1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

Eko is a lightweight and flexible command-line tool for managing environment variables in your projects. It allows you to easily set, get, and delete environment variables for different environments, making it simple to manage configurations across development, staging, and production environments. With Eko, you can streamline your workflow and ensure consistency in your application settings without the need for complex setup or configuration files.

ClearML Fractional GPU is a tool designed to optimize GPU resource utilization by allowing multiple containers to run on the same GPU with driver-level memory limitation and compute time-slicing. It supports CUDA 11.x & CUDA 12.x, preventing greedy processes from grabbing the entire GPU memory. The tool offers options like Dynamic GPU Slicing, Container-based Memory Limits, and Kubernetes-based Static MIG Slicing to enhance hardware utilization and workload performance for AI development.

Starmoon is an affordable, compact AI-enabled device that can understand and respond to your emotions with empathy. It offers supportive conversations and personalized learning assistance. The device is cost-effective, voice-enabled, open-source, compact, and aims to reduce screen time. Users can assemble the device themselves using off-the-shelf components and deploy it locally for data privacy. Starmoon integrates various APIs for AI language models, speech-to-text, text-to-speech, and emotion intelligence. The hardware setup involves components like ESP32S3, microphone, amplifier, speaker, LED light, and button, along with software setup instructions for developers. The project also includes a web app, backend API, and background task dashboard for monitoring and management.

Dataflow-oriented robotic application (dora-rs) is a framework that makes creation of robotic applications fast and simple. Building a robotic application can be summed up as bringing together hardwares, algorithms, and AI models, and make them communicate with each others. At dora-rs, we try to: make integration of hardware and software easy by supporting Python, C, C++, and also ROS2. make communication low latency by using zero-copy Arrow messages. dora-rs is still experimental and you might experience bugs, but we're working very hard to make it stable as possible.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

Lance is a modern columnar data format optimized for ML workflows and datasets. It offers high-performance random access, vector search, zero-copy automatic versioning, and ecosystem integrations with Apache Arrow, Pandas, Polars, and DuckDB. Lance is designed to address the challenges of the ML development cycle, providing a unified data format for collection, exploration, analytics, feature engineering, training, evaluation, deployment, and monitoring. It aims to reduce data silos and streamline the ML development process.

Buffer of Thoughts (BoT) is a thought-augmented reasoning framework designed to enhance the accuracy, efficiency, and robustness of large language models (LLMs). It introduces a meta-buffer to store high-level thought-templates distilled from problem-solving processes, enabling adaptive reasoning for efficient problem-solving. The framework includes a buffer-manager to dynamically update the meta-buffer, ensuring scalability and stability. BoT achieves significant performance improvements on reasoning-intensive tasks and demonstrates superior generalization ability and robustness while being cost-effective compared to other methods.

**Description:** PromptFuzz is an automated tool that generates high-quality fuzz drivers for libraries via a fuzz loop constructed on mutating LLMs' prompts. The fuzz loop of PromptFuzz aims to guide the mutation of LLMs' prompts to generate programs that cover more reachable code and explore complex API interrelationships, which are effective for fuzzing. **Features:** * **Multiply LLM support** : Supports the general LLMs: Codex, Inocder, ChatGPT, and GPT4 (Currently tested on ChatGPT). * **Context-based Prompt** : Construct LLM prompts with the automatically extracted library context. * **Powerful Sanitization** : The program's syntax, semantics, behavior, and coverage are thoroughly analyzed to sanitize the problematic programs. * **Prioritized Mutation** : Prioritizes mutating the library API combinations within LLM's prompts to explore complex interrelationships, guided by code coverage. * **Fuzz Driver Exploitation** : Infers API constraints using statistics and extends fixed API arguments to receive random bytes from fuzzers. * **Fuzz engine integration** : Integrates with grey-box fuzz engine: LibFuzzer. **Benefits:** * **High branch coverage:** The fuzz drivers generated by PromptFuzz achieved a branch coverage of 40.12% on the tested libraries, which is 1.61x greater than _OSS-Fuzz_ and 1.67x greater than _Hopper_. * **Bug detection:** PromptFuzz detected 33 valid security bugs from 49 unique crashes. * **Wide range of bugs:** The fuzz drivers generated by PromptFuzz can detect a wide range of bugs, most of which are security bugs. * **Unique bugs:** PromptFuzz detects uniquely interesting bugs that other fuzzers may miss. **Usage:** 1. Build the library using the provided build scripts. 2. Export the LLM API KEY if using ChatGPT or GPT4. 3. Generate fuzz drivers using the `fuzzer` command. 4. Run the fuzz drivers using the `harness` command. 5. Deduplicate and analyze the reported crashes. **Future Works:** * **Custom LLMs suport:** Support custom LLMs. * **Close-source libraries:** Apply PromptFuzz to close-source libraries by fine tuning LLMs on private code corpus. * **Performance** : Reduce the huge time cost required in erroneous program elimination.

pgvecto.rs is a Postgres extension written in Rust that provides vector similarity search functions. It offers ultra-low-latency, high-precision vector search capabilities, including sparse vector search and full-text search. With complete SQL support, async indexing, and easy data management, it simplifies data handling. The extension supports various data types like FP16/INT8, binary vectors, and Matryoshka embeddings. It ensures system performance with production-ready features, high availability, and resource efficiency. Security and permissions are managed through easy access control. The tool allows users to create tables with vector columns, insert vector data, and calculate distances between vectors using different operators. It also supports half-precision floating-point numbers for better performance and memory usage optimization.

HuatuoGPT-o1 is a medical language model designed for advanced medical reasoning. It can identify mistakes, explore alternative strategies, and refine answers. The model leverages verifiable medical problems and a specialized medical verifier to guide complex reasoning trajectories and enhance reasoning through reinforcement learning. The repository provides access to models, data, and code for HuatuoGPT-o1, allowing users to deploy the model for medical reasoning tasks.

LLM4Decompile is an open-source large language model dedicated to decompilation of Linux x86_64 binaries, supporting GCC's O0 to O3 optimization levels. It focuses on assessing re-executability of decompiled code through HumanEval-Decompile benchmark. The tool includes models with sizes ranging from 1.3 billion to 33 billion parameters, available on Hugging Face. Users can preprocess C code into binary and assembly instructions, then decompile assembly instructions into C using LLM4Decompile. Ongoing efforts aim to expand capabilities to support more architectures and configurations, integrate with decompilation tools like Ghidra and Rizin, and enhance performance with larger training datasets.

StableToolBench is a new benchmark developed to address the instability of Tool Learning benchmarks. It aims to balance stability and reality by introducing features like Virtual API System, Solvable Queries, and Stable Evaluation System. The benchmark ensures consistency through a caching system and API simulators, filters queries based on solvability using LLMs, and evaluates model performance using GPT-4 with metrics like Solvable Pass Rate and Solvable Win Rate.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

Vespa is a platform that performs operations such as selecting a subset of data in a large corpus, evaluating machine-learned models over the selected data, organizing and aggregating it, and returning it, typically in less than 100 milliseconds, all while the data corpus is continuously changing. It has been in development for many years and is used on a number of large internet services and apps which serve hundreds of thousands of queries from Vespa per second.

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

LETSQL is a data processing library built on top of Ibis and DataFusion to write multi-engine data workflows. It is currently in development and does not have a stable release. Users can install LETSQL from PyPI and use it to connect to data sources, read data, filter, group, and aggregate data for analysis. Contributions to the project are welcome, and the library is actively maintained with support available for any issues. LETSQL heavily relies on Ibis and DataFusion for its functionality.

RepoToText is a web app that scrapes a GitHub repository and converts its files into a single organized .txt. It allows users to enter the URL of a GitHub repository and an optional documentation URL, retrieves the contents of the repository and documentation, and saves them in a structured text file. The tool can be used to interact with the repository using chatbots like GPT-4 or Claude Opus. Users can run the application with Docker, set up environment variables, choose specific file types for scraping, and copy the generated text to the clipboard. Additionally, FolderToText.py script allows converting local folders or files into a .txt file with customizable options.

The GitHub Repo to Text Converter is a web-based tool that converts GitHub repository contents into a formatted text file for Large Language Model (LLM) prompts. It streamlines the process of transforming repository data into LLM-friendly input. The tool displays the GitHub repository structure, allows users to select files/directories to include, generates a formatted text file, enables copying text to clipboard, supports downloading generated text, and works with private repositories. It ensures data security by running entirely in the browser without server-side processing.

AudioNotes is a system built on FunASR and Qwen2 that can quickly extract content from audio and video, and organize it using large models into structured markdown notes for easy reading. Users can interact with the audio and video content, install Ollama, pull models, and deploy services using Docker or locally with a PostgreSQL database. The system provides a seamless way to convert audio and video into structured notes for efficient consumption.

DOM to Semantic Markdown is a tool that converts HTML DOM to Semantic Markdown for use in Large Language Models (LLMs). It maximizes semantic information, token efficiency, and preserves metadata to enhance LLMs' processing capabilities. The tool captures rich web content structure, including semantic tags, image metadata, table structures, and link destinations. It offers customizable conversion options and supports both browser and Node.js environments.

Scrape It Now is a versatile tool for scraping websites with features like decoupled architecture, CLI functionality, idempotent operations, and content storage options. The tool includes a scraper component for efficient scraping, ad blocking, link detection, markdown extraction, dynamic content loading, and anonymity features. It also offers an indexer component for creating AI search indexes, chunking content, embedding chunks, and enabling semantic search. The tool supports various configurations for Azure services and local storage, providing flexibility and scalability for web scraping and indexing tasks.

Perplexica is an open-source AI-powered search engine that utilizes advanced machine learning algorithms to provide clear answers with sources cited. It offers various modes like Copilot Mode, Normal Mode, and Focus Modes for specific types of questions. Perplexica ensures up-to-date information by using SearxNG metasearch engine. It also features image and video search capabilities and upcoming features include finalizing Copilot Mode and adding Discover and History Saving features.

KULLM (구름) is a Korean Large Language Model developed by Korea University NLP & AI Lab and HIAI Research Institute. It is based on the upstage/SOLAR-10.7B-v1.0 model and has been fine-tuned for instruction. The model has been trained on 8×A100 GPUs and is capable of generating responses in Korean language. KULLM exhibits hallucination and repetition phenomena due to its decoding strategy. Users should be cautious as the model may produce inaccurate or harmful results. Performance may vary in benchmarks without a fixed system prompt.

MMMU is a benchmark designed to evaluate multimodal models on college-level subject knowledge tasks, covering 30 subjects and 183 subfields with 11.5K questions. It focuses on advanced perception and reasoning with domain-specific knowledge, challenging models to perform tasks akin to those faced by experts. The evaluation of various models highlights substantial challenges, with room for improvement to stimulate the community towards expert artificial general intelligence (AGI).

1filellm is a command-line data aggregation tool designed for LLM ingestion. It aggregates and preprocesses data from various sources into a single text file, facilitating the creation of information-dense prompts for large language models. The tool supports automatic source type detection, handling of multiple file formats, web crawling functionality, integration with Sci-Hub for research paper downloads, text preprocessing, and token count reporting. Users can input local files, directories, GitHub repositories, pull requests, issues, ArXiv papers, YouTube transcripts, web pages, Sci-Hub papers via DOI or PMID. The tool provides uncompressed and compressed text outputs, with the uncompressed text automatically copied to the clipboard for easy pasting into LLMs.

GPT Researcher is an autonomous agent designed for comprehensive online research on a variety of tasks. It can produce detailed, factual, and unbiased research reports with customization options. The tool addresses issues of speed, determinism, and reliability by leveraging parallelized agent work. The main idea involves running 'planner' and 'execution' agents to generate research questions, seek related information, and create research reports. GPT Researcher optimizes costs and completes tasks in around 3 minutes. Features include generating long research reports, aggregating web sources, an easy-to-use web interface, scraping web sources, and exporting reports to various formats.

ChatTTS is a generative speech model optimized for dialogue scenarios, providing natural and expressive speech synthesis with fine-grained control over prosodic features. It supports multiple speakers and surpasses most open-source TTS models in terms of prosody. The model is trained with 100,000+ hours of Chinese and English audio data, and the open-source version on HuggingFace is a 40,000-hour pre-trained model without SFT. The roadmap includes open-sourcing additional features like VQ encoder, multi-emotion control, and streaming audio generation. The tool is intended for academic and research use only, with precautions taken to limit potential misuse.

HebTTS is a language modeling approach to diacritic-free Hebrew text-to-speech (TTS) system. It addresses the challenge of accurately mapping text to speech in Hebrew by proposing a language model that operates on discrete speech representations and is conditioned on a word-piece tokenizer. The system is optimized using weakly supervised recordings and outperforms diacritic-based Hebrew TTS systems in terms of content preservation and naturalness of generated speech.

This repository is a collection of research lectures and experience sharing posts from frontline researchers in the field of AI. It aims to help individuals upgrade their research skills and knowledge through insightful talks and experiences shared by experts. The content covers various topics such as evaluating research papers, choosing research directions, research methodologies, and tips for writing high-quality scientific papers. The repository also includes discussions on academic career paths, research ethics, and the emotional aspects of research work. Overall, it serves as a valuable resource for individuals interested in advancing their research capabilities in the field of AI.