Making large AI models cheaper, faster and more accessible
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Updated
Jul 4, 2024 - Python
Making large AI models cheaper, faster and more accessible
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.
Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
🦦 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.
Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)
DeepSeek-VL: Towards Real-World Vision-Language Understanding
EVA Series: Visual Representation Fantasies from BAAI
Images to inference with no labeling (use foundation models to train supervised models).
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
Emu Series: Generative Multimodal Models from BAAI
[ECCV2024] Video Foundation Models & Data for Multimodal Understanding
VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models
[ICLR 2024] Official PyTorch implementation of FasterViT: Fast Vision Transformers with Hierarchical Attention
[ECCV2024] Grounded Multimodal Large Language Model with Localized Visual Tokenization
A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
A unified multi-task time series model.
Creative interactive views of any dataset.
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