rudalson/Llama-3.2-3B-Instruct-KoAlpaca
rudalson/Llama-3.2-3B-Instruct-KoAlpaca is a 3.2 billion parameter causal language model, fine-tuned from Meta's Llama-3.2-3B-Instruct. It specializes in Korean language processing, specifically enhancing Korean question answering and instruction following capabilities. This model leverages the KoAlpaca v1.1a dataset to improve its performance in Korean conversational AI tasks, making it suitable for applications requiring robust Korean language understanding and generation.
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Model Overview
rudalson/Llama-3.2-3B-Instruct-KoAlpaca is a specialized language model built upon Meta's Llama-3.2-3B-Instruct. Its primary distinction lies in its fine-tuning with the KoAlpaca v1.1a dataset, which significantly enhances its proficiency in the Korean language. This 3.2 billion parameter model is designed to excel in tasks requiring Korean natural language understanding and generation.
Key Capabilities
- Korean Language Specialization: Optimized for processing and generating text in Korean.
- Instruction Following: Capable of understanding and executing instructions provided in Korean.
- Question Answering: Designed to provide accurate and concise answers to Korean questions.
- Conversational AI: Supports a conversational prompt format, making it suitable for interactive applications.
Training Details
The model was fine-tuned using LoRA (Low-Rank Adaptation) with specific hyperparameters, including an r value of 32 and lora_alpha of 64, targeting all linear layers. It underwent 1 training epoch with a learning rate of 2e-04 and utilized gradient checkpointing and fp16 for efficient training.
Recommended Use Cases
- Korean Chatbots: Ideal for developing chatbots that interact in Korean.
- Korean Content Generation: Useful for generating various forms of text content in Korean based on given prompts.
- Educational Tools: Can be integrated into tools for learning or practicing Korean.
Limitations
Evaluation metrics show F1 Score at 11.40%, ROUGE-1 at 4.94%, and ROUGE-L at 4.62%, indicating areas for potential improvement in generation quality.