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Fine-tuning large language models (LLM) has become an important tool for businesses seeking to tailor AI capabilities to niche tasks and personalized user experiences. But fine-tuning usually comes with steep computational and financial overhead, keeping its use limited for enterprises with limited resources.
To solve these challenges, researchers have created algorithms and techniques that cut the cost of fine-tuning LLMs and running fine-tuned models. The latest of these techniques is S-LoRA, a collaborative effort between researchers at Stanford University and University of California-Berkeley (UC Berkeley).
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