Bridging the Medical Knowledge Gap: Investigating the Efficacy of 4-bit Instruction-Tuning on Llama-3-8B for Clinical Reasoning
Year: 2026
Type: article
Abstract: This work investigates the effectiveness of 4-bit quantization and parameter-efficient fine-tuning (QLoRA) applied to the Llama-3-8B language model for clinical biomedical reasoning tasks. The study demonstrates that expert-level reasoning performance can be achieved on consumer-grade hardware by carefully balancing quantization precision, adapter configuration, and domain-specific instruction tuning. Results highlight the trade-offs between memory efficiency, accuracy, and deployability, supporting broader access to advanced medical AI systems under strict computational constraints.
Author: Verma, Aditya
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W7128365216
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