#large language model

Open Assistant enhances language innovation with its chat-based large language model. Completed and accessible, users engage via a live chat platform. It invites contributors to optimize performance through data collection, promising efficient AI for substantial tasks. Discover the final dataset on HuggingFace.

Experience the transformative power of browser-native large language models for creating autonomous agents. With WebGPU, achieve improved performance directly in the browser, ensuring enhanced privacy and cost efficiency. Enjoy a streamlined sandbox for seamless task execution, perfect for quick prototyping and testing, without the need for complex external tools.

The LangChain-ChatGLM-Webui project provides a WebUI utilizing LangChain and ChatGLM-6B series models for applications grounded in local knowledge. Supporting multiple text file formats like txt, docx, md, and pdf, it includes models such as ChatGLM-6B and Belle for enhanced embedding functionalities. Designed for real-world AI model implementation, the project offers online accessibility through HuggingFace, ModelScope, and AIStudio. Compatible with Python 3.8.1+, it facilitates straightforward deployment. Continuous updates and community engagement ensure its dynamic advancement, inviting developer participation without exaggerated claims.

This initiative presents a structured framework for developing, fine-tuning, and evaluating Large Language Models (LLMs) aimed at the financial sector. It grants open access to financial LLMs, instructional tuning datasets, and comprehensive datasets across diverse tasks, promoting transparency and collaborative research efforts. The project emphasizes multi-task capabilities and accommodates multi-modal financial data, enhancing understanding and prediction within the field of financial NLP.

This paper list investigates the latest advancements in pretrained recommendation models, highlighting large language models and novel methods in sequence representation and user modeling. It provides a comprehensive overview with various datasets and studies, encouraging community collaboration through open contributions. Read up on significant studies presented at conferences like SIGIR, CIKM, and WSDM, and explore innovative techniques including graph pretraining and generative recommendations, under the guidance of Xiangyang Li from Peking University.

Explore a bilingual conversational AI model designed for deployment on consumer-grade hardware. Featuring 62 billion parameters and utilizing INT4 quantization, it allows local deployment with as little as 6GB memory. Tailored for Chinese question-answering, the model is enhanced through extensive bilingual training and reinforcement learning. The project supports academic research with open access to its weights and offers customization options for developers. Importantly, it stresses responsible use by encouraging compliance with open-source licenses and ethical standards.

GPT4RoI leverages region-focused tuning to enhance image analysis with improved language model integration, highlighting compatibility with LLaMA-7B models and providing practical user tools like a web demo. It is supported by detailed data from RefCOCO, VCR, and more, ensuring comprehensive visual understanding and alignment with advanced AI technologies.

llama-zip implements a user-defined LLM for effective text and binary data compression using arithmetic coding. Its sliding window mechanism overcomes LLM context limits, outperforming traditional utilities like bzip2 in many cases, though at reduced speeds due to complex inference needs. Note its portability constraints with non-deterministic LLMs.