torchtorchkimtorch/Llama-3.2-Korean-GGACHI-1B-Instruct-v1

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:1BQuant:BF16Ctx Length:32kPublished:Sep 26, 2024Architecture:Transformer0.0K Warm

The Llama-3.2-Korean-GGACHI-1B-Instruct-v1 is a 1 billion parameter instruction-tuned language model developed by torchtorchkimtorch, based on the Llama-3.2-1B-Instruct architecture. Optimized for Korean language tasks, it was fine-tuned using over 230,000 high-quality Korean datasets. This model excels in Korean-specific benchmarks like KOBEST, demonstrating improved accuracy over its base model for tasks such as COPA, HellaSwag, and Sentineg. It is primarily designed for applications requiring strong performance in Korean natural language understanding and generation.

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Llama-3.2-Korean-GGACHI-1B-Instruct-v1 Overview

Llama-3.2-Korean-GGACHI-1B-Instruct-v1 is a 1 billion parameter instruction-tuned language model developed by torchtorchkimtorch. It is built upon the Llama-3.2-1B-Instruct architecture and has been specifically optimized for Korean language tasks. The model underwent fine-tuning using an extensive collection of over 230,000 high-quality Korean language datasets.

Key Capabilities and Performance

This model demonstrates enhanced performance on several Korean benchmarks (KOBEST) compared to its base Llama-3.2-1B-Instruct model. Notable improvements include:

  • KOBEST COPA: Achieved 0.521 (0-shot), 0.549 (5-shot), and 0.549 (10-shot) accuracy, surpassing the base model.
  • KOBEST HellaSwag: Showed improved accuracy with 0.380 (0-shot), 0.398 (5-shot), and 0.394 (10-shot).
  • KOBEST Sentineg: Significantly outperformed the base model with 0.594 (0-shot), 0.795 (5-shot), and 0.821 (10-shot) accuracy.

While performance on kobest_boolq was comparable or slightly lower in some few-shot scenarios, the overall fine-tuning has yielded a model highly proficient in Korean-specific natural language understanding tasks.

Ideal Use Cases

This model is particularly well-suited for applications requiring robust performance in Korean language processing, such as:

  • Korean text generation and summarization.
  • Korean question answering and natural language inference.
  • Developing AI agents or chatbots that interact primarily in Korean.