Update README.md

README.md

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 - tuned
 - sst
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+1. Overview
+This repository showcases the fine-tuning of the Llama-3.2-1B model on the SST-2 dataset. The objective is to perform binary sentiment classification, identifying whether a given sentence expresses a positive or negative sentiment. The fine-tuning process focuses on task-specific optimization, transforming the pre-trained Llama model into a powerful sentiment analysis tool.
+
+2. Model Information
+Model Used: meta-llama/Llama-3.2-1B
+Pre-trained Parameters: The model comprises approximately 1.03 billion parameters, confirmed through code inspection and consistent with the official documentation.
+Fine-tuned Parameters: The parameter count remains unchanged during fine-tuning, as the task updates existing model weights without adding new layers or parameters.
+3. Dataset and Task Details
+Dataset: SST-2
+The Stanford Sentiment Treebank (SST-2) dataset is widely used for binary sentiment classification tasks.
+The dataset consists of sentences labeled as either positive or negative sentiment.
+Task Objective
+Train the model to classify sentences into the appropriate sentiment category based on contextual cues.
+4. Fine-Tuning Approach
+Train-Test Split: The dataset was split into an 80:20 ratio using stratified sampling to ensure balanced representation of sentiment classes.
+Tokenization: Input text was tokenized with padding and truncation to a maximum length of 128 tokens.
+Model Training: Fine-tuning involved updating task-specific weights over three epochs with a learning rate of 2e-5.
+Hardware: Training was performed on GPU-enabled hardware for accelerated computations.
+5. Results and Observations
+Zero-shot vs. Fine-tuned Performance: The pre-trained Llama model in its zero-shot state exhibited moderate performance on SST-2. After fine-tuning, the model achieved significant improvements in its ability to classify sentiments accurately.
+
+Fine-tuning Benefits: Task-specific training allowed the model to better understand contextual nuances in the data, resulting in enhanced sentiment classification capabilities.
+
+Model Parameters: The total number of parameters did not change during fine-tuning, indicating that the improvement in performance is attributed solely to the updated weights.
+
+6. How to Use the Fine-Tuned Model
+Install Necessary Libraries:
+
+pip install transformers datasets
+Load the Fine-Tuned Model:
+
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_name = "<your-huggingface-repo>/sst2-llama-finetuned"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+Make Predictions:
+
+text = "The movie was absolutely fantastic!"
+inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+outputs = model(**inputs)
+sentiment = "Positive" if outputs.logits.argmax() == 1 else "Negative"
+print(f"Predicted Sentiment: {sentiment}")
+7. Key Takeaways
+Fine-tuning the Llama model for SST-2 significantly enhances its performance on binary sentiment classification tasks.
+The parameter count of the model remains constant during fine-tuning, demonstrating that improvements are achieved by optimizing existing weights.
+This work highlights the adaptability of Llama for downstream NLP tasks when fine-tuned on task-specific datasets.
+8. Acknowledgments
+Hugging Face Transformers library for facilitating model fine-tuning.
+Stanford Sentiment Treebank for providing a robust dataset for sentiment classification.