This repository contains the official code for the paper 'Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning'. It provides tools for point cloud segmentation experiments based on different settings, tree parameters extraction, handling large point clouds through tiling, predicting, and merging workflows. Additionally, it includes commands for training, testing, and evaluating the models, along with the necessary datasets and pretrained models.

Upgini is an intelligent data search engine with a Python library that helps users find and add relevant features to their ML pipeline from various public, community, and premium external data sources. It automates the optimization of connected data sources by generating an optimal set of machine learning features using large language models, GraphNNs, and recurrent neural networks. The tool aims to simplify feature search and enrichment for external data to make it a standard approach in machine learning pipelines. It democratizes access to data sources for the data science community.

This repository contains a vulnerability mining engine based on GPT technology. The engine is designed to identify logic vulnerabilities in code by utilizing task-driven prompts. It does not require prior knowledge or fine-tuning and focuses on prompt design rather than model design. The tool is effective in real-world projects and should not be used for academic vulnerability testing. It supports scanning projects in various languages, with current support for Solidity. The engine is configured through prompts and environment settings, enabling users to scan for vulnerabilities in their codebase. Future updates aim to optimize code structure, add more language support, and enhance usability through command line mode. The tool has received a significant audit bounty of $50,000+ as of May 2024.

This is a vulnerability mining engine purely based on GPT, requiring no prior knowledge base, no fine-tuning, yet its effectiveness can overwhelmingly surpass most of the current related research. The core idea revolves around being task-driven, not question-driven, driven by prompts, not by code, and focused on prompt design, not model design. The essence is encapsulated in one word: deception. It is a type of code understanding logic vulnerability mining that fully stimulates the capabilities of GPT, suitable for real actual projects.

Parsera is a lightweight Python library designed for scraping websites using LLMs. It offers simplicity and efficiency by minimizing token usage, enhancing speed, and reducing costs. Users can easily set up and run the tool to extract specific elements from web pages, generating JSON output with relevant data. Additionally, Parsera supports integration with various chat models, such as Azure, expanding its functionality and customization options for web scraping tasks.

llama.vim is a plugin that provides local LLM-assisted text completion for Vim users. It offers features such as auto-suggest on cursor movement, manual suggestion toggling, suggestion acceptance with Tab and Shift+Tab, control over text generation time, context configuration, ring context with chunks from open and edited files, and performance stats display. The plugin requires a llama.cpp server instance to be running and supports FIM-compatible models. It aims to be simple, lightweight, and provide high-quality and performant local FIM completions even on consumer-grade hardware.

Mergekit is a toolkit for merging pre-trained language models. It uses an out-of-core approach to perform unreasonably elaborate merges in resource-constrained situations. Merges can be run entirely on CPU or accelerated with as little as 8 GB of VRAM. Many merging algorithms are supported, with more coming as they catch my attention.

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

SWE-RL is the official codebase for the paper 'SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution'. It is the first approach to scale reinforcement learning based LLM reasoning for real-world software engineering, leveraging open-source software evolution data and rule-based rewards. The code provides prompt templates and the implementation of the reward function based on sequence similarity. Agentless Mini, a part of SWE-RL, builds on top of Agentless with improvements like fast async inference, code refactoring for scalability, and support for using multiple reproduction tests for reranking. The tool can be used for localization, repair, and reproduction test generation in software engineering tasks.

Cache-Augmented Generation (CAG) is an alternative paradigm to Retrieval-Augmented Generation (RAG) that eliminates real-time retrieval delays and errors by preloading all relevant resources into the model's context. CAG leverages extended context windows of large language models (LLMs) to generate responses directly, providing reduced latency, improved reliability, and simplified design. While CAG has limitations in knowledge size and context length, advancements in LLMs are addressing these issues, making CAG a practical and scalable alternative for complex applications.

Gemini SRT Translator is a tool that utilizes Google Generative AI to provide accurate and efficient translations for subtitle files. Users can customize translation settings, such as model name and batch size, and list available models from the Gemini API. The tool requires a free API key from Google AI Studio for setup and offers features like translating subtitles to a specified target language and resuming partial translations. Users can further customize translation settings with optional parameters like gemini_api_key2, output_file, start_line, model_name, batch_size, and more.

HippoRAG is a novel retrieval augmented generation (RAG) framework inspired by the neurobiology of human long-term memory that enables Large Language Models (LLMs) to continuously integrate knowledge across external documents. It provides RAG systems with capabilities that usually require a costly and high-latency iterative LLM pipeline for only a fraction of the computational cost. The tool facilitates setting up retrieval corpus, indexing, and retrieval processes for LLMs, offering flexibility in choosing different online LLM APIs or offline LLM deployments through LangChain integration. Users can run retrieval on pre-defined queries or integrate directly with the HippoRAG API. The tool also supports reproducibility of experiments and provides data, baselines, and hyperparameter tuning scripts for research purposes.

The GFM-RAG is a graph foundation model-powered pipeline that combines graph neural networks to reason over knowledge graphs and retrieve relevant documents for question answering. It features a knowledge graph index, efficiency in multi-hop reasoning, generalizability to unseen datasets, transferability for fine-tuning, compatibility with agent-based frameworks, and interpretability of reasoning paths. The tool can be used for conducting retrieval and question answering tasks using pre-trained models or fine-tuning on custom datasets.

SynCode is a novel framework for the grammar-guided generation of Large Language Models (LLMs) that ensures syntactically valid output with respect to defined Context-Free Grammar (CFG) rules. It supports general-purpose programming languages like Python, Go, SQL, JSON, and more, allowing users to define custom grammars using EBNF syntax. The tool compares favorably to other constrained decoders and offers features like fast grammar-guided generation, compatibility with HuggingFace Language Models, and the ability to work with various decoding strategies.

ChatWeb is a tool that can crawl web pages, extract text from PDF, DOCX, TXT files, and generate an embedded summary. It can answer questions based on text content using chatAPI and embeddingAPI based on GPT3.5. The tool calculates similarity scores between text vectors to generate summaries, performs nearest neighbor searches, and designs prompts to answer user questions. It aims to extract relevant content from text and provide accurate search results based on keywords. ChatWeb supports various modes, languages, and settings, including temperature control and PostgreSQL integration.

This repository contains Airflow DAGs for extracting, transforming, and loading (ETL) data from the Ethereum blockchain into BigQuery. The DAGs use the Google Cloud Platform (GCP) services, including BigQuery, Cloud Storage, and Cloud Composer, to automate the ETL process. The repository also includes scripts for setting up the GCP environment and running the DAGs locally.

This repository contains the official code for the paper 'Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning'. It provides tools for point cloud segmentation experiments based on different settings, tree parameters extraction, handling large point clouds through tiling, predicting, and merging workflows. Additionally, it includes commands for training, testing, and evaluating the models, along with the necessary datasets and pretrained models.

Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

AgentCloud is an open-source platform that enables companies to build and deploy private LLM chat apps, empowering teams to securely interact with their data. It comprises three main components: Agent Backend, Webapp, and Vector Proxy. To run this project locally, clone the repository, install Docker, and start the services. The project is licensed under the GNU Affero General Public License, version 3 only. Contributions and feedback are welcome from the community.

This framework generates fuzz targets for real-world `C`/`C++` projects with various Large Language Models (LLM) and benchmarks them via the `OSS-Fuzz` platform. It manages to successfully leverage LLMs to generate valid fuzz targets (which generate non-zero coverage increase) for 160 C/C++ projects. The maximum line coverage increase is 29% from the existing human-written targets.

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.

The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

The Azure-Analytics-and-AI-Engagement repository provides packaged Industry Scenario DREAM Demos with ARM templates (Containing a demo web application, Power BI reports, Synapse resources, AML Notebooks etc.) that can be deployed in a customer’s subscription using the CAPE tool within a matter of few hours. Partners can also deploy DREAM Demos in their own subscriptions using DPoC.