#multi-modal models

The project delivers a versatile framework for Chinese NLP tasks, utilizing PyTorch and Transformers. It includes comprehensive training and fine-tuning solutions for a variety of models such as text-to-vector and multimodal. Abundant open-source training data ensures easy setup with advanced processing methods suitable for large datasets. Models supported include GPT2, CLIP, and GPT-NeoX among others, offering multi-GPU training and deployment capabilities. Discover tutorials for model modification and explore a wide range of pretrained and custom models for diverse NLP needs.

Mantis enhances multi-image visual language task processing using LLaMA-3, allowing for efficient handling of text and image inputs simultaneously. By achieving top performance on five key benchmarks with minimal resources, Mantis stands out without extensive pre-training data. It ensures robust single-image capabilities similar to CogVLM and Emu2. Recent updates include Idefics-3 training support and evaluation tools via VLMEvalKit. Explore a variety of models and scripts available on Hugging Face.

ViTamin offers scalable vision models that excel in zero-shot ImageNet accuracy and open-vocabulary segmentation. It integrates with platforms like Hugging Face and timm, supporting applications like pre-training and detection. By using fewer parameters, ViTamin achieves high benchmark performances, contributing to advances in vision-language AI research.

This project allows for convenient deployment of private Gemini applications, accommodating models such as Gemini 1.5 Pro and Flash. It features a compact, cross-platform client that boosts productivity with image recognition and voice conversation capabilities, alongside a responsive design. The interface is straightforward, ensuring data privacy through local storage, and offers PWA support for enhanced performance. An extensive assistant market and complete Markdown support in multiple languages complement the smooth and rapid user experience.