Co-authored-by: David Koski <46639364+davidkoski@users.noreply.github.com> Co-authored-by: Christopher Webb <207731778+thechriswebb@users.noreply.github.com>
FastVLM
Demonstrates the performance of FastVLM models for on-device, visual question answering.
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Features
- FastVLM runs on iOS (18.2+) and macOS (15.2+).
- View Time-To-First-Token (TTFT) with every inference.
- All predictions are processed privately and securely using on-device models.
Flexible Prompting
The app includes a set of built-in prompts to help you get started quickly. Tap the Prompts button in the top-right corner to explore them. Selecting a prompt will immediately update the active input. To create new prompts or edit existing ones, choose Customize… from the Prompts menu.
Pretrained Model Options
There are 3 pretrained sizes of FastVLM to choose from:
- FastVLM 0.5B: Small and fast - great for mobile devices where speed matters.
- FastVLM 1.5B: Well balanced - great for larger devices where speed and accuracy matters.
- FastVLM 7B: Fast and accurate - ideal for situations where accuracy matters over speed.
To download any FastVLM listed above, use the get_pretrained_mlx_model.sh script. The script downloads the model from the web and places it in the appropriate location. Once a model has been downloaded using the steps below, no additional steps are needed to build the app in Xcode.
To explore how the other models work for your use-case, simply re-run the get_pretrained_mlx_model.sh with the new model selected, follow the prompts, and rebuild your app in Xcode.
Download Instructions
- Make the script executable
chmod +x app/get_pretrained_mlx_model.sh
- Download FastVLM
app/get_pretrained_mlx_model.sh --model 0.5b --dest app/FastVLM/model
- Open the app in Xcode, Build, and Run.
Custom Model
In addition to pretrained sizes of FastVLM, you can further quantize or fine-tune FastVLM to best fit their needs. To learn more, check out our documentation on how to export the model.
Please clear existing model in app/FastVLM/model before downloading or copying a new model.


