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--- |
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library_name: peft |
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base_model: |
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- distilbert/distilgpt2 |
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pipeline_tag: text-classification |
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language: |
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- en |
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--- |
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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: |
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### 1.1.Model and Tokenizer Setup: |
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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. |
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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. |
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### 1.2. Dataset: SST-2 from GLUE Benchmark: |
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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). |
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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. |
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https://machinelearningmastery.com/types-of-classification-in-machine-learning/ |
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https://machinelearningmastery.com/tour-of-evaluation-metrics-for-imbalanced-classification/ |
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https://machinelearningmastery.com/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset/ |
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### 1.3 Applying LoRA for Parameter-Efficient Fine-Tuning: |
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To efficiently fine-tune the model with minimal trainable parameters, I applied LoRA using the PEFT (Parameter-Efficient Fine-Tuning) library. |
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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. |
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### 1.4 Training the LoRA-Adapted Model: |
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I used Hugging Face’s Trainer API to fine-tune the LoRA-enhanced DistilGPT2 model on the SST-2 dataset. |
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The training loop was configured to evaluate F2 Score at each epoch, and I ensured efficient memory usage by utilizing GPU acceleration when available. |
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### 1.5 Evaluation and Saving the Fine-Tuned Model: |
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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. |
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Finally, I saved the fine-tuned LoRA model using the PeftModel.save_pretrained() method, making it available for further inference or fine-tuning tasks. |
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- PEFT 0.5.0 |
