Making large AI models cheaper, faster and more accessible
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Updated
Sep 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.
🦦 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.
Code and models for NExT-GPT: Any-to-Any Multimodal Large Language Model
[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
Chronos: Pretrained (Language) Models for Probabilistic Time Series Forecasting
EVA Series: Visual Representation Fantasies from BAAI
DeepSeek-VL: Towards Real-World Vision-Language Understanding
Images to inference with no labeling (use foundation models to train supervised models).
Prompt Learning for Vision-Language Models (IJCV'22, CVPR'22)
Emu Series: Generative Multimodal Models from BAAI
An automatic evaluator for instruction-following language models. Human-validated, high-quality, cheap, and fast.
Awesome things about LLM-powered agents. Papers / Repos / Blogs / ...
[ECCV2024] Video Foundation Models & Data for Multimodal Understanding
Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting
日本語LLMまとめ - Overview of Japanese LLMs
A general representation model across vision, audio, language modalities. Paper: ONE-PEACE: Exploring One General Representation Model Toward Unlimited Modalities
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
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