apple/ml-fastvlm
Ml Fastvlm
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.
Features
- On-Device Inference Engines - Provides a runtime optimized for executing quantized vision language models locally on mobile and desktop hardware accelerators.
- Vision-Language Inference - Provides a framework for executing multimodal models that process combined image and text inputs to generate analytical text.
- Image Description Generation - Generates detailed text-based summaries and descriptions of visual content using vision language models.
- Hardware-Specific Model Optimizations - Transforms model checkpoints into optimized formats and quantization levels compatible with specific hardware accelerators.
- Quantization Toolkits - Ships a toolkit for reducing model weight precision to optimize memory footprints and execution speed on specialized silicon.
- Model Quantization - Provides a workflow for reducing model weight precision to decrease memory usage and improve performance on local systems.
- Vision-Language Models - Offers a comprehensive framework for deploying and optimizing compact vision-language models on edge hardware.
- Weight Quantization - Implements techniques for compressing model weights into lower-precision formats to reduce memory footprint and increase local inference speed.
- Hybrid Token Compressors - Implements a hybrid encoding system that compresses image tokens to minimize processing time and memory usage.
- Visual Question Answering - Implements on-device models and frameworks that answer natural language questions about visual content while maintaining privacy.
- Visual-to-Text Generation - Implements a pipeline that converts visual inputs and text prompts into natural language descriptions and answers.
- Visual Token Compression - Implements a hybrid encoding system that reduces visual token counts to accelerate vision language model processing.
- Mobile Model Deployment - Integrates large vision models into native mobile applications through quantization and format conversion for local execution.
- Computer Vision Optimization - Provides techniques to reduce image token counts and encoding time to accelerate vision language model processing.
- Vision-Language Templates - Enables the creation of text templates to guide how the vision language model interprets images and generates responses.
- Model Weight Swapping - Allows switching neural network weight sets at runtime by loading specific checkpoints to optimize for local hardware.
- Model Weight Management - Provides utilities for downloading, storing, and loading pretrained or quantized model weights for local hardware execution.
- On-Device Model Profilers - Measures inference latency and time-to-first-token to evaluate and optimize on-device AI performance.
- Prompt Templates - Provides systems for defining and managing reusable prompt structures to guide how visual inputs are processed.
- Encoder Exporters - Saves model states and converts the vision model for compatibility with third party libraries.
Star history
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