Chinese-LLaMA 1&2、Chinese-Falcon 基础模型；ChatFlow中文对话模型；中文OpenLLaMA模型；NLP预训练/指令微调数据集
The main features of cvi-szu/linly are: Natural Language Processing, Text LLM Models.
Open-source alternatives to cvi-szu/linly include: optimalscale/lmflow — LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and… ymcui/chinese-llama-alpaca — This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a… dandelionsllm/pandallm — Panda项目是于2023年5月启动的开源海外中文大语言模型项目，致力于大模型时代探索整个技术栈，旨在推动中文自然语言处理领域的创新和合作。. openlmlab/openchinesellama — Chinese large language model base generated through incremental pre-training on Chinese datasets. openai/gpt-2 — This project is a transformer-based language model and autoregressive text generator designed to predict the next… abadojack/whatlanggo — Natural language detection library for Go.
LMFlow is a comprehensive suite for large language model fine-tuning, context extension, multimodal processing, and inference execution. It provides a toolkit for updating model parameters through full tuning or memory-efficient adapter algorithms, alongside an inference engine for executing tuned models via command-line or web-based interfaces. The framework includes a dedicated alignment suite for supervised tuning and reward model training to refine model behavior. It features a context window extender to increase maximum input lengths and a multimodal framework for building chatbots that
Chinese large language model base generated through incremental pre-training on Chinese datasets
Panda项目是于2023年5月启动的开源海外中文大语言模型项目，致力于大模型时代探索整个技术栈，旨在推动中文自然语言处理领域的创新和合作。
This project is a comprehensive toolkit for adapting large language models to the Chinese language, providing a specialized framework for fine-tuning, inference, and local deployment. It serves as a coordinated suite for language-specific adaptation, including tools for expanding tokenizers and implementing retrieval-augmented generation. The project distinguishes itself through a complete pipeline for model adaptation, featuring multilingual tokenizer expansion and a fine-tuning framework that supports instruction-based supervised training and adapter merging. It also includes a dedicated de