LingoWhale-8B
Middle English LLM
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.
LingoWhale-8B: Open Bilingual LLMs | 开源双语预训练大模型
134 stars
6 watching
10 forks
Language: Python
last commit: over 1 year ago llm
Related projects:
Repository | Description | Stars |
---|---|---|
| A multi-modal large language model that integrates natural language and visual capabilities with fine-tuning for various tasks | 73 |
| A polyglot large language model designed to address limitations in current LLM research and provide better multilingual instruction-following capability. | 77 |
| A large language model pre-trained on Chinese and English data, suitable for natural language processing tasks. | 43 |
| A multi-modal language model that integrates image, video, audio, and text data to improve language understanding and generation | 1,568 |
| A benchmark for evaluating large language models in multiple languages and formats | 93 |
| Library that provides a unified API to interact with various Large Language Models (LLMs) | 367 |
| A tool for training and fine-tuning large language models using advanced techniques | 387 |
| Korea University Large Language Model developed by researchers at Korea University and HIAI Research Institute. | 576 |
| Develops large language models for text understanding and generation tasks. | 85 |
| A deep learning project providing an open-source implementation of the LLaMA2 model with Chinese and English text data | 2,235 |
| A lightweight, multilingual language model with a long context length | 920 |
| Exploring various LLMs and their applications in natural language processing and related areas | 1,854 |
| An open-source implementation of a vision-language instructed large language model | 513 |
| An API that provides a unified interface to multiple large language models for chat fine-tuning | 79 |
| An AI model that bridges cross-lingual alignment and instruction following to improve multilingual language translation capabilities | 303 |