CrossPascal Early Access 016

Prepare and run all 13 supplied Pascal examples on Windows x64.

Download crosspascal-examples-prepare-ea016.zip for the complete set of preparation scripts, or choose the small Prepare-<Project>-ea016.zip for one example. Extract the ZIP to a writable folder and run Prepare.cmd. After READY, open the prepared .dproj in Delphi 13.2 or .lpi in Lazarus 4.8, select Windows 64-bit / Release, and Run.

Preparation downloads and verifies the SDK, portable preparation runtime, matching native libraries, CPU models and sample input. It sets the model, library and image paths automatically. You do not need to install Python, copy DLLs manually or convert a CPU model. Interrupted downloads resume; repeated preparation preserves editable source and existing settings. The complete guide is in crosspascal/docs/PREPARE-EXAMPLES.md in the SDK and in the source repository.

This release and its source repositories remain private. Use an account with access to pisarev/crosspascal. Preparation can use your GitHub CLI login or a fine-grained token with repository Contents: Read-only access. Token entry is hidden; saving it in Windows Credential Manager is optional. No credential is embedded in the scripts.

QwenChat and ImagePrompt use the accepted Qwen2.5-VL 3B CPU bundle. GemmaChat uses Gemma 3n E4B CPU with image input. The other five language examples use the experimental Gemma 2 2B CPU profile. Allow about 24 GiB, 32 GiB and 16 GiB of system RAM respectively, including Windows and application headroom. The corresponding model downloads are about 5.2 GiB, 11.4 GiB and 3.6 GiB; dependencies and extracted files need additional disk space. The Qwen-VL and Gemma 3n CPU bundles require AVX2 and FMA (Haswell-compatible or newer). CPU image inference can take minutes. Model bytes and their documented EA015 inference scopes are retained.

VisionModels prepares five CPU profiles: SqueezeNet 1.1, ResNet50, YOLO11n classification, YOLO11n detection and RT-DETR R18vd. MarkerLite opens the sample image. CrossCVHello and LeptonicaHello check their native libraries; LoadResizeSave writes a 640 × 480 PNG to the SDK results folder. The six language console projects run from their prepared settings without IDE arguments. Streaming examples retain automatic output and cooperative Stop.

The clean installation and project Run checks cover all 13 examples in Delphi 13.2 and Lazarus 4.8 / FPC 3.2.2. Distributed Pascal executables are built only with Lazarus. Native C/C++ dependencies include corresponding sources and notices. The five portable CPU vision bundles were exercised with both Pascal compilers. These checks establish the stated installation and example behavior, with the existing model-quality limitations retained.

Gemma remains subject to its supplied Terms of Use and Prohibited Use Policy. Qwen2.5-VL 3B uses its supplied Qwen Research License for non-commercial research and evaluation; commercial use requires a separate license from the licensor. Built with Qwen. Model weights do not inherit the CrossPascal code license. Keep the bundled terms and notices.

GPU conversion remains available through CrossPascalConvert: detect the GPU architecture or choose the destination architecture manually, then use a bundle built for that architecture. CPU preparation does not install a CUDA toolchain. Other platforms and untested model profiles are outside this release's verification matrix.

Documentation update - 9 November 2026

The accompanying README files, setup guides and preparation helper use the public-download workflow. SHA-256 inventories were refreshed for these revised packages. Model weights and inference binaries are unchanged. Existing Git tags retain the original code baseline; current setup guidance is maintained in the default branches.

Packaging maintenance, 10 November 2026

Source documentation and integrity inventories were refreshed. Runtime packaging changes affect documentation and integrity records. Model weights and the executable files in these historical packages are unchanged. The original Git tag and the scope of the model results are retained.