Best AI tools for< Upcycling Artist >

Trampoline is an AI-native proposal manager designed for sales teams to streamline the process of creating sales proposals. It leverages AI technology to help businesses excel at handling RFPs efficiently, saving valuable time and resources. Trampoline's innovative approach includes 'content upcycling' to quickly prepare necessary information, onboard new team members rapidly, and facilitate knowledge sharing within the organization. With features like semantic search, direct query forwarding, and expert contributions, Trampoline aims to revolutionize the way sales proposals are created and managed.

日本語LLMの包括的な概要です。400ワード未満で、改行や引用符はありません。

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

A curated collection of papers and resources on Mixture of Experts in Large Language Models. The repository provides a chronological overview of several representative Mixture-of-Experts (MoE) models in recent years, structured according to release dates. It covers MoE models from various domains like Natural Language Processing (NLP), Computer Vision, Multimodal, and Recommender Systems. The repository aims to offer insights into Inference Optimization Techniques, Sparsity exploration, Attention mechanisms, and safety enhancements in MoE models.

This repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more. It includes monthly best GenAI papers list, interview resources, free courses, and code repositories/notebooks for developing generative AI applications. The repository is regularly updated with the latest additions to keep users informed and engaged in the field of generative AI.

A comprehensive repository focusing on 'Model Merging in LLMs, MLLMs, and Beyond', providing an exhaustive overview of model merging methods, theories, applications, and future research directions. The repository covers various advanced methods, applications in foundation models, different machine learning subfields, and tasks like pre-merging methods, architecture transformation, weight alignment, basic merging methods, and more.

Efficient Foundation Model Survey is a comprehensive analysis of resource-efficient large language models (LLMs) and multimodal foundation models. The survey covers algorithmic and systemic innovations to support the growth of large models in a scalable and environmentally sustainable way. It explores cutting-edge model architectures, training/serving algorithms, and practical system designs. The goal is to provide insights on tackling resource challenges posed by large foundation models and inspire future breakthroughs in the field.

Your enthusiastic guide to creative upcycling and sustainability!

Your go-to source for trendy upcycling ideas and visuals!

I turn your waste into wonders with ideas, steps, pictures.

Transforming everyday items into stylish decor. Offering budget-friendly, creative living space makeovers with eco-conscious materials and trending designs.