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LLM Fine-tuning & Safety
Fine-tuned Dolphin LLM (Safety Research)
Performed memory-efficient fine-tuning on large models to test the effects of LLMs without guardrails in a university setting.
Research Objective
Understanding the boundaries of open-source LLMs through instruction-following optimization. This research focused on making models more direct and honest while analyzing the risks associated with removing standard safety alignment.
- PEFT Optimization: Utilizing Parameter-Efficient Fine-Tuning (PEFT) to optimize models on limited hardware.
- Data Alignment: Curating datasets to prioritize honesty and directness in model responses.
- Impact Analysis: Testing model behavior in a controlled academic setting to measure accuracy vs. hallucination rates.