#LLMs

Discover the capabilities of AI in code generation with GPT Pilot, where LLMs can produce nearly complete production-ready applications, complemented by developer adjustments. Seamlessly integrate with platforms like Docker and PostgreSQL. Engage with the Discord community and access insightful blogs. Compatible with Python 3.9+, utilizing advanced AI like OpenAI, this tool ensures efficient workflows via CLI and Docker. Streamline your development process with AI-driven solutions.

MetaGPT is a multi-agent framework that effectively manages complex tasks by integrating GPTs into various software roles. It features user story creation, competitive analysis, and API documentation within a collaborative environment based on 'Code = SOP(Team)' philosophy. Utilizing LLMs as role players, it efficiently orchestrates tasks for product managers and engineers. Recent updates include Data Interpreter features and support for various LLMs, ensuring robust solution offerings like debate handling and data analysis.

Discover essential insights for preparing for LLMs interviews and learn about key legal implications associated with the Apache-2.0 license. This resource provides guidance on managing intellectual property concerns and encourages readers to refer to original sources for comprehensive details.

This repository offers a comprehensive review of significant conference papers in LLMs, focusing on multimodal models, PEFT, low-shot QA, RAG, LMMs interpretability, agents, and CoT. It provides valuable resources such as 'LLMs Nine-Story Demon Tower' and 'LLMs Interview Notes'. Detailed insights into numerous series, including Gemini and GPT-4V evaluations, help AI algorithm engineers bridge essential understanding. Explore methods and advances in LLMs, NLP, and recommendation systems across sectors like healthcare and law, highlighting practical applications.

Discover a framework enabling efficient agent creation and seamless integration with LLMs for workflow optimization. Supports auto-generated prompts, diverse LLMs, vector databases, and facilitates output validation while streaming, ideal for varied AI applications.

Pykoi is an open-source Python library that facilitates the optimization of large language models utilizing Reinforcement Learning with Human Feedback (RLHF). It features a unified interface for collecting user feedback in real-time, finetuning, and comparing different models. Key functionalities include a UI for chat history storage, tools for efficient model performance comparison, and RAG chatbot integration. Compatible with CPU and GPU environments, Pykoi supports models from OpenAI, Amazon Bedrock, and Huggingface, aiding in fine-tuning models with custom datasets for improved precision and relevance.

This guide offers an in-depth look at prompt engineering, essential for the enhancement and effective application of large language models (LLMs). It features the newest research findings, educational resources, and tools to improve LLM performance in areas such as question answering and complex reasoning. Continuously updated with fresh content and collaborations, this resource serves both researchers and developers interested in mastering advanced prompt engineering.

This repository integrates OpenAI APIs with open source models, providing capabilities in chat, audio, and image processing. It supports libraries like OpenAI and LangChain for applications from transcription to image creation. The repository, though deprecated, allows for custom frontend development and model management with configurable settings. Discover extensive model support and API features for AI project customization.

Motorhead provides memory and retrieval capabilities for LLM-based chat applications, featuring session message handling and text searching via VSS. Despite discontinued support, its APIs include session memory management, automatic session initiation, and model integration with gpt-3.5-turbo and gpt-4.

A comprehensive overview of Human-Aware Loss Functions (HALOs) for aligning large-scale language models like Llama and Archangel using offline human feedback. Highlights include modular data loading, specialized trainer subclasses, and sophisticated evaluation techniques, offering scalable solutions for advanced AI alignment.

Discover an extensive collection of models tailored for chat applications utilizing advanced SFT, DPO, and RLHF techniques. Notus models emphasize a data-driven, human-focused methodology, demonstrated by performance metrics from MT-Bench, AlpacaEval, and Open LLM Leaderboard benchmarks. Named after the Greek god of the south wind, Notus integrates mythology with AI, appreciating the open-source community's indispensable support. Learn how Notus 7B v1 excels beyond earlier versions in recent assessments.

AgentTuning improves the general agent abilities of LLMs by using interaction trajectories for instruction tuning. The open-source AgentInstruct dataset and AgentLM models provide carefully curated interactions for enhancing AI performance in various real-world scenarios. This methodology shows strong generalization on new tasks while maintaining language capabilities. Models available on Huggingface are evaluated through tasks ranging from AgentBench to scientific and gaming applications, providing detailed insights into the capabilities and effectiveness of these enhanced LLM agents. Discover the innovative approaches employed by this project and its impact on generalized agent abilities.

Crawl4AI provides a web crawling solution tailored for AI applications, featuring asynchronous processing, multi-browser compatibility, and robust anti-bot strategies. Its LLM-optimized output formats and customizable data extraction options support JSON, HTML, and markdown structures. Recent enhancements include markdown extraction and refined chunking approaches that enhance performance, offering developers a powerful tool for data extraction without exaggeration.

Explore how Go developers can integrate large language models (LLMs) seamlessly using LangChain. With detailed documentation and practical examples, this project supports efficient AI solution development and experimentation with OpenAI and others. Access valuable resources and community support for innovative AI applications in Go.

Experience streamlined keyboard-centric interactions with both proprietary and local large language models in a terminal-based application. Enjoy efficient use with stored conversations in SQLite, customizable themes, and easy installation and API integrations.

The platform provides an intuitive interface for fine-tuning open-source LLMs such as Mistral, LLaMA, and GPT-J. It facilitates customized model management while maintaining data privacy, supports data ingestion, scalable GPU usage, and employs memory-efficient methods like INT4 and LoRA to lower hardware expenses. Users can explore various tuning techniques, assess models using defined metrics, and leverage new library features such as LLaMA 2 integration and CPU inference capabilities for enhanced performance and precision.

Explore Self-Extend for efficient LLM context window expansion without additional tuning. This project leverages intrinsic language model strengths through bi-level attention. Recent updates include LLama-3 support and ICML 2024 presentation. Suitable for researchers and developers targeting long-sequence model efficiency.

Open Interface automates computer tasks by integrating with LLMs such as GPT-4V to methodically execute user commands. It mimics user inputs and adjusts operations with real-time screenshots for accuracy. Compatible with macOS, Linux, and Windows, it simplifies both ordinary and complex tasks. By connecting to OpenAI’s GPT-4V, it expands functionality across diverse software applications. Explore its capabilities through demos and straightforward installation instructions to experience modern computer automation.

Data-Juicer is a versatile platform that streamlines the processing of multimodal data for large language models, supporting formats like text, image, audio, and video. Its integration with Alibaba Cloud's AI enhances data-model co-development, allowing swift iteration and refinement. With extensive features and flexible configurations, it upgrades data quality and efficiency in AI processing, aligning with top industry standards.

HackingBuddyGPT uses LLMs to streamline the discovery of security vulnerabilities with minimal code. It serves security professionals with tools for Linux privilege escalation and AI-based testing across web and API platforms, contributing to open-access research.

Discover the composability of Rust for developing applications with large language models (LLMs). The project supports various LLMs, including OpenAI and Anthropic Claude, and offers features like semantic routing and multi-format document loaders. Integrate with tools such as Wolfram/Math and DuckDuckGo Search to enhance application capabilities. The easy installation process allows integration of langchain-rust, leveraging Rust’s performance for advanced AI applications. Explore embeddings, vector stores, and conversational chains to streamline your development process.

Embedbase provides an intuitive API that makes it simple for developers to integrate VectorDBs and LLMs into AI applications without needing to manage hosting. It reduces development time while enhancing application functionality with its features like semantic search and text generation using over 9 LLMs. With easy-to-use functions like `.add()` and `.search()`, developers can swiftly implement AI capabilities, including recommendation engines and intelligent chat features, all through Embedbase's straightforward JavaScript SDK. Whether you're building chat integrations or enhancing documentation with AI, leverage OpenAI's ChatGPT technologies efficiently. Explore the potential with a free trial on Embedbase Cloud and experience seamless integration instantly.

This guide consolidates generative AI work from courses and projects, covering tools like LangChain and Autogen. It outlines practical use cases of LLMs, such as search and data generation, and features sections for learning, tool experimentation, and insightful blog discussions.

The Financial Datasets library enables the creation of question-answer sets from documents such as 10-K, 10-Q, and PDFs using advanced Large Language Models (LLMs). This Python-based open-source library, featuring models like gpt-4-turbo, facilitates the production of realistic datasets from varied financial texts. It's ideal for analysts and developers wanting to enhance their analytical projects with tailored information. The library supports easy installation via pip or Poetry, with opportunities for community contributions.

Step-DPO improves reasoning in language models via a step-wise preference framework, using a robust 10K-step dataset. It enhances models like Qwen2-7B-Instruct, raising MATH performance by 5.6% and GSM8K by 2.4% with limited data. The method yields 70.8% and 94.0% on Qwen2-72B-Instruct for MATH and GSM8K tests, outperforming models like GPT-4-1106. Suitable for researchers and developers, Step-DPO includes a demo and detailed documentation for easier implementation and evaluation.

This collection offers detailed interview notes for Large Language Models (LLMs) derived from expert experiences. It includes foundational to advanced preparation, addressing frequent interview questions, model structures, and training goals. The guide provides strategies for managing issues such as repetitive outputs and model choice in different fields, as well as insights on distributed training, efficient tuning, and inference. It serves as a practical resource for understanding LLMs in professional interviews without excessive embellishment.

This toolkit offers a secure solution for managing LLM interactions, mitigating significant risks in GenAI applications. It includes over 20 detectors for thorough protection and enables the customization of LLM behaviors. Additionally, it tracks incidents, expenses, and responsible AI metrics while addressing risks such as bias, toxicity, and privacy through multi-layered defense.

OpenHands is a platform utilizing AI to mirror the tasks of human developers, including code modifications and command executions. It provides a simple Docker setup and supports various LLM providers, catering to both developers and researchers. As a community-driven initiative, OpenHands offers opportunities for code contribution, research participation, and feedback. Explore detailed documentation and engage with an active community focused on improving AI in software development.

Skyvern is a tool that automates browsing tasks using LLMs and computer vision, efficiently managing workflows on various websites. It addresses the limitations of traditional scripting by dynamically adapting to changes without the need for customized code. Through its agents, Skyvern can navigate, extract data, manage credentials, and complete forms, facilitating complex tasks such as competitive analysis and product comparison. The cloud version includes anti-bot mechanisms and CAPTCHA solutions, providing streamlined automation tailored to numerous workflow requirements.

Aider is an AI-assisted tool for pair programming within your terminal, facilitating efficient collaboration on local git repositories. It utilizes advanced LLMs like GPT-4o and Claude 3.5 Sonnet to help edit, refactor, and improve codebases effectively. With features such as automatic git commits, support for various programming languages, and real-time editing, Aider is equipped to handle complex coding challenges by mapping the entire git repository. Users can explore diverse usage options like voice coding and API connections for enhanced productivity.

Discover the Reflex Chat App, a Python-based web application designed to demonstrate Large Language Models (LLMs) similar to ChatGPT. This app, built with Reflex, allows customization without the need for web development expertise. Key features include a Python-based UI, session creation and deletion, and model interchangeability. Easily set up using an OpenAI API key, it offers seamless, responsive use across devices, making it suitable for efficiently managing chat sessions.

torchchat facilitates smooth operation of large language models across platforms such as desktop, server, iOS, and Android. It features multimodal capabilities with the Llama3.2 11B model and integrates smoothly with PyTorch, supporting different execution modes like eager and AOT Inductor. Key features include interaction with well-known LLMs, hardware and OS compatibility, and versatile quantization and execution schemes.

This experimental application illustrates the use of Large Language Models (LLMs) in natural language SQL querying. Interaction with a SQL database requires the insertion of an OPENAI_API_KEY into the .env.local file. The app is straightforward to install using npm and can be initiated with basic commands. A sample database illustrates its functionality, complemented by video demonstrations showing its operations. Explore a modern technique in database querying that merges technological advancement with ease of use.

This low-code library assists developers in improving ML model accuracy by sourcing relevant features from diverse external data repositories, including public, community, and commercial platforms. Through automated feature search and optimization, it moves beyond typical parameter tuning. Supporting various supervised tasks, it integrates easily with ML workflows through a Scikit-learn interface, broadening access to essential data resources and optimizing feature application.

AI Gateway efficiently routes requests across language, vision, audio, and image models using a unified API. With key functionalities like load balancing, caching, automated retries, and multimodal support, it enhances application resilience. Its compact structure and enterprise-ready design ensure seamless integration and low latency. Ideal for scaling AI projects with robust and secure capabilities.

The project combines modern frontend tools such as Next JS and Tailwind CSS with robust backend support via FastAPI, designed for developers focused on building efficient AI products. By utilizing server-sent endpoints and React Flow, it streamlines the development process. Review installation steps, from environment setup to local server execution, and experience the web interface. Suitable for developers with a Python background looking to adopt advanced technologies for swift deployment.

This repository offers a curated list of LLMs designed for multimodal generation and editing, encompassing visual and audio modalities. It serves as a resource for those researching image, video, 3D, and audio content creation and modification. Contributors are invited to add insights or suggest enhancements. The focus is on both LLM-based and alternative methods, with an emphasis on datasets and practical applications. The project also includes tips for paper searches and links to code repositories, fostering innovation within the multimodal AI community.

KIVI significantly optimizes LLM memory usage and enhances throughput by using a tuning-free 2bit quantization strategy for KV caches. This method decreases memory needs by 2.6 times, enabling larger batch sizes and improving throughput up to 3.47 times. Compatible with models like Llama-2 and Mistral, KIVI maintains model quality while solving speed and memory challenges in inference tasks. Discover new features and examples on GitHub, including improvements for HuggingFace Transformers and support for the Mistral models.

Explore LocalAI, a free and open-source REST API providing compatibility with major AI specifications for on-premise deployment. Operates effectively on consumer hardware without requiring GPUs, suitable for running language models, and generating images and audio. Developed by Ettore Di Giacinto, it supports quick deployment via scripts or Docker, and offers a model gallery and community support on Discord. Keep informed about its advancements in decentralized inferencing and federated modes.

Explore the evolving field of prompt engineering, which optimizes prompts for language models. This guide provides an extensive range of resources like papers, lectures, and tools to expand your understanding of the capabilities and limitations of large language models in tasks such as question answering and reasoning. Discover effective prompting techniques and self-paced courses at DAIR.AI Academy. Stay informed with multi-language support and a community of over 3 million learners. Access the guide online or locally.

FlexLLMGen enables efficient large language model inference on single GPUs by optimizing memory usage through IO offloading and effective batch management. Designed for throughput-oriented tasks, it reduces costs while supporting applications in benchmarking and data processing. While less suited for small-batch operations, FlexLLMGen remains a viable solution for scalable AI deployments.

The pgai extension facilitates AI application development in PostgreSQL, supporting RAG and semantic search by integrating with extensions such as pgvector and pgvectorscale. It enables efficient data processing, including summarization and classification, through seamless LLM model integration, thus optimizing AI workflows within SQL.

Explore a versatile prompt engineering toolkit that addresses complex NLP challenges using advanced LLMs like GPT and PaLM. Enhance task prompt generation without the need for training data and take advantage of optimized workflows for tasks such as Named Entity Recognition and text classification. Utilize customizable templates and structured output for seamless business integration. Join a vibrant community for cutting-edge insights and advancements in prompt engineering.

Discover an informative guide for developers interested in understanding the core elements of large language models (LLMs). This tutorial covers the creation of a GPT-like model from scratch using PyTorch, with focus on data input pipelines, main architecture components, and pretraining techniques. It also includes instructions for loading and finetuning with pretrained weights via open-source libraries such as LitGPT. A convenient cloud setup is available for practical experimentation and smooth code execution.

Integrate AI Assistants into Django applications employing LLMs and RAG for efficient, intelligent solutions. This open-source initiative by Vinta Software helps developers combine AI features with Django, enabling effective method calls and functionalities to meet diverse needs. Access thorough documentation and participate in this dynamic environment while connecting with a supportive community. For commercial support, reach out to Vinta Software.

Tenere offers a Rust-based TUI interface for efficient use of LLMs, compatible with backends such as ChatGPT, llama.cpp, and Ollama. Key features include syntax highlighting, managing chat history, saving conversations, Vim-like keybindings, and clipboard interaction. Configure using a TOML file, with flexible installation options including binary releases, crates.io, or source builds. This allows users to tailor their language model experience with comprehensive customization capabilities.

Explore the capabilities of running GPT models in the command line environment using extended context memory and customizable prompts. This CLI bot provides extensive memory through embeddings, supports custom documents for Q&A, and facilitates smooth operation with its customizable prompts and real-time streaming responses. Compatible with Windows, Linux, and macOS, it offers features like undo, reset, and management of chat history, catering to the needs of both LLM enthusiasts and professionals.

Explore an experimental toolkit that transforms git repository documentation with Large Language Models like GPT-4. This tool quickly sets up in about 5 minutes to index and generate comprehensive codebase documentation. Use the 'doc' command for specific code queries and insights, keeping your documentation up-to-date with your CI pipeline. Open for contributions, this tool is in early development and evolving rapidly.

This project focuses on improving multimodal reasoning using advanced models such as GPT-4 and ChatGPT for enhanced scientific question answering. It employs the extensive ScienceQA dataset to drive innovations in scientific reasoning through the Chain-of-Thought approach. Widely recognized within the academic and AI sectors, it's featured in various prestigious publications. The project frequently updates with new model additions, maintaining its significance as a benchmark in AI, showcasing substantial progress in addressing intricate scientific questions.

Explore the process of creating a real-time financial advisor using LLMs with hands-on training. The course covers essential components like training, streaming, and inference pipelines, and provides insights into using services such as Alpaca, Qdrant, Comet ML, Beam, and AWS. With detailed video lectures and articles, users will gain understanding of LLMOps, QLoRA fine-tuning, real-time streaming, RAG design, and vector databases, suitable for understanding LLM deployment in financial contexts.