WristAssist is the first app for all WearOS watches that fully brings the classic ChatGPT and DALL-E features to your wrist. It allows users to chat, save chats, edit chats, view galleries, create images, and share images directly from their wrist. The app provides a seamless user experience with easy installation from Google Play and free lifetime updates. Users can refer to the Wiki page for detailed setup instructions. WristAssist is licensed under the terms of the Apache 2.0 license.

David UI is a free and open-source collection of customizable, production-ready UI components built with Tailwind CSS. It is designed to be developer-friendly and performance-focused, streamlining the creation of modern, visually appealing interfaces to help deliver high-quality user experiences faster.

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.

智元 Fast API is a one-stop API management system that unifies various LLM APIs in terms of format, standards, and management, achieving the ultimate in functionality, performance, and user experience. It supports various models from companies like OpenAI, Azure, Baidu, Keda Xunfei, Alibaba Cloud, Zhifu AI, Google, DeepSeek, 360 Brain, and Midjourney. The project provides user and admin portals for preview, supports cluster deployment, multi-site deployment, and cross-zone deployment. It also offers Docker deployment, a public API site for registration, and screenshots of the admin and user portals. The API interface is similar to OpenAI's interface, and the project is open source with repositories for API, web, admin, and SDK on GitHub and Gitee.

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.

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.

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.

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.

langfuse-js is a modular mono repo for the Langfuse JS/TS client libraries. It includes packages for Langfuse API client, tracing, OpenTelemetry export helpers, OpenAI integration, and LangChain integration. The SDK is currently in version 4 and offers universal JavaScript environments support as well as Node.js 20+. The repository provides documentation, reference materials, and development instructions for managing the monorepo with pnpm. It is licensed under MIT.

Pocket Flow is a 100-line minimalist LLM framework designed for (Multi-)Agents, Workflow, RAG, etc. It provides a core abstraction for LLM projects by focusing on computation and communication through a graph structure and shared store. The framework aims to support the development of LLM Agents, such as Cursor AI, by offering a minimal and low-level approach that is well-suited for understanding and usage. Users can install Pocket Flow via pip or by copying the source code, and detailed documentation is available on the project website.

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.

Intel® Extension for Transformers is an innovative toolkit designed to accelerate GenAI/LLM everywhere with the optimal performance of Transformer-based models on various Intel platforms, including Intel Gaudi2, Intel CPU, and Intel GPU. The toolkit provides the below key features and examples: * Seamless user experience of model compressions on Transformer-based models by extending [Hugging Face transformers](https://github.com/huggingface/transformers) APIs and leveraging [Intel® Neural Compressor](https://github.com/intel/neural-compressor) * Advanced software optimizations and unique compression-aware runtime (released with NeurIPS 2022's paper [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) and [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114), and NeurIPS 2021's paper [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754)) * Optimized Transformer-based model packages such as [Stable Diffusion](examples/huggingface/pytorch/text-to-image/deployment/stable_diffusion), [GPT-J-6B](examples/huggingface/pytorch/text-generation/deployment), [GPT-NEOX](examples/huggingface/pytorch/language-modeling/quantization#2-validated-model-list), [BLOOM-176B](examples/huggingface/pytorch/language-modeling/inference#BLOOM-176B), [T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), [Flan-T5](examples/huggingface/pytorch/summarization/quantization#2-validated-model-list), and end-to-end workflows such as [SetFit-based text classification](docs/tutorials/pytorch/text-classification/SetFit_model_compression_AGNews.ipynb) and [document level sentiment analysis (DLSA)](workflows/dlsa) * [NeuralChat](intel_extension_for_transformers/neural_chat), a customizable chatbot framework to create your own chatbot within minutes by leveraging a rich set of [plugins](https://github.com/intel/intel-extension-for-transformers/blob/main/intel_extension_for_transformers/neural_chat/docs/advanced_features.md) such as [Knowledge Retrieval](./intel_extension_for_transformers/neural_chat/pipeline/plugins/retrieval/README.md), [Speech Interaction](./intel_extension_for_transformers/neural_chat/pipeline/plugins/audio/README.md), [Query Caching](./intel_extension_for_transformers/neural_chat/pipeline/plugins/caching/README.md), and [Security Guardrail](./intel_extension_for_transformers/neural_chat/pipeline/plugins/security/README.md). This framework supports Intel Gaudi2/CPU/GPU. * [Inference](https://github.com/intel/neural-speed/tree/main) of Large Language Model (LLM) in pure C/C++ with weight-only quantization kernels for Intel CPU and Intel GPU (TBD), supporting [GPT-NEOX](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox), [LLAMA](https://github.com/intel/neural-speed/tree/main/neural_speed/models/llama), [MPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/mpt), [FALCON](https://github.com/intel/neural-speed/tree/main/neural_speed/models/falcon), [BLOOM-7B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/bloom), [OPT](https://github.com/intel/neural-speed/tree/main/neural_speed/models/opt), [ChatGLM2-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/chatglm), [GPT-J-6B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptj), and [Dolly-v2-3B](https://github.com/intel/neural-speed/tree/main/neural_speed/models/gptneox). Support AMX, VNNI, AVX512F and AVX2 instruction set. We've boosted the performance of Intel CPUs, with a particular focus on the 4th generation Intel Xeon Scalable processor, codenamed [Sapphire Rapids](https://www.intel.com/content/www/us/en/products/docs/processors/xeon-accelerated/4th-gen-xeon-scalable-processors.html).

Bytedesk is an AI-powered customer service and team instant messaging tool that offers features like enterprise instant messaging, online customer service, large model AI assistant, and local area network file transfer. It supports multi-level organizational structure, role management, permission management, chat record management, seating workbench, work order system, seat management, data dashboard, manual knowledge base, skill group management, real-time monitoring, announcements, sensitive words, CRM, report function, and integrated customer service workbench services. The tool is designed for team use with easy configuration throughout the company, and it allows file transfer across platforms using WiFi/hotspots without the need for internet connection.

WristAssist is the first app for all WearOS watches that fully brings the classic ChatGPT and DALL-E features to your wrist. It allows users to chat, save chats, edit chats, view galleries, create images, and share images directly from their wrist. The app provides a seamless user experience with easy installation from Google Play and free lifetime updates. Users can refer to the Wiki page for detailed setup instructions. WristAssist is licensed under the terms of the Apache 2.0 license.

AI-VTuber is a highly customizable AI VTuber project that integrates with Bilibili live streaming, uses Zhifu API as the language base model, and includes intent recognition, short-term and long-term memory, cognitive library building, song library creation, and integration with various voice conversion, voice synthesis, image generation, and digital human projects. It provides a user-friendly client for operations. The project supports virtual VTuber template construction, multi-person device template management, real-time switching of virtual VTuber templates, and offers various practical tools such as video/audio crawlers, voice recognition, voice separation, voice synthesis, voice conversion, AI drawing, and image background removal.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

MegaDetector is an AI model that identifies animals, people, and vehicles in camera trap images (which also makes it useful for eliminating blank images). This model is trained on several million images from a variety of ecosystems. MegaDetector is just one of many tools that aims to make conservation biologists more efficient with AI. If you want to learn about other ways to use AI to accelerate camera trap workflows, check out our of the field, affectionately titled "Everything I know about machine learning and camera traps".

LeapfrogAI is a self-hosted AI platform designed to be deployed in air-gapped resource-constrained environments. It brings sophisticated AI solutions to these environments by hosting all the necessary components of an AI stack, including vector databases, model backends, API, and UI. LeapfrogAI's API closely matches that of OpenAI, allowing tools built for OpenAI/ChatGPT to function seamlessly with a LeapfrogAI backend. It provides several backends for various use cases, including llama-cpp-python, whisper, text-embeddings, and vllm. LeapfrogAI leverages Chainguard's apko to harden base python images, ensuring the latest supported Python versions are used by the other components of the stack. The LeapfrogAI SDK provides a standard set of protobuffs and python utilities for implementing backends and gRPC. LeapfrogAI offers UI options for common use-cases like chat, summarization, and transcription. It can be deployed and run locally via UDS and Kubernetes, built out using Zarf packages. LeapfrogAI is supported by a community of users and contributors, including Defense Unicorns, Beast Code, Chainguard, Exovera, Hypergiant, Pulze, SOSi, United States Navy, United States Air Force, and United States Space Force.

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.

The 'carrot' repository on GitHub provides a list of free and user-friendly ChatGPT mirror sites for easy access. The repository includes sponsored sites offering various GPT models and services. Users can find and share sites, report errors, and access stable and recommended sites for ChatGPT usage. The repository also includes a detailed list of ChatGPT sites, their features, and accessibility options, making it a valuable resource for ChatGPT users seeking free and unlimited GPT services.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

AI-YinMei is an AI virtual anchor Vtuber development tool (N card version). It supports fastgpt knowledge base chat dialogue, a complete set of solutions for LLM large language models: [fastgpt] + [one-api] + [Xinference], supports docking bilibili live broadcast barrage reply and entering live broadcast welcome speech, supports Microsoft edge-tts speech synthesis, supports Bert-VITS2 speech synthesis, supports GPT-SoVITS speech synthesis, supports expression control Vtuber Studio, supports painting stable-diffusion-webui output OBS live broadcast room, supports painting picture pornography public-NSFW-y-distinguish, supports search and image search service duckduckgo (requires magic Internet access), supports image search service Baidu image search (no magic Internet access), supports AI reply chat box [html plug-in], supports AI singing Auto-Convert-Music, supports playlist [html plug-in], supports dancing function, supports expression video playback, supports head touching action, supports gift smashing action, supports singing automatic start dancing function, chat and singing automatic cycle swing action, supports multi scene switching, background music switching, day and night automatic switching scene, supports open singing and painting, let AI automatically judge the content.