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.

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.

CoboTian

CoBoTian is a prototype that leverages Microsoft's Phi-2 model to generates jokes with funny punchline based on user provided phrases. Main goal is to provide happiness and laughter which can greatly impact a person's mental health and well-being. It is just a small fun project created to study and test the model's performance based on the peer review. Its erratic outcomes are best suitable for this kind of task but still needs to be careful on its toxic nature.

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.

RAGify Assist- Elevating Customer Support with RAG

RAGify Assist is a customer support revolution that marries the prowess of Retrieval-Augmented Generation (RAG) with Vectara's GenAI platform. Imagine a world where customer queries are met not with generic responses but with personalized, contextually rich information powered by real-time data. Our project aims to redefine the customer support experience, ensuring that interactions are not just efficient but also tailored to individual needs. By harnessing the capabilities of RAG, RAGify Assist ensures that our AI-powered responses are not only accurate but also adaptive to the evolving landscape of customer inquiries. Vectara's GenAI platform plays a pivotal role, simplifying the complex aspects of document pre-processing, embedding models, and vector storage. This allows our developers to focus on the core of our GenAI application—transforming customer support. As we embark on this journey to reshape customer support, we invite you to join us in the week-long AI innovation challenge. The RAGify Assist project is not just about technology; it's about elevating customer interactions to new heights. Be part of the revolution! And if the categories and technologies listed don't quite fit, feel free to suggest ones that align better with our vision. Together, let's explore the uncharted possibilities of AI-driven customer support. #CustomerSupport #AIInnovation #RAGifyAssist