Update README.md

README.md

@@ -4,199 +4,125 @@ tags:
 - unsloth
 - trl
 - sft
+license: mit
+datasets:
+- iamtarun/python_code_instructions_18k_alpaca
+base_model:
+- deepseek-ai/DeepSeek-R1-Distill-Llama-8B
 ---
 
-# Model Card for Model ID
+# Model Card for Finetuned DeepSeek-R1 Code Review Model  
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model Details / 모델 세부 정보
 
+### Model Description / 모델 설명
 
+**English:**  
+This model is a finetuned version of the DeepSeek-R1 Distill Llama model, adapted for performing code reviews in Korean. It has been fine-tuned using QLoRA and additional dataset transformations from the [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) dataset, converting code generation prompts into code review prompts. The LoRA adapters have been merged into the base model to produce a self-contained model that can be deployed directly.
 
-## Model Details
+**한국어:**  
+이 모델은 DeepSeek-R1 Distill Llama 모델을 기반으로, 한국어 코드 리뷰 작업에 맞게 파인튜닝된 모델입니다. [iamtarun/python_code_instructions_18k_alpaca](https://huggingface.co/datasets/iamtarun/python_code_instructions_18k_alpaca) 데이터셋의 코드 생성 프롬프트를 코드 리뷰 프롬프트로 변환하여 QLoRA 기법을 사용해 파인튜닝하였으며, LoRA 어댑터를 베이스 모델에 병합해 self-contained 형태로 제작되었습니다.
 
-### Model Description
+- **Developed by / 개발자:** [More Information Needed / 추가 정보 필요]
+- **Model type / 모델 유형:** Causal Language Model with Finetuning for Code Review  
+- **Language(s) / 사용 언어:** Korean, English  
+- **License / 라이센스:** MIT  
+- **Base Model / 베이스 모델:** [deepseek-ai/DeepSeek-R1-Distill-Llama-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B)
 
-<!-- Provide a longer summary of what this model is. -->
+### Model Sources / 모델 소스
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+- **Repository / 리포지토리:** [More Information Needed / 추가 정보 필요]
+- **Paper / 논문 (optional):** [More Information Needed / 추가 정보 필요]
+- **Demo / 데모 (optional):** [More Information Needed / 추가 정보 필요]
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
+---
 
-<!-- Relevant interpretability work for the model goes here -->
+## Uses / 사용 용도
 
-[More Information Needed]
+### Direct Use / 직접 사용
 
-## Environmental Impact
+**English:**  
+This model is intended for generating code reviews for Python code. It is designed to provide feedback on code quality, style, and possible improvements.
+It is designed as prototype for programming education.
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+**한국어:**  
+이 모델은 Python 코드를 대상으로 코드 리뷰(피드백, 스타일 개선 등)를 생성하기 위해 개발되었습니다.
+프로그래밍 교육을 위한 모델의 프로토타입으로 개발되었습니다.
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+### Downstream Use / 다운스트림 사용 (optional)
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+**English:**  
+It can be integrated into developer tools, code analysis platforms, or educational environments to assist in code review tasks.
 
-## Technical Specifications [optional]
+**한국어:**  
+개발자 도구, 코드 분석 플랫폼 또는 교육 환경에 통합되어 코드 리뷰 작업을 보조하는 용도로 활용될 수 있습니다.
 
-### Model Architecture and Objective
+### Out-of-Scope Use / 지원하지 않는 사용 예시
 
-[More Information Needed]
+**English:**  
+This model is not optimized for generating full code, handling languages other than Python, or for use in critical production environments without human oversight.
 
-### Compute Infrastructure
+**한국어:**  
+이 모델은 코드 생성을 위한 모델이 아니며, 현재 데이터셋 상 Python 이외의 언어에 대해서는 최적화되어 있지 않습니다.
+이후 한국어 코드 리뷰 데이터셋과 Go와 Rust를 포함하는 모델은 추후에 업로드될 예정입니다.
 
-[More Information Needed]
+---
 
-#### Hardware
+## Bias, Risks, and Limitations / 편향, 위험 및 한계
 
-[More Information Needed]
+**English:**  
+- The model has been trained on data that may have inherent biases, and its reviews are generated automatically.  
+- This model is not perfectly optimized for Korean language code review.
 
-#### Software
+**한국어:**  
+- 모델이 생성한 리뷰에도 편향이 있을 수 있습니다.  
+- 한국어 코드 리뷰에 완벽하게 최적화되어있지 않을 수 있습니다.
 
-[More Information Needed]
+## How to get started with model / 모델 시작하기
 
-## Citation [optional]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+model_name = "your_hf_username/merged-deepseek-r1-codereview"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
 
-**BibTeX:**
+prompt = """아래는 작성된 Python 코드입니다.
+코드의 장단점, 개선 사항, 코드 스타일 등에 대해 3~4줄 정도의 간결한 리뷰를 작성하세요.
 
-[More Information Needed]
+# 코드:
+### Python 코드
+def sum_sequence(sequence):
+    sum = 0
+    for num in sequence:
+        sum += num
+    return sum
 
-**APA:**
+### 코드 리뷰:"""
 
-[More Information Needed]
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=300)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-## Glossary [optional]
+## Training Details / 학습 정보
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Training Data / 학습 데이터
 
-[More Information Needed]
+English:
+The model was fine-tuned using the iamtarun/python_code_instructions_18k_alpaca dataset. The original code generation prompts were transformed into code review prompts to suit the task.
 
-## More Information [optional]
+한국어:
+이 모델은 iamtarun/python_code_instructions_18k_alpaca 데이터셋을 사용해 파인튜닝되었습니다. 기존의 코드 생성 프롬프트를 코드 리뷰 프롬프트로 변환하여 학습에 사용하였습니다.
+Training Procedure / 학습 절차
 
-[More Information Needed]
+English:
 
-## Model Card Authors [optional]
+    Preprocessing: The dataset was preprocessed to convert the code generation prompts into a standardized code review format.
+    Fine-tuning: The base model was fine-tuned using QLoRA with 4-bit quantization for efficiency. LoRA adapters were merged into the base model to produce a self-contained model.
 
-[More Information Needed]
+한국어:
 
-## Model Card Contact
+    전처리: 코드 생성 프롬프트를 코드 리뷰 형식으로 변환하기 위해 데이터셋을 전처리하였습니다.
+    파인튜닝: 효율성을 위해 4비트 양자화를 사용하여 QLoRA 기법으로 베이스 모델을 파인튜닝하였으며, LoRA 어댑터를 병합하여 self-contained 모델로 제작하였습니다.
 
-[More Information Needed]