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CrossPascal Early Access 019

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@pisarev pisarev released this 11 Nov 14:56
early-access-019
0567f79

CrossPascal runs local language and vision models from Delphi and Lazarus through TVM on Windows x64. EA019 adds ready NVIDIA CUDA bundles alongside the CPU bundles. Model conversion and inference both run without Python.

Run your first example

Start with VisionModels to try object detection and image classification on the supplied photo. Choose QwenChat or GemmaChat if you want to ask a question about an image.

Prepare ZIP CPU download CUDA download Free VRAM for CUDA
VisionModels 800 MiB 800 MiB 1,024 MiB
QwenChat · Qwen2.5-VL 3B 5.4 GiB 4.1 GiB 5,120 MiB
GemmaChat · Gemma 3n E4B 11.5 GiB 8.6 GiB 5,500 MiB
GenerateText · Gemma 2 2B 3.8 GiB 3.1 GiB 5,500 MiB

The table shows approximate first-run downloads, including the SDK and model files. Leave room for the extracted files too. On a hard drive, unpacking and verifying the files can take longer than downloading them.

  1. Extract the ZIP and run Prepare.cmd. It checks your PC, prints the selected package and shows the download size. Public downloads need no GitHub sign-in or token.
  2. Open the prepared .dproj in Delphi 13.2 or .lpi in Lazarus 4.8. Select the Windows 64-bit target and run the project.
  3. In VisionModels, choose a profile and click Run inference. In either chat, click Load model, attach a photo if you like, then click Generate.

The kit for all 13 examples contains the setup scripts. Running all 13 with an empty cache downloads about 21.0 GiB in CPU mode, or 16.2 GiB when CUDA is selected for all supported examples. The preparation guide lists RAM, disk space and installation paths.

CPU and CUDA

Prepare selects CUDA when the driver, compute capability and free GPU memory meet the requirements. Otherwise it selects CPU and explains why. To choose explicitly, run Prepare.cmd -Device CPU or Prepare.cmd -Device CUDA.

CUDA inference needs a Windows NVIDIA driver that supports CUDA 12.8. Build checks cover compute capabilities 6.1, 7.5, 8.6, 8.9 and 12.0. The language and vision examples ran on an RTX 4060 Ti (8.9). All five vision profiles also ran on a GTX 1650 (7.5). For the language models, start with an 8 GiB card and check the free VRAM in the table.

VisionModels includes YOLO11n detection and classification, RT-DETR R18vd, SqueezeNet 1.1 and ResNet50 v1. Qwen2.5-VL and Gemma 3n accept text and images. Gemma 2 is an experimental text-only profile for short completions. Browse the model catalog and known limitations for details on each profile.

SDK, source and model terms

  • The Windows x64 SDK includes the native converter and all 13 examples. The converter guide lists supported checkpoints and build tools. Use CUDA Toolkit 12.8 to build CUDA bundles. The prepared examples use the supplied runtime and the NVIDIA driver.
  • Browse the Pascal examples or download the source archive, which records the five repository revisions.
  • Check the model and library terms before using or redistributing bundles. The Gemma bundles use Gemma Terms, Qwen2.5-VL 3B uses the Qwen Research License for non-commercial research and evaluation, and YOLO11n uses AGPL-3.0.

If an example fails, open an issue with the example name, CPU or CUDA mode, and the message printed by Prepare or the app. For CUDA, include your GPU model and driver version.

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