Update README.md

README.md

@@ -25,6 +25,54 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 - **License:** [More Information Needed]
 - **Finetuned from model [optional]:** [More Information Needed]
 
+
+
+# Learning Rate Optimization for Language Model Fine-tuning
+
+This script implements an advanced learning rate optimization strategy for fine-tuning large language models, combining Bayesian optimization with Gaussian Process Regression (GPR) for precise learning rate selection.
+
+## Key Features
+
+### 1. Bayesian Optimization
+* Uses Optuna framework to perform systematic learning rate search
+* Implements Tree-structured Parzen Estimators (TPE) for efficient hyperparameter optimization
+* Automatically explores learning rates between 1e-6 and 1e-4 in log space
+
+### 2. Advanced Loss Tracking
+* Evaluates model performance using mean loss from the final 20% of training steps
+* Handles training failures gracefully with proper memory management
+
+### 3. Sophisticated Post-processing
+* Applies Gaussian Process Regression to model the learning rate-loss relationship
+* Calculates uncertainty estimates for each prediction
+* Implements Expected Improvement (EI) acquisition function for optimal learning rate selection
+
+### 4. Memory Optimization
+* Implements gradient checkpointing for efficient memory usage
+* Includes automatic memory clearing between trials
+
+## Technical Details
+
+The optimization process consists of three main phases:
+1. Initial exploration using Bayesian optimization
+2. Refinement using Gaussian Process Regression
+3. Final selection using Expected Improvement criterion
+
+The script was designed this way because:
+* Bayesian optimization provides efficient exploration of the learning rate space
+* GPR adds uncertainty quantification and smooth interpolation between observed points
+* The combination allows for both exploration and exploitation of the learning rate space
+
+## Advantages
+
+* More reliable than manual learning rate selection
+* Provides uncertainty estimates for each prediction
+* Automatically adapts to different model sizes and datasets
+* Generates visualizations for analysis
+* Saves comprehensive results for reproducibility
+
+This approach is particularly valuable for fine-tuning large language models where training costs are high and optimal learning rate selection is crucial for model performance.
+
 ### Model Sources [optional]
 
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