The 'agents' repository is a comprehensive collection of 83 specialized AI subagents for Claude Code, providing domain-specific expertise across software development, infrastructure, and business operations. Each subagent incorporates current industry best practices, production-ready patterns, deep domain expertise, modern technology stacks, and optimized model selection based on task complexity.

Production-ready slash commands for Claude Code that accelerate development through intelligent automation and multi-agent orchestration. Contains 52 commands organized into workflows and tools categories. Workflows orchestrate complex tasks with multiple agents, while tools provide focused functionality for specific development tasks. Commands can be used with prefixes for organization or flattened for convenience. Best practices include using workflows for complex tasks and tools for specific scopes, chaining commands strategically, and providing detailed context for effective usage.

LLM-Finetune is a repository for fine-tuning language models for various NLP tasks such as text classification and named entity recognition. It provides instructions and scripts for training and inference using models like Qwen2-VL and GLM4. The repository also includes datasets for tasks like text classification, named entity recognition, and multimodal tasks. Users can easily prepare the environment, download datasets, train models, and perform inference using the provided scripts and notebooks. Additionally, the repository references SwanLab, an AI training record, analysis, and visualization tool.

Spice is a portable runtime written in Rust that offers developers a unified SQL interface to materialize, accelerate, and query data from any database, data warehouse, or data lake. It connects, fuses, and delivers data to applications, machine-learning models, and AI-backends, functioning as an application-specific, tier-optimized Database CDN. Built with industry-leading technologies such as Apache DataFusion, Apache Arrow, Apache Arrow Flight, SQLite, and DuckDB. Spice makes it fast and easy to query data from one or more sources using SQL, co-locating a managed dataset with applications or machine learning models, and accelerating it with Arrow in-memory, SQLite/DuckDB, or attached PostgreSQL for fast, high-concurrency, low-latency queries.

This repository contains examples of how to use the Hume API with different frameworks and languages. It includes examples for Empathic Voice Interface (EVI) and Expression Measurement API. The EVI examples cover custom language models, modal, Next.js integration, Vue integration, Hume Python SDK, and React integration. The Expression Measurement API examples include models for face, language, burst, and speech, with implementations in Python and Typescript using frameworks like Next.js.

No-cost-ai is a repository dedicated to providing a comprehensive list of free AI models and tools for developers, researchers, and curious builders. It serves as a living index for accessing state-of-the-art AI models without any cost. The repository includes information on various AI applications such as chat interfaces, media generation, voice and music tools, AI IDEs, and developer APIs and platforms. Users can find links to free models, their limits, and usage instructions. Contributions to the repository are welcome, and users are advised to use the listed services at their own risk due to potential changes in models, limitations, and reliability of free services.

hCaptcha Challenger is a tool designed to gracefully face hCaptcha challenges using a multimodal large language model. It does not rely on Tampermonkey scripts or third-party anti-captcha services, instead implementing interfaces for 'AI vs AI' scenarios. The tool supports various challenge types such as image labeling, drag and drop, and advanced tasks like self-supervised challenges and Agentic Workflow. Users can access documentation in multiple languages and leverage resources for tasks like model training, dataset annotation, and model upgrading. The tool aims to enhance user experience in handling hCaptcha challenges with innovative AI capabilities.

PredictorLLM is an advanced trading agent framework that utilizes large language models to automate trading in financial markets. It includes a profiling module to establish agent characteristics, a layered memory module for retaining and prioritizing financial data, and a decision-making module to convert insights into trading strategies. The framework mimics professional traders' behavior, surpassing human limitations in data processing and continuously evolving to adapt to market conditions for superior investment outcomes.

llm-export is a tool for exporting llm models to onnx and mnn formats. It has features such as passing onnxruntime correctness tests, optimizing the original code to support dynamic shapes, reducing constant parts, optimizing onnx models using OnnxSlim for performance improvement, and exporting lora weights to onnx and mnn formats. Users can clone the project locally, clone the desired LLM project locally, and use LLMExporter to export the model. The tool supports various export options like exporting the entire model as one onnx model, exporting model segments as multiple models, exporting model vocabulary to a text file, exporting specific model layers like Embedding and lm_head, testing the model with queries, validating onnx model consistency with onnxruntime, converting onnx models to mnn models, and more. Users can specify export paths, skip optimization steps, and merge lora weights before exporting.

Beet is a collection of crates for authoring and running web pages, games and AI behaviors. It includes crates like `beet_flow` for scenes-as-control-flow bevy library, `beet_spatial` for spatial behaviors, `beet_ml` for machine learning, `beet_sim` for simulation tooling, `beet_rsx` for authoring tools for html and bevy, and `beet_router` for file-based router for web docs. The `beet` crate acts as a base crate that re-exports sub-crates based on feature flags, similar to the `bevy` crate structure.

SecReport is a platform for collaborative information security penetration testing report writing and exporting, powered by ChatGPT. It standardizes penetration testing processes, allows multiple users to edit reports, offers custom export templates, generates vulnerability summaries and fix suggestions using ChatGPT, and provides APP security compliance testing reports. The tool aims to streamline the process of creating and managing security reports for penetration testing and compliance purposes.

BizFinBench is a benchmark tool designed for evaluating large language models (LLMs) in logic-heavy and precision-critical domains such as finance. It comprises over 100,000 bilingual financial questions rooted in real-world business scenarios. The tool covers five dimensions: numerical calculation, reasoning, information extraction, prediction recognition, and knowledge-based question answering, mapped to nine fine-grained categories. BizFinBench aims to assess the capacity of LLMs in real-world financial scenarios and provides insights into their strengths and limitations.

This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).

The 'airport' repository provides free Clash Meta nodes sourced from the internet, with testing every 6 hours to ensure quality and low latency. It includes features such as node deduplication, regional renaming, and geographical grouping.

YuLan-Mini is a lightweight language model with 2.4 billion parameters that achieves performance comparable to industry-leading models despite being pre-trained on only 1.08T tokens. It excels in mathematics and code domains. The repository provides pre-training resources, including data pipeline, optimization methods, and annealing approaches. Users can pre-train their own language models, perform learning rate annealing, fine-tune the model, research training dynamics, and synthesize data. The team behind YuLan-Mini is AI Box at Renmin University of China. The code is released under the MIT License with future updates on model weights usage policies. Users are advised on potential safety concerns and ethical use of the model.

This is the official repository for the ACM CCS 2024 paper 'Do Anything Now': Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models. The project employs a new framework called JailbreakHub to conduct the first measurement study on jailbreak prompts in the wild, collecting 15,140 prompts from December 2022 to December 2023, including 1,405 jailbreak prompts. The dataset serves as the largest collection of in-the-wild jailbreak prompts. The repository contains examples of harmful language and is intended for research purposes only.

The 'agents' repository is a comprehensive collection of 83 specialized AI subagents for Claude Code, providing domain-specific expertise across software development, infrastructure, and business operations. Each subagent incorporates current industry best practices, production-ready patterns, deep domain expertise, modern technology stacks, and optimized model selection based on task complexity.

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.