Fine-tuning LLama 3.1 8B and Outperforming the Competition

In this case study, the Llama 3.1 base model was fine-tuned using advanced techniques and outperformed larger models in benchmarks such as MuSR (Multistep Soft Reasoning) and GPQA (General Problem-solving and Question Answering). The fine-tuning process involved utilizing the Intel/orca_dpo_pairs dataset, incorporating Odds Ratio Preference Optimization (ORPO), and using MonsterAPI's no-code LLM fine-tuner, MonsterTuner. The resulting model demonstrated impressive results in various benchmarks, showcasing the potential of smaller models when effectively fine-tuned.