Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT’s capabilities. However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively. This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs). Model Developers : davidkim(changyeon kim) Repository : https://github.com/davidkim205/komt Lora target modules : qproj, oproj, vproj, gateproj, downproj, kproj, upproj Model Size : 84MB Model Architecture : The komt-mistral-7b-v1 is is a fine-tuned version of the Mistral-7B-Instruct-v0.1. For objective model evaluation, we initially used EleutherAI’s lm-evaluation-harness but obtained unsatisfactory results. Consequently, we conducted evaluations using ChatGPT, a widely used model, as described in Self-Alignment with Instruction Backtranslation and Three Ways of Using Large Language Models to Evaluate Chat .
Модальности:
Генерация текста
Задача: Генерация текста
Автор: davidkim205
Теги: peft, facebook, meta, llama, llama-2, llama-2-chat, en, ko
Лайков: 3 | Загрузок: 4
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
