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.


  • 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!


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.


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.

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.



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.

Multilingual Speech Recognizer and AI Assistant

Multilingual Speech Recognizer and AI Assistant

Overview: 1) Python Programming: Leveraging the versatility and robustness of Python, we've built a solid foundation for our speech recognizer and assistant, ensuring flexibility and scalability. 2) OPENAI API Integration: Empowering our assistant with the capabilities of the OPENAI API enables it to comprehend, process, and respond to queries across a spectrum of languages and topics. 3) Google Recognizer for Voice-to-Text: By utilizing Google's advanced speech recognition technology, we achieve accurate and efficient transcription of spoken words into text, forming the basis for seamless interaction. 4) Streamlit for Deployment: Deploying our solution using Streamlit provides an intuitive and user-friendly interface, making interaction effortless and accessible to users across diverse platforms. Advantages: Multilingual Mastery: Breaks language barriers, catering globally. AI-Powered Precision: Learns, adapts, and delivers tailored responses. Efficiency Booster: Swift voice interaction, enhancing productivity. Market Demand: The market demands seamless communication solutions that transcend language barriers and facilitate efficient interaction. Our Multilingual Speech Recognizer & AI Assistant addresses this demand by offering a versatile, intelligent, and accessible platform. Conclusion: In the dynamic landscape of communication technology, our Multilingual Speech Recognizer & AI Assistant stands as a testament to innovation and progress. With its multilingual competence, AI-powered assistance, and user-friendly deployment, it heralds a new era of effortless communication and interaction, catering to the evolving needs of a diverse global audience.