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README.md

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-library_name: transformers
-tags:
-- unsloth
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
-
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-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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
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-### 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. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
-
 
-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## Citation [optional]
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-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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-**BibTeX:**
-
-[More Information Needed]
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-**APA:**
+---
 
-[More Information Needed]
+# 🔍 otpensource vision LoRA
 
-## Glossary [optional]
+otpensource vision LoRA는 *otpensource vision* 모델을 기반으로, LoRA(Low-Rank Adaptation) 기법을 활용하여 학습된 경량 어댑터 모델입니다. 원본 모델의 성능을 유지하면서도, 적은 연산량으로 특정 도메인에 최적화된 결과를 제공합니다.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## 📌 특징
+- **경량 어댑터**: LoRA 기법을 활용하여 기존 모델 대비 적은 파라미터로 빠르게 적용 가능
+- **빠른 Fine-tuning**: 원본 모델의 성능을 유지하면서도 추가적인 학습이 필요한 영역에 맞춰 조정 가능
+- **저비용 고효율**: 연산량이 적어 디바이스에서 효율적으로 동작
 
-[More Information Needed]
+## 📥 모델 다운로드
+LoRA 어댑터를 적용한 모델은 아래 링크에서 다운로드할 수 있습니다.
+- [Hugging Face Model Hub](https://huggingface.co/otpensource-vision-lora)
 
-## More Information [optional]
+## 🚀 사용법
+```python
+from transformers import AutoModel, AutoProcessor
 
-[More Information Needed]
+model = AutoModel.from_pretrained("otpensource-vision-lora")
+processor = AutoProcessor.from_pretrained("otpensource-vision")
 
-## Model Card Authors [optional]
+image = ...  # 입력 이미지
+inputs = processor(images=image, return_tensors="pt")
+outputs = model(**inputs)
+```
 
-[More Information Needed]
+## 🛠 학습 방법
+LoRA 기반으로 기존 모델을 효과적으로 미세 조정하기 위해 아래와 같은 설정을 사용했습니다.
+- **기본 모델**: otpensource vision
+- **훈련 기법**: LoRA 적용
+- **데이터셋**: 특정 도메인에서 수집된 이미지 데이터
+- **학습 환경**: NVIDIA A100
 
-## Model Card Contact
+## 📄 라이선스
+본 모델은 **MIT 라이선스** 하에 제공되며, 자유롭게 사용 및 수정이 가능합니다.
 
-[More Information Needed]