Update README.md

README.md

@@ -66,26 +66,34 @@ method for shuffling. The proportions are as follows: Training: 3,200 examples (
 with Random seed: 42.
 
 
-## Methodology
+# Methodology
 
-Finetuning Tasks: As you did in the fourth project check in,
-describe which method you chose for training and why you chose
-that method. Make note of any hyperparameter values you used so
-that others can reproduce your results.
+The training method that I implemented for this task was finetuning, specifically the parameter-efficient finetuning method LoRA. In class we learned about several model interventions, ranging from few-shot prompting to full finetuning. 
+For the purposes of this project, I chose to use PEFT. PEFT updates various aspects of the model to increase task performance while also attempting to keep catastrophic forgetting at a minimal level,
+as many of the methods freeze parameters/layers to prevent ones irrelevant to the task from being updated. PEFT is a great alternative to full finetuning because it uses less resources, 
+but can still produce efficient results for a trained model. Knowing that I was going to use PEFT was not enough for my training approach, I also needed to decide which PEFT method to use, what to set the hyperparameters as, 
+and how to choose the best model. As we have learned in class, two basic PEFT models are prompt tuning and LoRA. Through past projects, I found that prompt tuning resulted in catastrophic forgetting, as well as no performance accuracy 
+increase in the task I was training. 
+The task, gsm8k_cot, has a flexible match accuracy of only 0.02 before and after prompt training, while the benchmark SST-2 decreased in accuracy from 0.72 to 0.60. 
+This was not something I was eager to repeat with this project, as I would prefer my training improves my task. In another assignment, I found that LoRA improved that same task 
+from 0.0 performance accuracy to 0.10 (a 10% increase), while decreasing the benchmark SST-2 from 0.72 to 0.63 after training. While there was still evidence of 
+catastrophic forgetting, it is hard to ignore a 10% performance increase. Due to this, I chose LoRA to be the PEFT model I implement in my training. LoRA injects 
+low-rank adapters into specific modules, which I am hoping will train the model to perform my task well. I performed my training with three sets of hyperparameters,
+while collecting the validation loss, then choose model/combination of hyperparameters with the lowest one. This hyperparameter combination was rank: 64, alpha: 128, and dropout: 0.15. 
 
-## Evaluation
+# Evaluation
 
-## Usage and Intended Use
+# Usage and Intended Use
 
-## Prompt Format
+# Prompt Format
 
-## Expected Output Format
+# Expected Output Format
 
  This section should
 briefly describe the expected output format for your model and include a
 general code chunk showing an example model response.
 
-## Limitations
+# Limitations
 
 This section should summarize the main
 limitations of your model. Limitations could be based on benchmark task