FastVLM-0.5B is an efficient vision-language model developed by Apple. It has 500 million parameters. Optimized through the GGUF format, it can achieve fast text generation and vision-language understanding tasks in resource-constrained environments.

Natural Language ProcessingGguf

FastVLM is a lightweight vision-language model developed by Apple. It has 500 million parameters and supports text generation and vision-language understanding tasks. The model offers multiple quantization versions and can be easily run using the gguf-connector tool.

MultimodalGguf

FastVLM is an efficient visual encoding scheme for vision-language models. It significantly reduces the encoding time of high-resolution images through the innovative FastViTHD hybrid visual encoder, improving model performance and response speed, and performs excellently in multiple vision-language understanding benchmark tests.

MultimodalSafetensors

FastVLM is an efficient vision-language model that significantly reduces the encoding time of high-resolution images and improves model performance through the innovative hybrid visual encoder FastViTHD. This model performs excellently in multiple visual question-answering and document understanding benchmark tests, while significantly reducing the time to generate the first word.

MultimodalSafetensors

FastVLM is an efficient visual coding scheme designed specifically for vision-language models. Through the innovative hybrid visual encoder FastViTHD, it significantly reduces the encoding time of high-resolution images and improves the model's response speed and performance. This model performs excellently in multiple visual question-answering and document understanding benchmark tests.

MultimodalSafetensors

fastvlm-0.5b-captions is an efficient vision-language model fine-tuned based on the third-stage model of FastVLM-0.5B, designed specifically for structured image description on mobile devices. This model uses LoRA fine-tuning, 4-bit quantization, and MobileCLIP-S0 visual tower technology, significantly reducing memory usage and enabling real-time inference on mobile devices such as iPhones.

MultimodalTransformersEnglish

FastVLM-1.5B-Stage3-MNN is a text generation model based on the Transformer architecture. It is an 8-bit quantized version of FastVLM-1.5B-Stage3, suitable for text generation scenarios such as chatting.

FastVLM-0.5B-Stage3 is an efficient multimodal language model with visual understanding and language processing capabilities. It can process long videos and generate structured outputs.

MultimodalTransformersEnglish

FastVLM-0.5B-Stage2 is an efficient multimodal language model capable of understanding visual content and handling text tasks.

MultimodalTransformersEnglish