Best AI tools for< Dao Researcher >

GenAI Summit San Francisco 2024 is an innovative AI tool designed to bring together industry leaders, researchers, and enthusiasts to explore the latest trends and advancements in artificial intelligence. The platform offers a virtual space for networking, knowledge sharing, and collaboration, enabling participants to gain insights into cutting-edge AI technologies and applications. With interactive sessions, keynote speeches, and panel discussions, GenAI Summit fosters a vibrant community of AI professionals and facilitates meaningful connections in the field.

Alphakek AI is an AI lab that offers an alternative to big tech AI platforms like OpenAI and Anthropic. They have redesigned and re-engineered every layer of AI for crypto developers, traders, and creators. Alphakek provides uncensored AI models fine-tuned on crypto data, available via Telegram, Discord, Web App, and API. Their proprietary data engine, codenamed 'Fractal,' processes 2-5x more information sources simultaneously than conventional models, encompassing onchain data, mainstream financial news, niche crypto forums, and more.

OnOut is a platform that offers a variety of tools for developers to deploy web3 apps on their own domain with ease. It provides deployment tools for blockchain apps, DEX, farming, DAO, cross-chain setups, IDOFactory, NFT staking, and AI applications like Chate and AiGram. The platform allows users to customize their apps, earn commissions, and manage various aspects of their projects without the need for coding skills. OnOut aims to simplify the process of launching and managing decentralized applications for both developers and non-technical users.

LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

Chinese-LLaMA-Alpaca-2 is a large Chinese language model developed by Meta AI. It is based on the Llama-2 model and has been further trained on a large dataset of Chinese text. Chinese-LLaMA-Alpaca-2 can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. Here are some of the key features of Chinese-LLaMA-Alpaca-2: * It is the largest Chinese language model ever trained, with 13 billion parameters. * It is trained on a massive dataset of Chinese text, including books, news articles, and social media posts. * It can be used for a variety of natural language processing tasks, including text generation, question answering, and machine translation. * It is open-source and available for anyone to use. Chinese-LLaMA-Alpaca-2 is a powerful tool that can be used to improve the performance of a wide range of natural language processing tasks. It is a valuable resource for researchers and developers working in the field of artificial intelligence.

Liger Kernel is a collection of Triton kernels designed for LLM training, increasing training throughput by 20% and reducing memory usage by 60%. It includes Hugging Face Compatible modules like RMSNorm, RoPE, SwiGLU, CrossEntropy, and FusedLinearCrossEntropy. The tool works with Flash Attention, PyTorch FSDP, and Microsoft DeepSpeed, aiming to enhance model efficiency and performance for researchers, ML practitioners, and curious novices.

This repository provides a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from **model-centric** , **data-centric** , and **framework-centric** perspective, respectively. We hope our survey and this GitHub repository can serve as valuable resources to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.

Awesome-LLM is a curated list of resources related to large language models, focusing on papers, projects, frameworks, tools, tutorials, courses, opinions, and other useful resources in the field. It covers trending LLM projects, milestone papers, other papers, open LLM projects, LLM training frameworks, LLM evaluation frameworks, tools for deploying LLM, prompting libraries & tools, tutorials, courses, books, and opinions. The repository provides a comprehensive overview of the latest advancements and resources in the field of large language models.

LitGPT is a command-line tool designed to easily finetune, pretrain, evaluate, and deploy 20+ LLMs **on your own data**. It features highly-optimized training recipes for the world's most powerful open-source large-language-models (LLMs).

Since the emergence of chatGPT in 2022, the acceleration of Large Language Model has become increasingly important. Here is a list of papers on LLMs inference and serving.

The AI Agents Contributions Library is a repository dedicated to managing datasets on voice and cognitive core data for AI agents within the Virtual DAO ecosystem. It provides a structured framework for recording, reviewing, and rewarding contributions from contributors. The repository includes folders for character cards, contribution datasets, fine-tuning resources, text datasets, and voice datasets. Contributors can submit datasets following specific guidelines and formats, and the Virtual DAO team reviews and integrates approved datasets to enhance AI agents' capabilities.

AiTreasureBox is a versatile AI tool that provides a collection of pre-trained models and algorithms for various machine learning tasks. It simplifies the process of implementing AI solutions by offering ready-to-use components that can be easily integrated into projects. With AiTreasureBox, users can quickly prototype and deploy AI applications without the need for extensive knowledge in machine learning or deep learning. The tool covers a wide range of tasks such as image classification, text generation, sentiment analysis, object detection, and more. It is designed to be user-friendly and accessible to both beginners and experienced developers, making AI development more efficient and accessible to a wider audience.

AITreasureBox is a comprehensive collection of AI tools and resources designed to simplify and accelerate the development of AI projects. It provides a wide range of pre-trained models, datasets, and utilities that can be easily integrated into various AI applications. With AITreasureBox, developers can quickly prototype, test, and deploy AI solutions without having to build everything from scratch. Whether you are working on computer vision, natural language processing, or reinforcement learning projects, AITreasureBox has something to offer for everyone. The repository is regularly updated with new tools and resources to keep up with the latest advancements in the field of artificial intelligence.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

Awesome-LLM-Large-Language-Models-Notes is a repository that provides a comprehensive collection of information on various Large Language Models (LLMs) classified by year, size, and name. It includes details on known LLM models, their papers, implementations, and specific characteristics. The repository also covers LLM models classified by architecture, must-read papers, blog articles, tutorials, and implementations from scratch. It serves as a valuable resource for individuals interested in understanding and working with LLMs in the field of Natural Language Processing (NLP).

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

SLMs-Survey is a comprehensive repository that includes papers and surveys on small language models. It covers topics such as technology, on-device applications, efficiency, enhancements for LLMs, and trustworthiness. The repository provides a detailed overview of existing SLMs, their architecture, enhancements, and specific applications in various domains. It also includes information on SLM deployment optimization techniques and the synergy between SLMs and LLMs.

MedicalGPT is a training medical GPT model with ChatGPT training pipeline, implement of Pretraining, Supervised Finetuning, RLHF(Reward Modeling and Reinforcement Learning) and DPO(Direct Preference Optimization).

ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

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