ai-infra-learning

AI Infra Learning is a repository focused on providing resources and materials for learning about various topics related to artificial intelligence infrastructure. The repository includes documentation, papers, videos, and blog posts covering different aspects of AI infrastructure, such as large language models, memory management, decoding techniques, and text generation. Users can access a wide range of materials to deepen their understanding of AI infrastructure and improve their skills in this field.

Awesome-LLM-Constrained-Decoding is a curated list of papers, code, and resources related to constrained decoding of Large Language Models (LLMs). The repository aims to facilitate reliable, controllable, and efficient generation with LLMs by providing a comprehensive collection of materials in this domain.

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).

Current trends and state of the art for using open & local LLM models as copilots to complete code, generate projects, act as shell assistants, automatically fix bugs, and more. This document is a curated list of local Copilots, shell assistants, and related projects, intended to be a resource for those interested in a survey of the existing tools and to help developers discover the state of the art for projects like these.

LLM-Finetune is a repository for fine-tuning language models for various NLP tasks such as text classification and named entity recognition. It provides instructions and scripts for training and inference using models like Qwen2-VL and GLM4. The repository also includes datasets for tasks like text classification, named entity recognition, and multimodal tasks. Users can easily prepare the environment, download datasets, train models, and perform inference using the provided scripts and notebooks. Additionally, the repository references SwanLab, an AI training record, analysis, and visualization tool.

CS7320-AI is a repository containing lecture materials, simple Python code examples, and assignments for the course CS 5/7320 Artificial Intelligence. The code examples cover various chapters of the textbook 'Artificial Intelligence: A Modern Approach' by Russell and Norvig. The repository focuses on basic AI concepts rather than advanced implementation techniques. It includes HOWTO guides for installing Python, working on assignments, and using AI with Python.

Awesome Mobile LLMs is a curated list of Large Language Models (LLMs) and related studies focused on mobile and embedded hardware. The repository includes information on various LLM models, deployment frameworks, benchmarking efforts, applications, multimodal LLMs, surveys on efficient LLMs, training LLMs on device, mobile-related use-cases, industry announcements, and related repositories. It aims to be a valuable resource for researchers, engineers, and practitioners interested in mobile LLMs.

This repository contains the training code of LLM-QAT for large language models. The work investigates quantization-aware training for LLMs, including quantizing weights, activations, and the KV cache. Experiments were conducted on LLaMA models of sizes 7B, 13B, and 30B, at quantization levels down to 4-bits. Significant improvements were observed when quantizing weight, activations, and kv cache to 4-bit, 8-bit, and 4-bit, respectively.

The Awesome-Neuro-Symbolic-Learning-with-LLM repository is a curated collection of papers and resources focusing on improving reasoning and planning capabilities of Large Language Models (LLMs) and Multi-Modal Large Language Models (MLLMs) through neuro-symbolic learning. It covers a wide range of topics such as neuro-symbolic visual reasoning, program synthesis, logical reasoning, mathematical reasoning, code generation, visual reasoning, geometric reasoning, classical planning, game AI planning, robotic planning, AI agent planning, and more. The repository provides a comprehensive overview of tutorials, workshops, talks, surveys, papers, datasets, and benchmarks related to neuro-symbolic learning with LLMs and MLLMs.

The LLM Engineer Toolkit is a curated repository containing over 120 LLM libraries categorized for various tasks such as training, application development, inference, serving, data extraction, data generation, agents, evaluation, monitoring, prompts, structured outputs, safety, security, embedding models, and other miscellaneous tools. It includes libraries for fine-tuning LLMs, building applications powered by LLMs, serving LLM models, extracting data, generating synthetic data, creating AI agents, evaluating LLM applications, monitoring LLM performance, optimizing prompts, handling structured outputs, ensuring safety and security, embedding models, and more. The toolkit covers a wide range of tools and frameworks to streamline the development, deployment, and optimization of large language models.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

A curated repository of code recipes, demos, and resources for basic and advanced Redis use cases in the AI ecosystem. It includes demos for ArxivChatGuru, Redis VSS, Vertex AI & Redis, Agentic RAG, ArXiv Search, and Product Search. Recipes cover topics like Getting started with RAG, Semantic Cache, Advanced RAG, and Recommendation systems. The repository also provides integrations/tools like RedisVL, AWS Bedrock, LangChain Python, LangChain JS, LlamaIndex, Semantic Kernel, RelevanceAI, and DocArray. Additional content includes blog posts, talks, reviews, and documentation related to Vector Similarity Search, AI-Powered Document Search, Vector Databases, Real-Time Product Recommendations, and more. Benchmarks compare Redis against other Vector Databases and ANN benchmarks. Documentation includes QuickStart guides, official literature for Vector Similarity Search, Redis-py client library docs, Redis Stack documentation, and Redis client list.

visionOS-examples is a repository containing accelerators for Spatial Computing. It includes examples such as Local Large Language Model, Chat Apple Vision Pro, WebSockets, Anchor To Head, Hand Tracking, Battery Life, Countdown, Plane Detection, Timer Vision, and PencilKit for visionOS. The repository showcases various functionalities and features for Apple Vision Pro, offering tools for developers to enhance their visionOS apps with capabilities like hand tracking, plane detection, and real-time cryptocurrency prices.

MiniCPM-V & o Cookbook is a comprehensive repository for building multimodal AI applications effortlessly. It provides easy-to-use documentation, supports a wide range of users, and offers versatile deployment scenarios. The repository includes live demonstrations, inference recipes for vision and audio capabilities, fine-tuning recipes, serving recipes, quantization recipes, and a framework support matrix. Users can customize models, deploy them efficiently, and compress models to improve efficiency. The repository also showcases awesome works using MiniCPM-V & o and encourages community contributions.

The Together Cookbook is a collection of code and guides designed to help developers build with open source models using Together AI. The recipes provide examples on how to chain multiple LLM calls, create agents that route tasks to specialized models, run multiple LLMs in parallel, break down tasks into parallel subtasks, build agents that iteratively improve responses, perform LoRA fine-tuning and inference, fine-tune LLMs for repetition, improve summarization capabilities, fine-tune LLMs on multi-step conversations, implement retrieval-augmented generation, conduct multimodal search and conditional image generation, visualize vector embeddings, improve search results with rerankers, implement vector search with embedding models, extract structured text from images, summarize and evaluate outputs with LLMs, generate podcasts from PDF content, and get LLMs to generate knowledge graphs.

The Edge AI Libraries project is a collection of libraries, microservices, and tools for Edge application development. It includes sample applications showcasing generic AI use cases. Key components include Anomalib, Dataset Management Framework, Deep Learning Streamer, ECAT EnableKit, EtherCAT Masterstack, FLANN, OpenVINO toolkit, Audio Analyzer, ORB Extractor, PCL, PLCopen Servo, Real-time Data Agent, RTmotion, Audio Intelligence, Deep Learning Streamer Pipeline Server, Document Ingestion, Model Registry, Multimodal Embedding Serving, Time Series Analytics, Vector Retriever, Visual-Data Preparation, VLM Inference Serving, Intel Geti, Intel SceneScape, Visual Pipeline and Platform Evaluation Tool, Chat Question and Answer, Document Summarization, PLCopen Benchmark, PLCopen Databus, Video Search and Summarization, Isolation Forest Classifier, Random Forest Microservices. Visit sub-directories for instructions and guides.

AI Infra Learning is a repository focused on providing resources and materials for learning about various topics related to artificial intelligence infrastructure. The repository includes documentation, papers, videos, and blog posts covering different aspects of AI infrastructure, such as large language models, memory management, decoding techniques, and text generation. Users can access a wide range of materials to deepen their understanding of AI infrastructure and improve their skills in this field.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.