CrossPascal Early Access 013

CrossPascal Early Access 013 adds small language-model examples and a shared chat interface for Delphi 13.2 and Lazarus 4.8 on Windows x64.

What is new

• Six console examples: inspect/load/unload, complete text answers, generation settings, streaming and Stop, repeated requests, and image prompts.
• QwenChat and GemmaChat expose the same model lifecycle and generation settings. Both include an image selector, thumbnail and removal button. Text-only bundles explain the limitation and reject image inference before loading.
• Compatible Gemma 3n CUDA text bundles can receive a separately converted gemma3nv vision projector through the local converter. The new independent bundle supports real text-plus-image inference through TVM.
• Automatic generation remains the default (0). Stop, manual limits, sampling, temperature, Top P, Top K, repetition penalty, seed, answer budget, instructions and plain completion are available. Automatic output is still bounded by the model's actual context.
• Qwen CUDA uses non-pooled temporary VM storage to avoid the observed long prefill-to-decode stalls; model weights still remain loaded for repeated requests.
• Console output preserves UTF-8 in Delphi and Lazarus, including redirected output.

Start here

1. Download and extract crosspascal-windows-x64-ea013.zip.
2. Extract crosspascal-gemma-runtime-windows-x64-ea013.zip into the same parent folder. It adds the Qwen native DLLs and the portable Gemma/Pillow runtime to the SDK.
3. Open crosspascal/examples/qwen-chat/README.md, crosspascal/examples/gemma-chat/README.md, or crosspascal/examples/language-models/README.md inside the SDK.
4. Select a completed model bundle containing model.json, then the installed SDK root. Original weights are inputs to the converter, not inputs to these demos.

Our distributed executables are built with Lazarus 4.8. Sources were also built and exercised with Delphi 13.2. Upstream native/Python dependencies retain their own provenance and licenses. Completed-bundle inference does not need the conversion toolchain. Conversion requires the separately documented TVM/exporter/compiler dependencies.

Scope and limits

Dense Qwen2/Qwen2.5 is a text route. Qwen2.5-VL is not implemented by that adapter. Gemma 2 CPU retains its experimental numerical restrictions. Gemma 3n CPU/CUDA text profiles remain available; images currently require the compatible CUDA vision profile. Dispatch uses architecture metadata rather than a list of model or graphics-card names.

The checked vision profile resizes to 768 pixels and contributes 256 image tokens. Its embeddings were compared with PyTorch on the same weights, and changing the image changed the colour answer correctly. This bounded acceptance is not a claim of general image-understanding accuracy. Image tokens are read sequentially by the text decoder, so image questions take longer to start answering than short text questions. Audio and video are not implemented.

Initial Qwen startup is still more variable than a request using resident weights. In the final fresh-archive check, the 1.5B hello run took about 68 seconds overall; retained-session short replies in the scoped allocator test took about 2 seconds. These are measurements on the checked system, not a latency guarantee. Loading and generation status remain visible.

Qwen and Gemma weights are not included. Obtain them under their original access and license conditions. Input images remain local. The unchanged OpenCV, Leptonica and CPU-model source assets retain their existing installation instructions and licenses.

All 81 owned Pascal source files passed the current canonical formatter check. Generated and third-party source was preserved. Existing releases remain available. This is a private prerelease.

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.