Text Generation Web UI AI technology page Top Builders

Explore the top contributors showcasing the highest number of Text Generation Web UI AI technology page app submissions within our community.

Text Generation Web UI

The Text Generation Web UI is a Gradio-based interface for running Large Language Models like LLaMA, llama.cpp, GPT-J, Pythia, OPT, and GALACTICA. It provides a user-friendly interface to interact with these models and generate text, with features such as model switching, notebook mode, chat mode, and more. The project aims to become the go-to web UI for text generation and is similar to AUTOMATIC1111/stable-diffusion-webui in terms of functionality.

Features

Dropdown menu for switching between models
Notebook mode that resembles OpenAI's playground
Chat mode for conversation and role-playing
Instruct mode compatible with various formats, including Alpaca, Vicuna, Open Assistant, Dolly, Koala, ChatGLM, and MOSS
Nice HTML output for GPT-4chan
Markdown output for GALACTICA, including LaTeX rendering
Custom chat characters
Advanced chat features (send images, get audio responses with TTS)
Efficient text streaming
Parameter presets
Layers splitting across GPU(s), CPU, and disk
CPU mode
and much more!

Installation

There are different installation methods available, including one-click installers for Windows, Linux, and macOS, as well as manual installation using Conda. Detailed installation instructions can be found in the Text Generation Web UI repository.

Downloading Models

Models should be placed inside the models folder. You can download models from Hugging Face, such as Pythia, OPT, GALACTICA, and GPT-J 6B. Use the download-model.py script to automatically download a model from Hugging Face.

Starting the Web UI

After installing the necessary dependencies and downloading the models, you can start the web UI by running the server.py script. The web UI can be accessed at http://localhost:7860/?__theme=dark. You can customize the interface and behavior using various command-line flags.

System Requirements

Check the wiki for examples of VRAM and RAM usage in both GPU and CPU mode.

Contributing

Pull requests, suggestions, and issue reports are welcome. Before reporting a bug, make sure you have followed the installation instructions provided and searched for existing issues.

Edit on Github

Text Generation Web UI AI technology page Hackathon projects

Discover innovative solutions crafted with Text Generation Web UI AI technology page, developed by our community members during our engaging hackathons.

Dipllama

Dipllama addresses the need for seamless document interaction and learning. Users can upload any document to the platform and choose between a chat interface for natural language queries or an interactive quiz based on the document's content. This application is ideal for students, professionals, and anyone needing efficient document comprehension tools. Unique features include AI-powered chat capabilities for instant answers and personalized quizzes tailored to enhance learning outcomes. Moreover, personalized quizzes cater to individual learning styles, enhancing overall comprehension and retention.

Lagorithmic

A web application which makes a revolution in time management industry. Main focus of our app is simplicity and easy to use interface. We want to help people manage their time and achieve lagom, which means 'just the right amount'. Our app is very simple from users perspective, you just tell an app which events to add, how to reschedule something and it will figure out the rest. You don't have to move events in your calendar, change time and dates, our app can do it for you. After that user can export his events into a calendar app. In the following presentation we present a prototype: we show how llama3 works on this task and also how everything will look from users perspective.

QueryMaster - AI driven SQL composer

Our project revolutionizes database interaction by integrating cutting-edge NLP algorithms with Python scripting to enable users, including data scientists and software engineers, to articulate complex database queries naturally. By interpreting user intent from plain language inputs, the system dynamically formulates precise SQL commands tailored to diverse database schemas and operations. Key features include syntactic and semantic parsing for query validation, ensuring accuracy and efficiency in query execution. This innovation not only accelerates development cycles but also empowers users to focus more on data analysis rather than query formulation complexities.

ANATOMY MENTOR

The Anatomy Mentor integrates the Gemini API, enriching its functionality with real-time access to anatomical data and resources. Leveraging Gemini's vast database, the Mentor enhances explanations with up-to-date information, ensuring accuracy and relevance. Additionally, it utilizes Gemini's image recognition capabilities to augment its image processing features, accurately identifying anatomical structures within uploaded images. This seamless integration enables students to delve deeper into anatomical studies, accessing a wealth of resources and insights at their fingertips. By harnessing the power of the Gemini API, the Anatomy Mentor provides a comprehensive and dynamic learning experience, setting a new standard in anatomy education.

Adapt-a-RAG

Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.

Bulbul

Bulbul is the first Saudi and Arab platform offering a personalized and interactive learning journey to help you gain and develop language skills. It features a virtual teacher with whom you can speak and learn within a comprehensive educational plan. Utilizing generative artificial intelligence and the latest technologies, Bulbul ensures a unique educational experience with maximum benefit. It provides a comprehensive and flexible plan to achieve the student's educational goals, along with tracking progress and language acquisition. Additionally, Bulbul offers libraries and resources for listening and reading to cover language skills. The learning journey is personalized, fun, and interactive, tailored to the learner's level. Bulbul stands out as the first Saudi Arabian platform in this field, employing modern technology to enhance language learning.