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So people are arguing that there is some potential data contamination that led to the model being trained on test data, which could explain some of the high benchmark scores. QA-LoRA, unlike QLoRA, quantizes the LoRA (adapter) weights, preventing the costly conversion of quantized base model weights back to 16 bits when adding adapter weights. This is especially true since most companies no longer share or disclose training data; which would likely avoid lawsuits (for example, see “‘Game of Thrones’ creator and other writers sue ChatGPT maker OpenAI for copyright infringement”). 5 has also been added to the Lit-GPT repository, to which I contribute regularly and is part of the NeurIPS LLM productivity challenge. If you are interested in giving Mistral and Phi-1. However, much fewer parameters were used for adapter weights. QA-LoRA: Quantization-Aware Low-Order Adaptation of Large Language Models is essentially a minor modification or improvement over QLoRA (quantized LoRA) at the cost of higher computational efficiency – QLoRA saves GPU memory but increases runtime due to quantization of the quantized on each forward pass weights of the base model. Most readers are probably familiar with how LoRA works, but if there is interest, I can share an implementation and explanation from scratch in a future article to explain in detail how it works. The opinions expressed here are my own.
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