Update README.md

README.md

@@ -17,8 +17,68 @@ base_model:
 library_name: transformers
 tags:
 - llama
-- gemma
 - sqaud
 - fine
 - tuned
----
+---
+
+1. Overview
+This repository highlights the fine-tuning of the Llama-3.2-1B model on the SQuAD (Stanford Question Answering Dataset) dataset. The task involves training the model to accurately answer questions based on a given context passage. Fine-tuning the pre-trained Llama model aligns it with the objectives of extractive question-answering.
+
+2. Model Information
+Model Used: meta-llama/Llama-3.2-1B
+Pre-trained Parameters: The model contains approximately 1.03 billion parameters, verified during setup and matching official documentation.
+Fine-tuned Parameters: The parameter count remains consistent with the pre-trained model, as fine-tuning only updates task-specific weights.
+
+3. Dataset and Task Details
+Dataset: SQuAD
+The Stanford Question Answering Dataset (SQuAD) is a benchmark dataset designed for extractive question-answering tasks. It contains passages with corresponding questions and answer spans extracted directly from the text.
+Task Objective
+Given a passage and a question, the model is trained to identify the correct span of text in the passage that answers the question.
+
+4. Fine-Tuning Approach
+Train-Test Split: An 80:20 split was applied to the dataset, ensuring a balanced distribution of passages and questions in the train and test subsets. Stratified sampling was used, with a seed value of 1 for reproducibility.
+Tokenization: Context and question pairs were tokenized with padding and truncation to ensure uniform input lengths (maximum 512 tokens).
+Model Training: Fine-tuning was conducted over three epochs with a learning rate of 3e-5. Gradient accumulation and early stopping were used to enhance training efficiency and prevent overfitting.
+Hardware: Training utilized GPU acceleration to handle the large model size and complex token sequences efficiently.
+
+5. Results and Observations
+Zero-shot vs. Fine-tuned Performance: Without fine-tuning, the pre-trained Llama model demonstrated limited ability to answer questions accurately. Fine-tuning significantly improved the model’s performance on metrics such as F1 score, exact match, and ROUGE.
+
+Fine-tuning Benefits: Training on the SQuAD dataset equipped the model with a deeper understanding of context and its relationship to specific queries, enhancing its ability to extract precise answer spans.
+
+Model Parameters: The parameter count remained unchanged during fine-tuning, underscoring that performance improvements stemmed from the optimization of existing weights rather than structural changes.
+
+6. How to Use the Fine-Tuned Model
+Install Necessary Libraries:
+
+pip install transformers datasets
+Load the Fine-Tuned Model:
+
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+model_name = "<your-huggingface-repo>/squad-llama-finetuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+Make Predictions:
+
+context = "Llama is a model developed by Meta AI designed for natural language understanding tasks."
+question = "Who developed Llama?"
+
+inputs = tokenizer(question, context, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
+
+start_idx = outputs.start_logits.argmax()
+end_idx = outputs.end_logits.argmax()
+
+answer = tokenizer.decode(inputs["input_ids"][0][start_idx:end_idx + 1])
+print(f"Predicted Answer: {answer}")
+
+7. Key Takeaways
+Fine-tuning Llama on SQuAD equips it with the ability to handle extractive question-answering tasks with high accuracy and precision.
+The parameter count of the model does not change during fine-tuning, highlighting that performance enhancements are derived from weight updates rather than architectural modifications.
+The comparison between zero-shot and fine-tuned performance demonstrates the necessity of task-specific training to achieve state-of-the-art results.
+
+8. Acknowledgments
+Hugging Face for providing seamless tools for model fine-tuning and evaluation.
+Stanford Question Answering Dataset for serving as a robust benchmark for extractive QA tasks.