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QLoRA：一种高效LLMs微调方法，48G内存可调65B 模型，调优 …
https://zhuanlan.zhihu.com/p/632229856
WEBMay 25, 2023 · QLoRA微调方法. QLORA是一种针对深度神经网络的低精度量化和微调技术，能够实现高保真的4位微调。「它采用了两种技术——4位NormalFloat (NF4)量化和Double Quantization」。同时，引入了Paged Optimizers，它可以避免梯度检查点操作时内存爆满导致的内存错误。
DA: 48 PA: 17 MOZ Rank: 88
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详解 QLoRA 原理 （附源码剖析） - 知乎 - 知乎专栏
https://zhuanlan.zhihu.com/p/638927564
WEB新的微调方法取名为qlora，它可以显著减少内存使用，使得可以在单个48gb的gpu上微调一个有650亿参数的模型，同时保持16比特微调的性能。 QLoRA通过冻结的、4比特量化的预训练语言模型来做 LoRA，进行反向传播梯度。
DA: 2 PA: 12 MOZ Rank: 51
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QLoRA（Quantized LoRA）详解 - 知乎
https://zhuanlan.zhihu.com/p/666234324
WEB分位数 （Quantile）在数学上的定义指的是把 顺序排列 的一组数据分割为若干个相等块的分割点的数值。. 在标准正态分布中，对于分布X给定的概率 \alpha ，如果存在 u_\alpha 使得它的累积分布函数（CDF） P (X < u_\alpha ) = \alpha ，则称 u_\alpha 是标准正态分布的 …
DA: 48 PA: 90 MOZ Rank: 27
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[2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs
https://arxiv.org/abs/2305.14314
WEBMay 23, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~ (LoRA).
DA: 48 PA: 100 MOZ Rank: 57
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QLoRA: Efficient Finetuning of Quantized LLMs - GitHub
https://github.com/artidoro/qlora
WEBJul 18, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA).
DA: 31 PA: 50 MOZ Rank: 90
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Paper page - QLoRA: Efficient Finetuning of Quantized LLMs
https://huggingface.co/papers/2305.14314
WEBMay 23, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~ (LoRA).
DA: 87 PA: 66 MOZ Rank: 65
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OValery16/Tutorial-about-LLM-Finetuning-using-QLORA
https://github.com/OValery16/Tutorial-about-LLM-Finetuning-using-QLORA
WEBThis tutorial will guide you through the process of fine-tuning a Language Model (LLM) using the QLORA technique on a single GPU. We will be using the Hugging Face Transformers library, PyTorch, and the peft and datasets packages. The goal is to fine-tune an LLM for a specific task using a provided dataset and then perform inference on the ...
DA: 16 PA: 75 MOZ Rank: 40
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[2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs
https://ar5iv.labs.arxiv.org/html/2305.14314
WEBWe present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters (LoRA).
DA: 24 PA: 68 MOZ Rank: 90
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大模型面试一日一问：介绍下QLoRA算法-CSDN博客
https://blog.csdn.net/sinat_37574187/article/details/138128905
WEBApr 23, 2024 · 文章浏览阅读348次，点赞5次，收藏4次。原创 芝士AI吃鱼芝士AI吃鱼QLoRA（Quantized Low-Rank Adaptation）算法是一种针对大型预训练语言模型（如GPT-3、BERT等）的高效微调方法，旨在减少微调过程中的内存占用，同时保持或接近全精度微调的性能。QLoRA算法的核心原理是在保持预训练模型权重不变的情况下 ...
DA: 16 PA: 11 MOZ Rank: 10
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Making LLMs even more accessible with bitsandbytes, 4-bit …
https://huggingface.co/blog/4bit-transformers-bitsandbytes
WEBMay 24, 2023 · We present QLoRA, an efficient finetuning approach that reduces memory usage enough to finetune a 65B parameter model on a single 48GB GPU while preserving full 16-bit finetuning task performance. QLoRA backpropagates gradients through a frozen, 4-bit quantized pretrained language model into Low Rank Adapters~(LoRA).
DA: 17 PA: 82 MOZ Rank: 6
