Update README.md

README.md

@@ -10,14 +10,54 @@ tags:
 license: apache-2.0
 language:
 - en
+- ko
 ---
 
-# Uploaded  model
+# Qwen2.5 Korean Code Review LLM
 
-- **Developed by:** ewhk9887
-- **License:** apache-2.0
-- **Finetuned from model :** unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
+[![Unsloth](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)
 
-This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+## Overview
+This model is a fine-tuned version of [`unsloth/qwen2.5-coder-14b-instruct-bnb-4bit`](https://huggingface.co/unsloth/qwen2.5-coder-14b-instruct-bnb-4bit). It is optimized for Korean-language code reviews and programming education.
+
+The model was trained using [ewhk9887/korean_code_reviews_from_github](https://huggingface.co/datasets/ewhk9887/korean_code_reviews_from_github), a dataset consisting of Korean code reviews collected from GitHub. The fine-tuning process was done using [Unsloth](https://github.com/unslothai/unsloth) and Hugging Face's `transformers` and `trl` libraries, enabling a **2x faster** training process.
+
+## 모델 개요
+이 모델은 [`unsloth/qwen2.5-coder-14b-instruct-bnb-4bit`](https://huggingface.co/unsloth/qwen2.5-coder-14b-instruct-bnb-4bit)를 파인튜닝한 버전으로, **한국어 코드 리뷰 및 코딩 학습**을 위한 최적화를 거쳤습니다.
+
+[GitHub에서 수집된 코드 리뷰 데이터셋](https://huggingface.co/datasets/ewhk9887/korean_code_reviews_from_github)을 사용하여 학습했으며, [Unsloth](https://github.com/unslothai/unsloth) 및 Hugging Face의 `transformers`, `trl` 라이브러리를 활용하여 **2배 빠른** 학습을 가능하게 했습니다.
+
+## Features / 특징
+- **Korean Code Review Support**: Designed specifically for analyzing and reviewing code in Korean.
+- **Efficient Fine-Tuning**: Utilized `bnb-4bit` quantization and Unsloth for optimized performance.
+- **Bilingual Support**: Can process both Korean and English inputs.
+- **Transformer-based Model**: Leverages Qwen2.5's strong coding capabilities.
+
+- **한국어 코드 리뷰 최적화**: 코드 리뷰를 한국어로 분석하고 작성하는 데 최적화되었습니다.
+- **효율적인 파인튜닝**: `bnb-4bit` 양자화 및 Unsloth 기술을 활용하여 빠른 학습이 가능했습니다.
+- **한영 지원**: 한국어와 영어 입력을 모두 처리할 수 있습니다.
+- **강력한 트랜스포머 기반**: Qwen2.5 모델을 활용한 코드 분석 성능.
+
+## Usage / 사용 방법
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "ewhk9887/qwen2.5-korean-code-review"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
+
+inputs = tokenizer("코드를 리뷰해 주세요: def add(a, b): return a + b", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Developer / 개발자
+- **Name**: 은은수 (Eunsoo Max Eun)
+- **License**: Apache-2.0
+
+## Acknowledgments / 참고 자료
+- [Unsloth](https://github.com/unslothai/unsloth)
+- [Hugging Face TRL](https://huggingface.co/docs/trl)
+- [Dataset Used](https://huggingface.co/datasets/ewhk9887/korean_code_reviews_from_github)
 
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)