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README.md

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+Model Overview
+This model is a fine-tuned version of LLaMA-3.2B, specifically trained on a dataset processed using Facility Location (FL) and Facility Location Mutual Information (FLMI) techniques. These data selection methods were employed to reduce the dataset size while retaining high-quality and representative samples, ensuring the model is trained on the most informative and diverse data points.
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+Dataset Details
+Original Dataset: A filtered subset of a conversational dataset, containing examples of chosen and rejected responses.
+Data Preprocessing:
+The dataset underwent an initial Facility Location (FL) process to select 1,000 samples from the original dataset.
+Further refinement using Facility Location Mutual Information (FLMI) reduced the dataset to 500 highly informative samples.
+These methods ensured that the final dataset preserved critical information and diversity, optimizing the training efficiency and model performance.
+Training Configuration
+Base Model: LLaMA-3.2B
+Fine-Tuning Dataset: The final dataset of 500 samples refined through FL and FLMI techniques.
+Objective: Enhance the model's ability to generate high-quality, contextually accurate responses in conversational settings.
+Training Framework: Hugging Face Transformers library with PyTorch backend.
+Training Hardware: Multi-GPU setup (e.g., NVIDIA A100 GPUs).
+Batch Size: 16
+Learning Rate: 5e-5 with linear decay.
+Optimizer: AdamW