SolarLLMZeroToAll is a comprehensive repository that provides a step-by-step guide and resources for learning and implementing Solar Longitudinal Learning Machines (SolarLLM) from scratch. The repository covers various aspects of SolarLLM, including theory, implementation, and applications, making it suitable for beginners and advanced users interested in solar energy forecasting and machine learning. The materials include detailed explanations, code examples, datasets, and visualization tools to facilitate understanding and practical implementation of SolarLLM models.

RD-Agent is a tool designed to automate critical aspects of industrial R&D processes, focusing on data-driven scenarios to streamline model and data development. It aims to propose new ideas ('R') and implement them ('D') automatically, leading to solutions of significant industrial value. The tool supports scenarios like Automated Quantitative Trading, Data Mining Agent, Research Copilot, and more, with a framework to push the boundaries of research in data science. Users can create a Conda environment, install the RDAgent package from PyPI, configure GPT model, and run various applications for tasks like quantitative trading, model evolution, medical prediction, and more. The tool is intended to enhance R&D processes and boost productivity in industrial settings.

Datasets is a repository that provides a collection of various datasets for machine learning and data analysis projects. It includes datasets in different formats such as CSV, JSON, and Excel, covering a wide range of topics including finance, healthcare, marketing, and more. The repository aims to help data scientists, researchers, and students access high-quality datasets for training models, conducting experiments, and exploring data analysis techniques.

MCP Context Forge is a powerful tool for generating context-aware data for machine learning models. It provides functionalities to create diverse datasets with contextual information, enhancing the performance of AI algorithms. The tool supports various data formats and allows users to customize the context generation process easily. With MCP Context Forge, users can efficiently prepare training data for tasks requiring contextual understanding, such as sentiment analysis, recommendation systems, and natural language processing.

ML-Retreat is a comprehensive machine learning library designed to simplify and streamline the process of building and deploying machine learning models. It provides a wide range of tools and utilities for data preprocessing, model training, evaluation, and deployment. With ML-Retreat, users can easily experiment with different algorithms, hyperparameters, and feature engineering techniques to optimize their models. The library is built with a focus on scalability, performance, and ease of use, making it suitable for both beginners and experienced machine learning practitioners.

The Atomic Agents framework is a modular and extensible tool designed for creating powerful applications. It leverages Pydantic for data validation and serialization. The framework follows the principles of Atomic Design, providing small and single-purpose components that can be combined. It integrates with Instructor for AI agent architecture and supports various APIs like Cohere, Anthropic, and Gemini. The tool includes documentation, examples, and testing features to ensure smooth development and usage.

The GPT Server project leverages the basic capabilities of FastChat to provide the capabilities of an openai server. It perfectly adapts more models, optimizes models with poor compatibility in FastChat, and supports loading vllm, LMDeploy, and hf in various ways. It also supports all sentence_transformers compatible semantic vector models, including Chat templates with function roles, Function Calling (Tools) capability, and multi-modal large models. The project aims to reduce the difficulty of model adaptation and project usage, making it easier to deploy the latest models with minimal code changes.

A drill of scientific methods, processes, algorithms, and systems to build stories & models. An in-depth learning resource for humans. This repository is designed for individuals aiming to excel in the field of Data and AI, providing video sessions and text content for learning. It caters to those in leadership positions, professionals, and students, emphasizing the need for dedicated effort to achieve excellence in the tech field. The content covers various topics with a focus on practical application.

DeepFlow is an open-source project that provides deep observability for complex cloud-native and AI applications. It offers Zero Code data collection with eBPF for metrics, distributed tracing, request logs, and function profiling. DeepFlow is integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

This repository contains a Geographic Information System (GIS) tool for performing Minimum Cumulative Path (MCP) analysis. The tool allows users to calculate the optimal path that minimizes cumulative cost between multiple locations on a map. It is particularly useful for urban planning, transportation route optimization, and environmental impact assessment. The tool supports various cost functions such as distance, travel time, and resource consumption, providing flexibility for different applications. Users can visualize the results on interactive maps and export the analysis outputs for further processing. The tool is implemented in Python and leverages popular GIS libraries such as GeoPandas and NetworkX for efficient spatial analysis.

The Alignment Handbook provides robust training recipes for continuing pretraining and aligning language models with human and AI preferences. It includes techniques such as continued pretraining, supervised fine-tuning, reward modeling, rejection sampling, and direct preference optimization (DPO). The handbook aims to fill the gap in public resources on training these models, collecting data, and measuring metrics for optimal downstream performance.

pdr_ai_v2 is a Python library for implementing machine learning algorithms and models. It provides a wide range of tools and functionalities for data preprocessing, model training, evaluation, and deployment. The library is designed to be user-friendly and efficient, making it suitable for both beginners and experienced data scientists. With pdr_ai_v2, users can easily build and deploy machine learning models for various applications, such as classification, regression, clustering, and more.

MemOS is an operating system for Large Language Models (LLMs) that enhances them with long-term memory capabilities. It allows LLMs to store, retrieve, and manage information, enabling more context-aware, consistent, and personalized interactions. MemOS provides Memory-Augmented Generation (MAG) with a unified API for memory operations, a Modular Memory Architecture (MemCube) for easy integration and management of different memory types, and multiple memory types including Textual Memory, Activation Memory, and Parametric Memory. It is extensible, allowing users to customize memory modules, data sources, and LLM integrations. MemOS demonstrates significant improvements over baseline memory solutions in multiple reasoning tasks, with a notable improvement in temporal reasoning accuracy compared to the OpenAI baseline.

llm_rl is a repository that combines llm (language model) and rl (reinforcement learning) techniques. It likely focuses on using language models in reinforcement learning tasks, such as natural language understanding and generation. The repository may contain implementations of algorithms that leverage both llm and rl to improve performance in various tasks. Developers interested in exploring the intersection of language models and reinforcement learning may find this repository useful for research and experimentation.

FLAME is a lightweight and efficient deep learning framework designed for edge devices. It provides a simple and user-friendly interface for developing and deploying deep learning models on resource-constrained devices. With FLAME, users can easily build and optimize neural networks for tasks such as image classification, object detection, and natural language processing. The framework supports various neural network architectures and optimization techniques, making it suitable for a wide range of applications in the field of edge computing.

RAFT (Reusable Accelerated Functions and Tools) is a C++ header-only template library with an optional shared library that contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

SolarLLMZeroToAll is a comprehensive repository that provides a step-by-step guide and resources for learning and implementing Solar Longitudinal Learning Machines (SolarLLM) from scratch. The repository covers various aspects of SolarLLM, including theory, implementation, and applications, making it suitable for beginners and advanced users interested in solar energy forecasting and machine learning. The materials include detailed explanations, code examples, datasets, and visualization tools to facilitate understanding and practical implementation of SolarLLM models.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

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.