OSM-AI-helper is a Blueprint by Mozilla.ai designed to assist users in mapping features in OpenStreetMap using object detection and image segmentation models. It provides tools for identifying and mapping various features, such as swimming pools, in OpenStreetMap. Users can also create custom datasets and fine-tune models for different use cases. The project is licensed under the AGPL-3.0 License and welcomes contributions from the community.

Helicone is an open-source observability platform designed for Language Learning Models (LLMs). It logs requests to OpenAI in a user-friendly UI, offers caching, rate limits, and retries, tracks costs and latencies, provides a playground for iterating on prompts and chat conversations, supports collaboration, and will soon have APIs for feedback and evaluation. The platform is deployed on Cloudflare and consists of services like Web (NextJs), Worker (Cloudflare Workers), Jawn (Express), Supabase, and ClickHouse. Users can interact with Helicone locally by setting up the required services and environment variables. The platform encourages contributions and provides resources for learning, documentation, and integrations.

TTS WebUI is a comprehensive tool for text-to-speech synthesis, audio/music generation, and audio conversion. It offers a user-friendly interface for various AI projects related to voice and audio processing. The tool provides a range of models and extensions for different tasks, along with integrations like Silly Tavern and OpenWebUI. With support for Docker setup and compatibility with Linux and Windows, TTS WebUI aims to facilitate creative and responsible use of AI technologies in a user-friendly manner.

The Alchemyst Platform Cookbook is a comprehensive guide for developers and builders to bring their AI ideas to life. It provides cutting-edge AI tools and templates to empower users in creating innovative projects. The platform offers API documentation, quick start guides, official and community templates for various projects. Users can contribute to the platform by forking the repository, adding the topic 'alchemyst-awesome-saas', making their repository public, and submitting a pull request. Troubleshooting guidelines are provided for contributors. The platform is actively maintained by the Alchemyst AI Team.

Uni-MoE is a MoE-based unified multimodal model that can handle diverse modalities including audio, speech, image, text, and video. The project focuses on scaling Unified Multimodal LLMs with a Mixture of Experts framework. It offers enhanced functionality for training across multiple nodes and GPUs, as well as parallel processing at both the expert and modality levels. The model architecture involves three training stages: building connectors for multimodal understanding, developing modality-specific experts, and incorporating multiple trained experts into LLMs using the LoRA technique on mixed multimodal data. The tool provides instructions for installation, weights organization, inference, training, and evaluation on various datasets.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

Supabase is an open source Firebase alternative that provides a wide range of features including a hosted Postgres database, authentication and authorization, auto-generated APIs, REST and GraphQL support, realtime subscriptions, functions, file storage, AI and vector/embeddings toolkit, and a dashboard. It aims to offer developers a Firebase-like experience using enterprise-grade open source tools.

Bear Stone Smart Home Documentation is a live, personal Home Assistant configuration shared for browsing and inspiration. It provides real-world automations, scripts, and scenes for users to reverse-engineer patterns and adapt to their own setups. The repository layout includes reusable config under `config/`, with runtime artifacts hidden by `.gitignore`. The platform runs on Docker/compose, and featured examples cover various smart home scenarios like alarm monitoring, lighting control, and presence awareness. The repository also includes a network diagram and lists Docker add-ons & utilities, gear tied to real automations, and affiliate links for supported hardware.

Astra.ai is a multimodal agent powered by TEN, showcasing its capabilities in speech, vision, and reasoning through RAG from local documentation. It provides a platform for developing AI agents with features like RTC transportation, extension store, workflow builder, and local deployment. Users can build and test agents locally using Docker and Node.js, with prerequisites including Agora App ID, Azure's speech-to-text and text-to-speech API keys, and OpenAI API key. The platform offers advanced customization options through config files and API keys setup, enabling users to create and deploy their AI agents for various tasks.

Folo is a content organization tool that creates a noise-free timeline for users. It allows sharing lists, exploring collections, and distraction-free browsing. Users can subscribe to feeds, curate favorites, and utilize AI-powered features like translation and summaries. Folo supports various content types such as articles, videos, images, and audio. It introduces an ownership economy with $POWER tipping for creators and fosters a community-driven experience. The tool is under active development, welcoming feedback from users and developers.

LLaMA Factory is a unified framework for fine-tuning 100+ large language models (LLMs) with various methods, including pre-training, supervised fine-tuning, reward modeling, PPO, DPO and ORPO. It features integrated algorithms like GaLore, BAdam, DoRA, LongLoRA, LLaMA Pro, LoRA+, LoftQ and Agent tuning, as well as practical tricks like FlashAttention-2, Unsloth, RoPE scaling, NEFTune and rsLoRA. LLaMA Factory provides experiment monitors like LlamaBoard, TensorBoard, Wandb, MLflow, etc., and supports faster inference with OpenAI-style API, Gradio UI and CLI with vLLM worker. Compared to ChatGLM's P-Tuning, LLaMA Factory's LoRA tuning offers up to 3.7 times faster training speed with a better Rouge score on the advertising text generation task. By leveraging 4-bit quantization technique, LLaMA Factory's QLoRA further improves the efficiency regarding the GPU memory.

SWIFT (Scalable lightWeight Infrastructure for Fine-Tuning) supports training, inference, evaluation and deployment of nearly **200 LLMs and MLLMs** (multimodal large models). Developers can directly apply our framework to their own research and production environments to realize the complete workflow from model training and evaluation to application. In addition to supporting the lightweight training solutions provided by [PEFT](https://github.com/huggingface/peft), we also provide a complete **Adapters library** to support the latest training techniques such as NEFTune, LoRA+, LLaMA-PRO, etc. This adapter library can be used directly in your own custom workflow without our training scripts. To facilitate use by users unfamiliar with deep learning, we provide a Gradio web-ui for controlling training and inference, as well as accompanying deep learning courses and best practices for beginners. Additionally, we are expanding capabilities for other modalities. Currently, we support full-parameter training and LoRA training for AnimateDiff.

Hands-On Large Language Models CN(ZH) is a Chinese version of the book 'Hands-On Large Language Models' by Jay Alammar and Maarten Grootendorst. It provides detailed code annotations and additional insights, offers Notebook versions suitable for Chinese network environments, utilizes openbayes for free GPU access, allows convenient environment setup with vscode, and includes accompanying Chinese language videos on platforms like Bilibili and YouTube. The book covers various chapters on topics like Tokens and Embeddings, Transformer LLMs, Text Classification, Text Clustering, Prompt Engineering, Text Generation, Semantic Search, Multimodal LLMs, Text Embedding Models, Fine-tuning Models, and more.

Chat Your Doc is an experimental project exploring various applications based on LLM technology. It goes beyond being just a chatbot project, focusing on researching LLM applications using tools like LangChain and LlamaIndex. The project delves into UX, computer vision, and offers a range of examples in the 'Lab Apps' section. It includes links to different apps, descriptions, launch commands, and demos, aiming to showcase the versatility and potential of LLM applications.

InternLM is a powerful language model series with features such as 200K context window for long-context tasks, outstanding comprehensive performance in reasoning, math, code, chat experience, instruction following, and creative writing, code interpreter & data analysis capabilities, and stronger tool utilization capabilities. It offers models in sizes of 7B and 20B, suitable for research and complex scenarios. The models are recommended for various applications and exhibit better performance than previous generations. InternLM models may match or surpass other open-source models like ChatGPT. The tool has been evaluated on various datasets and has shown superior performance in multiple tasks. It requires Python >= 3.8, PyTorch >= 1.12.0, and Transformers >= 4.34 for usage. InternLM can be used for tasks like chat, agent applications, fine-tuning, deployment, and long-context inference.

OSM-AI-helper is a Blueprint by Mozilla.ai designed to assist users in mapping features in OpenStreetMap using object detection and image segmentation models. It provides tools for identifying and mapping various features, such as swimming pools, in OpenStreetMap. Users can also create custom datasets and fine-tune models for different use cases. The project is licensed under the AGPL-3.0 License and welcomes contributions from the community.

Cyclops is a toolkit for facilitating research and deployment of ML models for healthcare. It provides a few high-level APIs namely: data - Create datasets for training, inference and evaluation. We use the popular 🤗 datasets to efficiently load and slice different modalities of data models - Use common model implementations using scikit-learn and PyTorch tasks - Use common ML task formulations such as binary classification or multi-label classification on tabular, time-series and image data evaluate - Evaluate models on clinical prediction tasks monitor - Detect dataset shift relevant for clinical use cases report - Create model report cards for clinical ML models

This tool is a fork of the 3D Gaussian Splatting code. It allows users to create a dataset ready to be trained with the Gaussian Splatting code. The dataset can be used for various experiments, such as creating orthogonal images, steganography, and lenticular effects. The tool also includes a visualizer that allows users to visualize the "painting" process during the Gaussian Splatting optimization.

UHGEval is a comprehensive framework designed for evaluating the hallucination phenomena. It includes UHGEval, a framework for evaluating hallucination, XinhuaHallucinations dataset, and UHGEval-dataset pipeline for creating XinhuaHallucinations. The framework offers flexibility and extensibility for evaluating common hallucination tasks, supporting various models and datasets. Researchers can use the open-source pipeline to create customized datasets. Supported tasks include QA, dialogue, summarization, and multi-choice tasks.

RAG Foundry is a library designed to enhance Large Language Models (LLMs) by fine-tuning models on RAG-augmented datasets. It helps create training data, train models using parameter-efficient finetuning (PEFT), and measure performance using RAG-specific metrics. The library is modular, customizable using configuration files, and facilitates prototyping with various RAG settings and configurations for tasks like data processing, retrieval, training, inference, and evaluation.

The 'ollama-ebook-summary' repository is a Python project that creates bulleted notes summaries of books and long texts, particularly in epub and pdf formats with ToC metadata. It automates the extraction of chapters, splits them into ~2000 token chunks, and allows for asking arbitrary questions to parts of the text for improved granularity of response. The tool aims to provide summaries for each page of a book rather than a one-page summary of the entire document, enhancing content curation and knowledge sharing capabilities.

AgentNeo is a Python package that provides functionalities for project, trace, dataset, experiment management. It allows users to authenticate, create projects, trace agents and LangGraph graphs, manage datasets, and run experiments with metrics. The tool aims to streamline AI project management and analysis by offering a comprehensive set of features.

RAG-FiT is a library designed to improve Language Models' ability to use external information by fine-tuning models on specially created RAG-augmented datasets. The library assists in creating training data, training models using parameter-efficient finetuning (PEFT), and evaluating performance using RAG-specific metrics. It is modular, customizable via configuration files, and facilitates fast prototyping and experimentation with various RAG settings and configurations.

SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

PEFT (Parameter-Efficient Fine-Tuning) is a collection of state-of-the-art methods that enable efficient adaptation of large pretrained models to various downstream applications. By only fine-tuning a small number of extra model parameters instead of all the model's parameters, PEFT significantly decreases the computational and storage costs while achieving performance comparable to fully fine-tuned models.

This repo hosts tutorial documentation for running generative AI models on NVIDIA Jetson devices. The documentation is auto-generated and hosted on GitHub Pages using their CI/CD feature to automatically generate/update the HTML documentation site upon new commits.

Emgu CV is a cross-platform .Net wrapper for the OpenCV image-processing library. It allows OpenCV functions to be called from .NET compatible languages. The wrapper can be compiled by Visual Studio, Unity, and "dotnet" command, and it can run on Windows, Mac OS, Linux, iOS, and Android.

MMStar is an elite vision-indispensable multi-modal benchmark comprising 1,500 challenge samples meticulously selected by humans. It addresses two key issues in current LLM evaluation: the unnecessary use of visual content in many samples and the existence of unintentional data leakage in LLM and LVLM training. MMStar evaluates 6 core capabilities across 18 detailed axes, ensuring a balanced distribution of samples across all dimensions.

VLMEvalKit is an open-source evaluation toolkit of large vision-language models (LVLMs). It enables one-command evaluation of LVLMs on various benchmarks, without the heavy workload of data preparation under multiple repositories. In VLMEvalKit, we adopt generation-based evaluation for all LVLMs, and provide the evaluation results obtained with both exact matching and LLM-based answer extraction.

This Docker image for LLaVA (Large Language and Vision Assistant) provides a convenient way to run LLaVA locally or on RunPod. LLaVA is a powerful AI tool that combines natural language processing and computer vision capabilities. With this Docker image, you can easily access LLaVA's functionalities for various tasks, including image captioning, visual question answering, text summarization, and more. The image comes pre-installed with LLaVA v1.2.0, Torch 2.1.2, xformers 0.0.23.post1, and other necessary dependencies. You can customize the model used by setting the MODEL environment variable. The image also includes a Jupyter Lab environment for interactive development and exploration. Overall, this Docker image offers a comprehensive and user-friendly platform for leveraging LLaVA's capabilities.