interpretable-foundation-models

Making large AI models cheaper, faster and more accessible

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

DeepSeek-VL: Towards Real-World Vision-Language Understanding

Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model

? TabPFN: Foundation Model for Tabular Data ?

? 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.

[CVPR2024 Highlight][VideoChatGPT] ChatGPT with video understanding! And many more supported LMs such as miniGPT4, StableLM, and MOSS.

SuperCLUE: 中文通用大模型综合性基准 | A Benchmark for Foundation Models in Chinese