work summary for 2024-10 focusing on features delivered in microsoft/Olive. Key outcomes include security-conscious CI/CD workflow improvements for fork builds and quantized-module handling enhancements that improve stability and observability in production-like scenarios.