ChatYuan: Large Language Model for Dialogue in Chinese and English
The main features of clue-ai/chatyuan are: Open Source Models, Text LLM Models.
Open-source alternatives to clue-ai/chatyuan include: thudm/chatglm2-6b — ChatGLM2-6B is an open-weight large language model designed for natural language conversations and text generation in… openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… lianjiatech/belle — BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction… thudm/chatglm-6b — ChatGLM-6B is an open-source bilingual large language model designed for natural dialogue and text generation in both… blinkdl/chatrwkv — ChatRWKV is an open-source frontend and GPU-accelerated inference engine designed for interacting with RWKV recurrent… ymcui/chinese-llama-alpaca — This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a…
BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction tuning framework. It provides a pipeline for training, evaluating, and deploying models optimized for natural language understanding and dialogue tasks in the Chinese language. The project is distinguished by its integrated approach to model refinement, combining the curation of multi-million entry instruction datasets with a distributed training pipeline. This pipeline supports both full fine-tuning and low-rank adaptation to optimize conversational performance. The system
MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for deploying large language models capable of multi-turn dialogue, general-purpose response generation, and following complex instructions. The system functions as a tool-augmented framework that extends model knowledge through external plugins and tool-call loops. This allows the model to execute tasks via search engines and calculators to augment responses with external data. The project covers model training through supervised conversational fine-tuning and optimizes deployment
ChatRWKV is an open-source frontend and GPU-accelerated inference engine designed for interacting with RWKV recurrent neural network language models. It provides a self-hosted web chat interface and a specialized client for generating human-like text using a linear-complexity architecture. The project utilizes a GPU-accelerated backend that employs custom CUDA kernels and dynamic model format conversion to increase processing speed and reduce memory overhead. It manages conversation history through state-based context management, updating a fixed-size hidden state to maintain a constant memor
ChatGLM-6B is an open-source bilingual large language model designed for natural dialogue and text generation in both English and Chinese. It is structured as a dialogue model capable of tasks such as role-playing and information extraction. The project provides implementations for quantized language models, using low-precision weights to reduce GPU memory requirements for local inference. It also supports parameter-efficient fine-tuning, allowing model behavior to be optimized for specific tasks without requiring full retraining. The model includes capabilities for local execution on GPUs a