# apple/ml-fastvlm

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7,375 stars · 555 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/apple/ml-fastvlm
- awesome-repositories: https://awesome-repositories.com/repository/apple-ml-fastvlm.md

## Description

This project is a vision language model framework and vision-to-text pipeline designed for deploying and optimizing models that process both images and text. It provides an on-device inference engine and a vision language model framework to run quantized models locally on mobile and desktop hardware accelerators.

The framework features a model quantization toolkit to reduce weight precision for lower memory footprints and increased execution speed on specialized silicon. It also includes an efficient vision encoder utilizing a hybrid encoding system to compress image tokens, which reduces processing time and memory usage.

The system covers a broad range of capabilities, including model export for hardware-specific and silicon-optimized formats, vision encoder optimization, and template-based prompt engineering. It supports vision-language tasks such as visual question answering, visual content description, and inference latency tracking to measure time-to-first-token performance.

## Tags

### Artificial Intelligence & ML

- [On-Device Inference Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/on-device-inference-engines.md) — Provides a runtime optimized for executing quantized vision language models locally on mobile and desktop hardware accelerators.
- [Vision-Language Inference](https://awesome-repositories.com/f/artificial-intelligence-ml/vision-language-inference.md) — Provides a framework for executing multimodal models that process combined image and text inputs to generate analytical text. ([source](https://github.com/apple/ml-fastvlm/blob/main/README.md))
- [Image Description Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/image-description-generation.md) — Generates detailed text-based summaries and descriptions of visual content using vision language models. ([source](https://github.com/apple/ml-fastvlm/blob/main/predict.py))
- [Hardware-Specific Model Optimizations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/training-algorithms/machine-learning-optimization/ml-performance-profilers/hardware-specific-model-optimizations.md) — Transforms model checkpoints into optimized formats and quantization levels compatible with specific hardware accelerators. ([source](https://github.com/apple/ml-fastvlm#readme))
- [Quantization Toolkits](https://awesome-repositories.com/f/artificial-intelligence-ml/memory-optimization-techniques/quantization-toolkits.md) — Ships a toolkit for reducing model weight precision to optimize memory footprints and execution speed on specialized silicon.
- [Model Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/model-quantization.md) — Provides a workflow for reducing model weight precision to decrease memory usage and improve performance on local systems.
- [Vision-Language Models](https://awesome-repositories.com/f/artificial-intelligence-ml/on-device-models/vision-language-models.md) — Offers a comprehensive framework for deploying and optimizing compact vision-language models on edge hardware.
- [Weight Quantization](https://awesome-repositories.com/f/artificial-intelligence-ml/quantized-inference-runtimes/weight-quantization.md) — Implements techniques for compressing model weights into lower-precision formats to reduce memory footprint and increase local inference speed.
- [Hybrid Token Compressors](https://awesome-repositories.com/f/artificial-intelligence-ml/vision-encoders/hybrid-token-compressors.md) — Implements a hybrid encoding system that compresses image tokens to minimize processing time and memory usage.
- [Visual Question Answering](https://awesome-repositories.com/f/artificial-intelligence-ml/visual-question-answering.md) — Implements on-device models and frameworks that answer natural language questions about visual content while maintaining privacy. ([source](https://github.com/apple/ml-fastvlm/blob/main/app))
- [Visual-to-Text Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/visual-to-text-generation.md) — Implements a pipeline that converts visual inputs and text prompts into natural language descriptions and answers.
- [Computer Vision Optimization](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-optimization.md) — Provides techniques to reduce image token counts and encoding time to accelerate vision language model processing.
- [Vision-Language Templates](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai/generative-text-inference/image-text-prompt-inferences/vision-language-templates.md) — Enables the creation of text templates to guide how the vision language model interprets images and generates responses. ([source](https://github.com/apple/ml-fastvlm/blob/main/app))
- [Model Weight Swapping](https://awesome-repositories.com/f/artificial-intelligence-ml/model-capability-extensions/ai-provider-interfaces/hot-swappable-providers/model-weight-swapping.md) — Allows switching neural network weight sets at runtime by loading specific checkpoints to optimize for local hardware.
- [Model Weight Management](https://awesome-repositories.com/f/artificial-intelligence-ml/model-weight-management.md) — Provides utilities for downloading, storing, and loading pretrained or quantized model weights for local hardware execution. ([source](https://github.com/apple/ml-fastvlm/blob/main/app))
- [On-Device Model Profilers](https://awesome-repositories.com/f/artificial-intelligence-ml/on-device-models/on-device-speech-to-text-sdks/on-device-model-runtimes/on-device-model-profilers.md) — Measures inference latency and time-to-first-token to evaluate and optimize on-device AI performance.
- [Prompt Templates](https://awesome-repositories.com/f/artificial-intelligence-ml/prompt-templates.md) — Provides systems for defining and managing reusable prompt structures to guide how visual inputs are processed.
- [Encoder Exporters](https://awesome-repositories.com/f/artificial-intelligence-ml/vision-encoders/encoder-exporters.md) — Saves model states and converts the vision model for compatibility with third party libraries. ([source](https://github.com/apple/ml-fastvlm/blob/main/model_export))

### Data & Databases

- [Visual Token Compression](https://awesome-repositories.com/f/data-databases/data-compression-algorithms/visual-token-compression.md) — Implements a hybrid encoding system that reduces visual token counts to accelerate vision language model processing. ([source](https://github.com/apple/ml-fastvlm#readme))

### Mobile Development

- [Mobile Model Deployment](https://awesome-repositories.com/f/mobile-development/mobile-model-deployment.md) — Integrates large vision models into native mobile applications through quantization and format conversion for local execution. ([source](https://github.com/apple/ml-fastvlm#readme))