OpenLLaMA-Chinese
Chinese LLaMA Model
A Chinese language large language model built from OpenLLaMA and fine-tuned on various datasets for multilingual text generation.
OpenLLaMA-Chinese, a permissively licensed open source instruction-following models based on OpenLLaMA
65 stars
3 watching
10 forks
Language: Python
last commit: about 3 years agoRelated projects:
| Repository | Description | Stars |
|---|---|---|
| An incremental pre-trained Chinese large language model based on the LLaMA-7B model | 234 | |
| A deep learning project providing an open-source implementation of the LLaMA2 model with Chinese and English text data | 2,235 | |
| Develops and maintains a Chinese language model finetuned on LLaMA, used for text generation and summarization tasks. | 711 | |
| Develops a multimodal Chinese language model with visual capabilities | 429 | |
| A custom Chinese version of the Meta Llama 2 model for improved Chinese language support and application | 748 | |
| Trains a large Chinese language model on massive data and provides a pre-trained model for downstream tasks | 230 | |
| This project provides pre-trained models and tools for natural language understanding (NLU) and generation (NLG) tasks in Chinese. | 439 | |
| An AI model trained on legal data to provide answers and explanations in Chinese law | 871 | |
| An open bilingual LLM developed using the LingoWhale model, trained on a large dataset of high-quality middle English text, and fine-tuned for specific tasks such as conversation generation. | 134 | |
| Provides pre-trained language models and tools for fine-tuning and evaluation | 439 | |
| An effort to develop and compare large language models beyond OpenGPT | 105 | |
| An AI model that bridges cross-lingual alignment and instruction following to improve multilingual language translation capabilities | 303 | |
| A curated list of commercial-use large language models | 11,314 | |
| A Common Lisp port of a Large Language Model (LLM) implementation | 36 | |
| A small language model designed to run efficiently on edge devices with minimal resource requirements. | 607 |