Generic LLMs often struggle with domain-specific tasks due to limited specialized knowledge and the high cost of full fine-tuning.
Artificial Intelligence
Smarter, Leaner LLMs: Domain-Specific Training with CPT, iRAFT, and LoRA
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Summary
This whitepaper presents a cost-effective solution using Continual Pretraining (CPT) and Instruction Retrieval-Augmented Fine-Tuning (iRAFT), enhanced by LoRA for efficient training.
CPT adapts the model to domain-specific language using unlabeled data, while iRAFT fine-tunes it with labeled Q&A pairs and retrieved context. LoRA reduces computational overhead by updating only a small subset of parameters.
Together, this pipeline improves accuracy, reduces hallucinations, and enables scalable domain adaptation—achieving high performance with minimal resources.
CPT adapts the model to domain-specific language using unlabeled data, while iRAFT fine-tunes it with labeled Q&A pairs and retrieved context. LoRA reduces computational overhead by updating only a small subset of parameters.
Together, this pipeline improves accuracy, reduces hallucinations, and enables scalable domain adaptation—achieving high performance with minimal resources.
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