flflmillama

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.

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