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“百聆”是一个基于LLaMA的语言对齐增强的英语/中文大语言模型，具有优越的英语/中文能力，在多语言和通用任务等多项测试中取得ChatGPT 90%的性能。BayLing is an English/Chinese LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction.⚠️ This project has been moved to: https://github.com/BayLing-Models/BayLing
The main features of ictnlp/bayling are: Specialized Domain Models, Text LLM Models.
Projects with overlapping indexed features include: tigerresearch/tigerbot — TigerBot: A multi-language multi-task LLM. zjunlp/knowlm — An Open-sourced Knowledgable Large Language Model Framework. lianjiatech/belle — BELLE is a specialized implementation of Chinese conversational large language models, encompassing a full instruction… openlmlab/moss — MOSS is a conversational AI platform, fine-tuning toolkit, and quantized model runtime. It provides a framework for… openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next… baichuan-inc/baichuan2 — Baichuan2 is a collection of pre-trained large language models, including base and chat variants, designed for natural…
TigerBot: A multi-language multi-task LLM
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
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
An Open-sourced Knowledgable Large Language Model Framework.