#pretrained model

GLM-4-9B, developed by Zhipu AI, delivers outstanding performance in comparison to other leading models like GPT-4-turbo and Llama-3-8B, across tasks such as dialogue, mathematics, reasoning, and code execution. As an open-source model, it supports multilingual capabilities and can process up to 1 million tokens in chat environments, providing high-resolution dialogue in two languages. Recent updates include OpenAI API compatibility and improvements in generating extended content. Explore its capabilities demonstrated through various multilingual and multimodal assessments.

The MM-Interleaved model is a pioneer in interleaved image-text generative modeling, featuring a multi-modal feature synchronizer for high-resolution recognition. It supports tasks like visual storytelling, visual question answering, and text-to-image generation. With zero-shot and finetuning capabilities, it offers excellent performance across multiple benchmarks. Access pretrained models for versatile application adaptation.

Explore a unified sequence-to-sequence model that supports cross-modality tasks such as image captioning, VQA, and text classification with state-of-the-art pretraining and finetuning. This model offers multi-language support and interactive online demos, with resources for transformers integration, demonstrating strong performance in COCO and VQA challenges.

Learn about implementing the BERT model with PyTorch for tasks such as text classification, question answering, and named entity recognition. The project offers insights into BERT's functionalities and related applications, aiding in the comprehension of transformer mechanisms. Suitable for developers and researchers employing BERT's pre-trained models in language processing.