#ChatGLM

Explore a diverse collection of over 100 Chinese language models, applications, datasets, and tutorials. This project showcases notable models such as ChatGLM, LLaMA, and Qwen, offering resources for various technical requirements. It serves as a collaborative platform for sharing open-source models and applications, fostering a broad resource hub in the evolving field of Chinese language models from development to deployment and learning materials.

Discover the digital avatar creation framework utilizing ChatGLM, Wav2lip, and So-VITS-SVC technologies. This open-source initiative, finalized in March 2023, provides instructions for setting up Python environments, installing ffmpeg, and training models, including ChatGLM and So-VITS-svc. Detailed instructions on integrating Wav2lip are also provided. Anticipate updates for enhanced usability by AI学社 post the ongoing competition. The project abides by the GNU GPLv3 licensing terms, with specific compliance needed for each model's license.

This project aims to build an open-source financial model using AI for more efficient analysis of financial data. It emphasizes simplifying the interpretation of financial reports by offering tools that deliver expert-level insights. The project focuses on using community-driven improvements and comprehensive datasets to refine its analytical capabilities. Resources such as tutorials and model fine-tuning are available to foster learning and collaboration. Collaboration and sponsorship are welcome to further enhance the AI's application in financial reporting.

This article offers a thorough analysis of the practical uses of large language models across fields including natural language processing, efficient fine-tuning techniques, and artifact production. It highlights models such as ChatGLM2 and ChatGLM3, and techniques like distributed training and text-to-image conversion. The guide also delves into progress in visual question answering and automatic speech recognition, providing insights into how LLMs are innovating sectors like finance, healthcare, and education.

Discover ChatGLM's content creation features emphasizing revision and extension capabilities. Designed to be accessible for users with no coding or Python background, it includes offline packages for easy installation under poor network conditions. Learn to enhance conversations through response adjustments or initial prompt settings. Explore installation options including offline packages, virtual environments, and Python configurations. This framework caters to both casual users and developers, simplifying API hosting and providing insights on offline model use.

This project details a complete process for tuning the ChatGLM large language model through LoRA and Reinforcement Learning with Human Feedback (RLHF) on accessible hardware. It covers data processing, supervised fine-tuning, and reward modeling. The guide also addresses effective PEFT version utilization for model integration, overcoming Hugging Face transformer compatibility challenges. This enables efficient model development and tuning, specifically for those working with constrained resources.