KoLlama2: Korean-Optimized Llama2 Model

KoLlama2 is an open-source project by psymon aimed at significantly improving the Korean language capabilities of the Llama2 base model. Recognizing that Korean constitutes a very small percentage (0.06%) of Llama2's pre-training data, this initiative seeks to bridge the performance gap for Korean speakers.

Key Capabilities & Approach

• Enhanced Korean Performance: Focuses on boosting Llama2's proficiency in Korean through targeted fine-tuning.
• Methodology Exploration: Investigates various fine-tuning techniques, including QLoRA, LoRA, and Full-Finetuning, to determine their effectiveness on Llama2's inherent Korean understanding.
• Dataset Evaluation: Applies diverse Korean datasets, such as Alpaca and Vicuna, to identify which data types yield the most substantial improvements in Korean language tasks.
• Innovative Techniques: Explores advanced methods like Curriculum Learning (progressively increasing difficulty from simple translation) and vocabulary expansion (similar to Chinese-LLaMA).
• Evaluation Framework: Aims to devise a robust evaluation methodology to objectively assess the performance of different fine-tuning approaches.

Good for

• Developers and researchers working on Korean natural language processing with Llama2.
• Applications requiring improved Korean language generation and understanding from a Llama2-based model.
• Experimentation with different fine-tuning strategies for low-resource languages on large language models.