Update README.md

README.md

@@ -6,10 +6,38 @@ pipeline_tag: text-classification
 language:
 - en
 ---
-## Training procedure
+In this project - [notebook](https://github.com/etechoptimist/generative_ai/blob/master/peft_foundationmodels_adaptation/LightweightFineTuning.ipynb), I utilized LoRA (Low-Rank Adaptation) to fine-tune DistilGPT2, a foundation model, for a sequence classification task using the SST-2 dataset from the GLUE benchmark. The following steps were performed to implement and adapt the model efficiently:
 
+### 1.1.Model and Tokenizer Setup:
 
-### Framework versions
+I started by loading DistilGPT2, a compact variant of GPT-2, using the Hugging Face AutoModelForSequenceClassification class. This base model was configured for a binary classification task with two labels: positive and negative.
+
+I also loaded the corresponding DistilGPT2 tokenizer, ensuring proper tokenization and padding, especially since GPT-2 models typically do not have a padding token by default.
+
+### 1.2. Dataset: SST-2 from GLUE Benchmark:
+
+The Stanford Sentiment Treebank (SST-2) dataset from the GLUE benchmark was used for training and evaluation. SST-2 is a sentiment classification dataset consisting of movie reviews, where each review is labeled as either positive (1) or negative (0).
+Given that the dataset exhibited a slight imbalance between the number of positive and negative samples, additional steps were taken to mitigate this imbalance. In essence , I used the F2 score that gives more relevance to false negatives. The next articles were crucial to handle imbalance classes.
+
+https://machinelearningmastery.com/types-of-classification-in-machine-learning/
+https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/
+https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/
+
+
+### 1.3 Applying LoRA for Parameter-Efficient Fine-Tuning:
+
+To efficiently fine-tune the model with minimal trainable parameters, I applied LoRA using the PEFT (Parameter-Efficient Fine-Tuning) library.
+LoRA was specifically applied to the attention layers of the base model, introducing low-rank adaptations that allow the model to be fine-tuned without updating all of its parameters. This reduces the memory and computational requirements compared to traditional fine-tuning.
+
+### 1.4 Training the LoRA-Adapted Model:
+
+I used Hugging Face’s Trainer API to fine-tune the LoRA-enhanced DistilGPT2 model on the SST-2 dataset.
+The training loop was configured to evaluate F2 Score  at each epoch, and I ensured efficient memory usage by utilizing GPU acceleration when available.
+
+### 1.5 Evaluation and Saving the Fine-Tuned Model:
+
+After training, I evaluated the model’s performance on the validation set, focusing on F2-score to measure how well the model handled false negatives.
+Finally, I saved the fine-tuned LoRA model using the PeftModel.save_pretrained() method, making it available for further inference or fine-tuning tasks.
 
 
 - PEFT 0.5.0