Proposed: https://github.com/tobi/walgit/pull/8
259 karma · joined November 8, 2011
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What if they revise and re-issue the video based on the comments?
The alternate titles suggested are also interesting:
“ A good wording could be: Почему я обратился в ЦРУ Почему я вышел на ЦРУ Почему я начал работать с ЦРУ Почему я написал ЦРУ Почему я пошел к ЦРУ (not "в ЦРУ", as the latter is for cadets who start their work for the agency) or even Почему я выбрал ЦРУ”
From Google translate:
“ A good wording could be: Why I turned to the CIA Why did I contact the CIA? Why I started working with the CIA Why did I write to the CIA Why did I go to the CIA (not ‘to the CIA’, as the latter is for cadets who start their work for the agency) or even Why I chose the CIA”
"LoftQ aims to solve the problem of the discrepancy between the quantized and full-precision model in the context of quantization and LoRA fine-tuning for Large Language Models (LLMs). By simultaneously quantizing an LLM and finding a proper low-rank initialization for LoRA fine-tuning, LoftQ significantly enhances generalization in downstream tasks."
Bard: https://bard.google.com/chat/31e0a3bb74b29b3b
"Based on the abstract, LoftQ aims to solve the performance gap observed when applying both quantization and LoRA fine-tuning to a pre-trained Large Language Model (LLM).
Here's a breakdown of the problem and LoftQ's approach:
Problem:
Quantization: Reduces the precision of model weights to save memory and computation, but can lower accuracy. LoRA fine-tuning: Improves accuracy on specific tasks by adding a low-rank adapter, but can struggle with quantized models. Combined approach: Applying both quantization and LoRA fine-tuning often leads to a performance gap compared to full fine-tuning. LoftQ's solution:
Simultaneous quantization and LoRA initialization: LoftQ proposes a novel framework that quantizes the LLM while also finding a suitable low-rank initialization for LoRA. This helps bridge the gap between the quantized and full-precision model. Improved generalization: This approach improves the model's ability to generalize well on downstream tasks, especially in challenging memory-constrained settings. Evaluation and results:
LoftQ is tested on various NLP tasks like question answering and summarization. It outperforms existing quantization methods, particularly in low-precision scenarios like 2-bit and 2/4-bit mixed precision. Overall, LoftQ tackles the challenge of combining quantization and LoRA fine-tuning for LLMs, leading to better performance and efficiency, especially in resource-limited environments."
https://www.iflscience.com/the-full-1lll-a-rubiks-cube-holy-...
https://www.reddit.com/r/Cubers/comments/whuhkq/i_learned_fu...
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