🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.
The main features of luodian/otter are: In Context Learning, Multimodal Datasets, Pre-training Datasets.
Open-source alternatives to luodian/otter include: mbzuai-oryx/video-chatgpt — [ACL 2024 🔥] Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos.… plexpt/chatgpt-corpus — This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of… alirezadir/machine-learning-interviews — This project is a comprehensive machine learning interview guide and technical study resource designed for individuals… allenai/visprog — Official code for VisProg (CVPR 2023 Best Paper!). chen700564/metaner-icl — An implementation for ACL 2023 paper Learning In-context Learning for Named Entity Recognition. baaivision/emu — Emu Series: Generative Multimodal Models from BAAI.
ACL 2024 🔥 Video-ChatGPT is a video conversation model capable of generating meaningful conversation about videos. It combines the capabilities of LLMs with a pretrained visual encoder adapted for spatiotemporal video representation. We also introduce a rigorous 'Quantitative Evaluation Benchmarking' for video-based conversational models.
This project provides a comprehensive Chinese language corpus designed to support the training and fine-tuning of large language models. It serves as a structured natural language processing resource, offering a collection of text data that includes dialogue, customer service interactions, and creative writing. The dataset is organized into distinct thematic categories, allowing for targeted model development across specific conversational and narrative contexts. By providing information in standardized, schema-agnostic text formats, the collection ensures portability across various machine l
This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l
