Update README.md

README.md

@@ -8,200 +8,145 @@ tags:
 - krx
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-# Model Card for Model ID
+여기 마크다운 형식으로 수정된 버전입니다:
+
+# Qwen 2.5 7B Instruct Model Fine-tuning
+
+This repository contains code for fine-tuning the Qwen 2.5 7B Instruct model using Amazon SageMaker. The project uses QLoRA (Quantized Low-Rank Adaptation) for efficient fine-tuning of large language models.
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Project Structure
+
+```
+.
+├── scripts/
+│   ├── train.py
+│   ├── tokenization_qwen2.py
+│   ├── requirements.txt
+│   └── bootstrap.sh
+├── sagemaker_train.py
+└── README.md
+```
 
+## Prerequisites
+
+- Amazon SageMaker access
+- Hugging Face account and access token
+- AWS credentials configured
+- Python 3.10+
 
+## Environment Setup
 
-## Model Details
+The project uses the following key dependencies:
 
-### Model Description
+- PyTorch 2.1.0
+- Transformers (latest from main branch)
+- Accelerate >= 0.27.0
+- PEFT >= 0.6.0
+- BitsAndBytes >= 0.41.0
 
-<!-- Provide a longer summary of what this model is. -->
+## Model Configuration
 
+- Base Model: `Qwen/Qwen2.5-7B-Instruct`
+- Training Method: QLoRA (4-bit quantization)
+- Instance Type: ml.p5.48xlarge
+- Distribution Strategy: PyTorch DDP
 
+## Training Configuration
 
-- **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]
+### Hyperparameters
 
-### Model Sources [optional]
+```python
+{
+    'epochs': 3,
+    'per_device_train_batch_size': 4,
+    'gradient_accumulation_steps': 8,
+    'learning_rate': 1e-5,
+    'max_steps': 1000,
+    'bf16': True,
+    'max_length': 2048,
+    'gradient_checkpointing': True,
+    'optim': 'adamw_torch',
+    'lr_scheduler_type': 'cosine',
+    'warmup_ratio': 0.1,
+    'weight_decay': 0.01,
+    'max_grad_norm': 0.3
+}
+```
 
-<!-- Provide the basic links for the model. -->
+### Environment Variables
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+The training environment is configured with optimizations for distributed training and memory management:
 
-## Uses
+- CUDA device configuration
+- Memory optimization settings
+- EFA (Elastic Fabric Adapter) configuration for distributed training
+- Hugging Face token and cache settings
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Training Process
 
-### Direct Use
+1. **Environment Preparation**:
+   - Creates `requirements.txt` with necessary dependencies
+   - Generates `bootstrap.sh` for Transformers installation
+   - Sets up SageMaker training configuration
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+2. **Model Loading**:
+   - Loads the base Qwen 2.5 7B model with 4-bit quantization
+   - Configures BitsAndBytes for quantization
+   - Prepares model for k-bit training
 
-[More Information Needed]
+3. **Dataset Processing**:
+   - Uses the Sujet Finance dataset
+   - Formats conversations in Qwen2 format
+   - Applies tokenization with maximum length of 2048 tokens
+   - Implements data preprocessing with parallel processing
 
-### Downstream Use [optional]
+4. **Training**:
+   - Implements gradient checkpointing for memory efficiency
+   - Uses cosine learning rate schedule with warmup
+   - Saves checkpoints every 50 steps
+   - Logs training metrics every 10 steps
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+## Monitoring and Metrics
 
-[More Information Needed]
+The training process tracks the following metrics:
+- Training loss
+- Evaluation loss
 
-### Out-of-Scope Use
+## Error Handling
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+The implementation includes comprehensive error handling and logging:
+- Environment validation
+- Dataset preparation verification
+- Training process monitoring
+- Detailed error messages and stack traces
 
-[More Information Needed]
+## Usage
 
-## Bias, Risks, and Limitations
+1. Configure AWS credentials and SageMaker role
+2. Set up Hugging Face token
+3. Run the training script:
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+```bash
+python sagemaker_train.py
+```
 
-[More Information Needed]
+## Custom Components
 
-### Recommendations
+### Custom Tokenizer
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+The project includes a custom implementation of the Qwen2 tokenizer (`tokenization_qwen2.py`) with:
+- Special token handling
+- Unicode normalization
+- Vocabulary management
+- Input preparation for model training
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## Notes
 
-## How to Get Started with the Model
+- The training script is optimized for the ml.p5.48xlarge instance type
+- Uses PyTorch Distributed Data Parallel for training
+- Implements gradient checkpointing for memory optimization
+- Includes automatic retry mechanism for training failures
+
+## License
 
-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 -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **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]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
-### Framework versions
-
-- PEFT 0.7.1
+[Add License Information]