Production ready toolkit to run AI locally

Collection of resources on the applications of Large Language Models (LLMs) in Audio AI.

MOMENT: A Family of Open Time-series Foundation Models, ICML'24

👁️ + 💬 + 🎧 = 🤖 Curated list of top foundation and multimodal models! [Paper + Code + Examples + Tutorials]

Cosmos-Predict2.5, the latest version of the Cosmos World Foundation Models (WFMs) family, specialized for simulating and predicting the future state of the world in the form of video.

Official repository for "CLIP model is an Efficient Continual Learner".

Google Cloud Medical Imaging ML Development Accelerators

ACM Multimedia 2023 (Oral) - RTQ: Rethinking Video-language Understanding Based on Image-text Model

In this course navigates through the LLMOps pipeline, enabling you to preprocess training data for supervised fine-tuning and deploy custom Large Language Models (LLMs).

This repository is for profiling, extracting, visualizing and reusing generative AI weights to hopefully build more accurate AI models and audit/scan weights at rest to identify knowledge domains for risk(s).

🐍📦 High-performance cosine similarity ranking for Retrieval-Augmented Generation (RAG) pipelines.

Glycan Informed Foundational Framework for Learning Abstract Representations, based on Combinatorial Complexes and Heterogeneous GNNs

An improved temporal data pipeline with foundational model for battery State of Health (SOH) prediction (R²->0.99) using advanced time series decomposition (D3R, CEEMDAN) and transformer-based methods. Utilized 100-150 features (ARIMA-based, Rolling statistics, Degradation indicators)

Course assignments of COL828:- Advanced Computer Vision course at IIT Delhi under Professor Chetan Arora

An interactive dashboard for time-series forecasting using state-of-the-art foundational models.

A repository with demo Chatbot and Agent using Foundational Models

Short course's of deeplearning.ai

This repository is created to compared foundational models at different task, using visualisation and statistics.

A collection of medical imaging and machine learning projects, including foundational segmentation models.

We introduce Weight Sharing Attention to improve state representation in Reinforcement Learning. By combining embeddings from different Foundational Models, WSA enhances learning efficiency. Tested on Atari games, it performs on par with advanced methods and addresses issues like out-of-distribution data.