The Classical Language Toolkit (CLTK) is a Python library that provides natural language processing (NLP) capabilities for pre-modern languages. It offers a modular processing pipeline with pre-configured defaults and supports almost 20 languages. Users can install the latest version using pip and access detailed documentation on the official website. The toolkit is designed to meet the unique needs of researchers working with historical languages, filling a void in the NLP landscape that often neglects non-spoken languages and different research goals.

GOLEM is an open-source AI framework focused on optimization and learning of structured graph-based models using meta-heuristic methods. It emphasizes the potential of meta-heuristics in complex problem spaces where gradient-based methods are not suitable, and the importance of structured models in various problem domains. The framework offers features like structured model optimization, metaheuristic methods, multi-objective optimization, constrained optimization, extensibility, interpretability, and reproducibility. It can be applied to optimization problems represented as directed graphs with defined fitness functions. GOLEM has applications in areas like AutoML, Bayesian network structure search, differential equation discovery, geometric design, and neural architecture search. The project structure includes packages for core functionalities, adapters, graph representation, optimizers, genetic algorithms, utilities, serialization, visualization, examples, and testing. Contributions are welcome, and the project is supported by ITMO University's Research Center Strong Artificial Intelligence in Industry.

Instructor is a Typescript library for structured extraction in Typescript, powered by llms, designed for simplicity, transparency, and control. It stands out for its simplicity, transparency, and user-centric design. Whether you're a seasoned developer or just starting out, you'll find Instructor's approach intuitive and steerable.

xLSTM is a new Recurrent Neural Network architecture based on ideas of the original LSTM. Through Exponential Gating with appropriate normalization and stabilization techniques and a new Matrix Memory it overcomes the limitations of the original LSTM and shows promising performance on Language Modeling when compared to Transformers or State Space Models. The package is based on PyTorch and was tested for versions >=1.8. For the CUDA version of xLSTM, you need Compute Capability >= 8.0. The xLSTM tool provides two main components: xLSTMBlockStack for non-language applications or integrating in other architectures, and xLSTMLMModel for language modeling or other token-based applications.

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

Fractl is a programming language designed for generative AI, making it easier for developers to work with AI-generated code. It features a data-oriented and declarative syntax, making it a better fit for generative AI-powered code generation. Fractl also bridges the gap between traditional programming and visual building, allowing developers to use multiple ways of building, including traditional coding, visual development, and code generation with generative AI. Key concepts in Fractl include a graph-based hierarchical data model, zero-trust programming, declarative dataflow, resolvers, interceptors, and entity-graph-database mapping.

Empower Functions is a family of large language models (LLMs) that provide GPT-4 level capabilities for real-world 'tool using' use cases. These models offer compatibility support to be used as drop-in replacements, enabling interactions with external APIs by recognizing when a function needs to be called and generating JSON containing necessary arguments based on user inputs. This capability is crucial for building conversational agents and applications that convert natural language into API calls, facilitating tasks such as weather inquiries, data extraction, and interactions with knowledge bases. The models can handle multi-turn conversations, choose between tools or standard dialogue, ask for clarification on missing parameters, integrate responses with tool outputs in a streaming fashion, and efficiently execute multiple functions either in parallel or sequentially with dependencies.

Swarms provides simple, reliable, and agile tools to create your own Swarm tailored to your specific needs. Currently, Swarms is being used in production by RBC, John Deere, and many AI startups.

aiosqlite is a Python library that provides a friendly, async interface to SQLite databases. It replicates the standard sqlite3 module but with async versions of all the standard connection and cursor methods, along with context managers for automatically closing connections and cursors. It allows interaction with SQLite databases on the main AsyncIO event loop without blocking execution of other coroutines while waiting for queries or data fetches. The library also replicates most of the advanced features of sqlite3, such as row factories and total changes tracking.

Superpipe is a lightweight framework designed for building, evaluating, and optimizing data transformation and data extraction pipelines using LLMs. It allows users to easily combine their favorite LLM libraries with Superpipe's building blocks to create pipelines tailored to their unique data and use cases. The tool facilitates rapid prototyping, evaluation, and optimization of end-to-end pipelines for tasks such as classification and evaluation of job departments based on work history. Superpipe also provides functionalities for evaluating pipeline performance, optimizing parameters for cost, accuracy, and speed, and conducting grid searches to experiment with different models and prompts.

Bosquet is a tool designed for LLMOps in large language model-based applications. It simplifies building AI applications by managing LLM and tool services, integrating with Selmer templating library for prompt templating, enabling prompt chaining and composition with Pathom graph processing, defining agents and tools for external API interactions, handling LLM memory, and providing features like call response caching. The tool aims to streamline the development process for AI applications that require complex prompt templates, memory management, and interaction with external systems.

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

AI is an open-source Swift framework for interfacing with generative AI. It provides functionalities for text completions, image-to-text vision, function calling, DALLE-3 image generation, audio transcription and generation, and text embeddings. The framework supports multiple AI models from providers like OpenAI, Anthropic, Mistral, Groq, and ElevenLabs. Users can easily integrate AI capabilities into their Swift projects using AI framework.

AI Hospital is a research repository focusing on the interactive evaluation and collaboration of Large Language Models (LLMs) as intern doctors for clinical diagnosis. The repository includes a simulation module tailored for various medical roles, introduces the Multi-View Medical Evaluation (MVME) Benchmark, provides dialog history documents of LLMs, replication instructions, performance evaluation, and guidance for creating intern doctor agents. The collaborative diagnosis with LLMs emphasizes dispute resolution. The study was authored by Zhihao Fan, Jialong Tang, Wei Chen, Siyuan Wang, Zhongyu Wei, Jun Xie, Fei Huang, and Jingren Zhou.

Zshot is a highly customizable framework for performing Zero and Few shot named entity and relationships recognition. It can be used for mentions extraction, wikification, zero and few shot named entity recognition, zero and few shot named relationship recognition, and visualization of zero-shot NER and RE extraction. The framework consists of two main components: the mentions extractor and the linker. There are multiple mentions extractors and linkers available, each serving a specific purpose. Zshot also includes a relations extractor and a knowledge extractor for extracting relations among entities and performing entity classification. The tool requires Python 3.6+ and dependencies like spacy, torch, transformers, evaluate, and datasets for evaluation over datasets like OntoNotes. Optional dependencies include flair and blink for additional functionalities. Zshot provides examples, tutorials, and evaluation methods to assess the performance of the components.

The Classical Language Toolkit (CLTK) is a Python library that provides natural language processing (NLP) capabilities for pre-modern languages. It offers a modular processing pipeline with pre-configured defaults and supports almost 20 languages. Users can install the latest version using pip and access detailed documentation on the official website. The toolkit is designed to meet the unique needs of researchers working with historical languages, filling a void in the NLP landscape that often neglects non-spoken languages and different research goals.

LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

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.

Khoj is an open-source, personal AI assistant that extends your capabilities by creating always-available AI agents. You can share your notes and documents to extend your digital brain, and your AI agents have access to the internet, allowing you to incorporate real-time information. Khoj is accessible on Desktop, Emacs, Obsidian, Web, and Whatsapp, and you can share PDF, markdown, org-mode, notion files, and GitHub repositories. You'll get fast, accurate semantic search on top of your docs, and your agents can create deeply personal images and understand your speech. Khoj is self-hostable and always will be.

Infinity is an AI-native database designed for LLM applications, providing incredibly fast full-text and vector search capabilities. It supports a wide range of data types, including vectors, full-text, and structured data, and offers a fused search feature that combines multiple embeddings and full text. Infinity is easy to use, with an intuitive Python API and a single-binary architecture that simplifies deployment. It achieves high performance, with 0.1 milliseconds query latency on million-scale vector datasets and up to 15K QPS.

SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.

Langfun is a Python library that aims to make language models (LM) fun to work with. It enables a programming model that flows naturally, resembling the human thought process. Langfun emphasizes the reuse and combination of language pieces to form prompts, thereby accelerating innovation. Unlike other LM frameworks, which feed program-generated data into the LM, langfun takes a distinct approach: It starts with natural language, allowing for seamless interactions between language and program logic, and concludes with natural language and optional structured output. Consequently, langfun can aptly be described as Language as functions, capturing the core of its methodology.

LiteLLM is a tool that allows you to call all LLM APIs using the OpenAI format. This includes Bedrock, Huggingface, VertexAI, TogetherAI, Azure, OpenAI, and more. LiteLLM manages translating inputs to provider's `completion`, `embedding`, and `image_generation` endpoints, providing consistent output, and retry/fallback logic across multiple deployments. It also supports setting budgets and rate limits per project, api key, and model.

The ASReview project implements active learning for systematic reviews, utilizing AI-aided pipelines to assist in finding relevant texts for search tasks. It accelerates the screening of textual data with minimal human input, saving time and increasing output quality. The software offers three modes: Oracle for interactive screening, Exploration for teaching purposes, and Simulation for evaluating active learning models. ASReview LAB is designed to support decision-making in any discipline or industry by improving efficiency and transparency in screening large amounts of textual data.

The NewEraAI-Papers repository provides links to collections of influential and interesting research papers from top AI conferences, along with open-source code to promote reproducibility and provide detailed implementation insights beyond the scope of the article. Users can stay up to date with the latest advances in AI research by exploring this repository. Contributions to improve the completeness of the list are welcomed, and users can create pull requests, open issues, or contact the repository owner via email to enhance the repository further.

The Classical Language Toolkit (CLTK) is a Python library that provides natural language processing (NLP) capabilities for pre-modern languages. It offers a modular processing pipeline with pre-configured defaults and supports almost 20 languages. Users can install the latest version using pip and access detailed documentation on the official website. The toolkit is designed to meet the unique needs of researchers working with historical languages, filling a void in the NLP landscape that often neglects non-spoken languages and different research goals.

The 'Conference-Acceptance-Rate' repository provides acceptance rates for top-tier AI-related conferences in the fields of Natural Language Processing, Computational Linguistics, Computer Vision, Pattern Recognition, Machine Learning, Learning Theory, Artificial Intelligence, Data Mining, Information Retrieval, Speech Processing, and Signal Processing. The data includes acceptance rates for long papers and short papers over several years for each conference, allowing researchers to track trends and make informed decisions about where to submit their work.

PDFToChat is a tool that allows users to chat with their PDF documents in seconds. It is powered by Together AI and Pinecone, utilizing a tech stack including Next.js, Mixtral, M2 Bert, LangChain.js, MongoDB Atlas, Bytescale, Vercel, Clerk, and Tailwind CSS. Users can deploy the tool to Vercel or any other host by setting up Together.ai, MongoDB Atlas database, Bytescale, Clerk, and Vercel. The tool enables users to interact with PDFs through chat, with future tasks including adding features like trash icon for deleting PDFs, exploring different embedding models, implementing auto scrolling, improving replies, benchmarking accuracy, researching chunking and retrieval best practices, adding demo video, upgrading to Next.js 14, adding analytics, customizing tailwind prose, saving chats in postgres DB, compressing large PDFs, implementing custom uploader, session tracking, error handling, and support for images in PDFs.

Awesome LLM Strawberry is a collection of research papers and blogs related to OpenAI Strawberry(o1) and Reasoning. The repository is continuously updated to track the frontier of LLM Reasoning.

The `Call-for-Reviewers` repository aims to collect the latest 'call for reviewers' links from various top CS/ML/AI conferences/journals. It provides an opportunity for individuals in the computer/ machine learning/ artificial intelligence fields to gain review experience for applying for NIW/H1B/EB1 or enhancing their CV. The repository helps users stay updated with the latest research trends and engage with the academic community.

Language Models is a Python package that provides building blocks to explore large language models with as little as 512MB of RAM. It simplifies the usage of large language models from Python, ensuring all inference is performed locally to keep data private. The package includes features such as text completions, chat capabilities, code completions, external text retrieval, semantic search, and more. It outperforms Hugging Face transformers for CPU inference and offers sensible default models with varying parameters based on memory constraints. The package is suitable for learners and educators exploring the intersection of large language models with modern software development.