README.md

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library_name: transformers
tags: []
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# Gemma-2-2b-it Fine-Tuned on KoAlpaca-v1.1a

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### Model Description

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This model is a fine-tuned version of the google/gemma-2-2b-it model on the Korean dataset beomi/KoAlpaca-v1.1a. It is designed to generate coherent, contextually appropriate responses in Korean. The fine-tuning process has enhanced the model's ability to handle conversational prompts in a colloquial style, responding with contextually aware and polite expressions.

The base model, Gemma-2-2B-it, is a large pre-trained language model built for multilingual text generation tasks. With the fine-tuning on the KoAlpaca dataset, the model has been optimized to perform better on Korean text generation, offering more natural and conversational outputs.


### Training Process

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The model was fine-tuned using the KoAlpaca-v1.1a dataset, which is designed for instruction-following tasks in Korean. The dataset contains various examples of questions and corresponding responses in Korean, which helped the model learn polite conversational structures.

Dataset
- **Dataset Used:** beomi/KoAlpaca-v1.1a
- **Type of Data:** Instruction-following examples in Korean, with both the instruction and expected response provided in each entry.
Training Configuration
- **Model Base:** google/gemma-2-2b-it
- **LoRA Configuration:** Applied LoRA with the following parameters:
- **Quantization:** 4-bit quantization (bnb_4bit) for efficient fine-tuning
- **Training Hyperparameters:**
  - **Steps:** 3000
  - **Learning Rate:** 2e-4
  - **Batch Size:** 1
  - **Warmup Steps:** 100
  - **Gradient Accumulation:** 4 steps
  - **Optimizer:** paged_adamw_8bit
  - **Precision:** FP16


### Results and Performance

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Example Input:
“배가 고파서 마라탕을 먹었어요.”
(I was feeling hungry, so I ate maratang.)

- Before Fine-tuning:
    Output:“마라탕 (maratang): This is a Korean soup made with various ingredients like meat, vegetables, and noodles.  
            고파 (gopa): This means ‘to be hungry.’  
            먹었어요 (meok-eosseoyo): This is the polite way to say ‘I ate.’”  

- After Fine-tuning:
Output:
“맛있게 드셨군요! 저도 이렇게 하면 좋겠습니다. 내일은 어떤 음식을 해볼까 생각해보세요?”
(It sounds like you enjoyed your meal! I should try that too. What do you plan to cook tomorrow?)

The fine-tuned model shows a significant improvement in contextual understanding and produces more conversational and polite responses in Korean. It also demonstrates an ability to provide helpful follow-up suggestions, which is essential in conversational agents.


### Future Work

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- Further fine-tuning on larger or more diverse Korean datasets could improve the model's versatility.
- Exploring different LoRA configurations and quantization techniques could yield more efficient results for deployment on smaller devices.
- Evaluation with human raters to measure improvements in conversation quality.