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.
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.
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.
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
Quantum-Scribe is a pioneering AI project that pushes the boundaries of content creation. Leveraging the power of natural language processing and deep learning, our system generates well-structured, coherent manuscripts, reports, and articles on a wide range of topics. Whether you're a student, researcher, or writer, Quantum-Scribe streamlines the writing process, enabling you to focus on ideas and insights rather than struggling with words. Our project is poised to transform how information is documented, shared, and disseminated in various fields. Join us in reshaping the future of written communication.
Introducing "Llamarizer," a dynamic text summarization tool fueled by the potent Llama2-13b model crafted by Meta AI. Leveraging the robust capabilities of the Clarifai platform, our app represents our earnest entry into the "Llama2 Hackathon with Clarifai," thoughtfully organized by LabLab. In a world overwhelmed by information, "Llamarizer" seeks to empower users with the ability to transform extensive text into concise, coherent summaries, revolutionizing the way content is absorbed and understood. Join us on this journey to enhance efficiency and elevate your content consumption experience.
Building Your Own Jarvis
JARVIS acts as an intelligent intermediary between users and a network of specialized agents. When a user interacts with the system, their message is directed to JARVIS as the primary point of contact. This initial step is where the magic begins to unfold. After understanding the user need. JARVIS navigates through a repository of specialized agents, each programmed to excel in specific tasks. Whether it's fetching information, performing calculations, or executing complex actions, JARVIS knows just the right agent for the job. Upon identifying the ideal agent, JARVIS initiates a seamless handover. The chosen agent becomes active, taking on the responsibility of fulfilling the user's request. This activation process extends to both the frontend and backend components, ensuring a cohesive and synchronized interaction between the user, JARVIS, and the chosen agent. Rather than users needing to interact with multiple agents individually, JARVIS simplifies the experience by acting as a gatekeeper. Users interact with a single point of contact, making their queries and requests in natural language, while JARVIS handles the intricate orchestration behind the scenes. To exhibit our system's potential, we've crafted a user-friendly web interface, sidestepping authentication complexities. Inside, two prototype agents—"music" and "call"—showcase our concept's prowess. As we look towards the future, our vision encompasses the integration of an expanding repertoire of specialized agents. This entails leveraging the power of prompt engineering to craft prompts that elicit precise and effective responses from the agents. By refining these prompts and training the agents, we aim to elevate the system's accuracy and versatility, enabling it to address an ever-widening array of user needs and inquiries.
Real Time Language Translation for video calls
1. Technologies used : a. Eleven Labs Whisper : speech recognition and translation model for real time language translation b. Eleven Labs Voice AI : generates natural & life like voice that speaks out translated text almost simultaneously 2. Existing Technologies and their Limitations : a) Skype Translator : Less accurate due to complex accents => miscommunication b) Google meet's live caption : Used only for live captions , not accurate for complex language translation c) Zoom language Interpretation : Limited availability & higher cost. 3. Unique Selling Proposition - unlike existing technologies that focus on text based translation - we will provide natural life like voice translations for effective & interactive communication 4.How will we build? i. develop environment + frameworks, libraries ii. integrate whisper's speech recognition iii. implement video call functionality iv. use Voice AI to generate voice output for translated text and play it v. test our application to ensure accuracy vi. optimize app's performance and user experience vii. Deploy the app on server / cloud platform 5. Real Life Use Cases : ✅. Multilingual Business Meetings ✅ Language Exchange Programs ✅ Virtual Language Education ✅Cross cultural Collaboration ✅Global Customer Support Teams. ✅International Virtual Event