请记住,在这种新范式中,指令数据集是关键,你的模型的质量在很大程度上取决于它所微调的数据。 本文是基于以下文章进行翻译和扩充:towardsdatascience.com/ 参考论文: LoRA: Low-Rank Adaptation of Large Language Models QLoRA: Efficient Finetuning of Quantized LLMs (文章结束)...
LoRA 的全称是 Low-Rank Adaptation (低秩适配),它是一种 Parameter-Efficient Fine-Tuning (参数高效微调,PEFT) 方法,即在微调时只训练原模型中的部分参数,以加速微调的过程。相比其他的 PEFT 方法,LoRA 之所以能脱颖而出,是因为它有几个明显的优点: 从性能上来看,使用 LoRA 时,只需要存储少量被微调过的参数...
LoRAis an efficient fine-tuning method where instead of finetuning all the weights that constitute the weight matrix of the pre-trained LLM, it optimizes rank decomposition matrices of the dense layers to change during adaptation. These matrices constitute the LoRA adapter. This fine-tu...
In this post, we walk through an end-to-end example of fine-tuning the Llama2 large language model (LLM) using the QLoRA method. QLoRA combines the benefits of parameter efficient fine-tuning with 4-bit/8-bit quantization to further reduce the resources required...
weights. This vastly reduces the storage requirement for large language models adapted to specific tasks and enables efficient task-switching during deployment all without introducing inference latency. LoRA also outperforms several other adaptation methods including adapter, prefix-tuning, and fine-tuning....
Efficient Fine-Tuning: Each example provided in the project has been meticulously fine-tuned to showcase the specific strengths and nuances of the respective language model. This fine-tuning process ensures optimal performance and relevance for various NLP tasks and scenarios. Jupyter Notebook Format...
from transformers.utils import check_min_version, send_example_telemetry from transformers.utils.versions import require_version logger = logging.getLogger(__name__) MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys()) MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_...
The implementation of LoRA enabled us to run the Whisper large fine-tuning task on a single GPU instance (for example, ml.g5.2xlarge). In comparison, the Whisper large full fine-tuning task requires multiple GPUs (for example, ml.p4d.24xlarge) and a much longer training time. More speci...
Parameter-Efficient Fine-Tuning(PEFT)可以用于在不触及LLM的所有参数的情况下对LLM进行有效的微调。PEFT支持QLoRa方法，通过4位量化对LLM参数的一小部分进行微调。Transformer Reinforcement Learning (TRL)是一个使用强化学习来训练语言模型的库。TRL也提供的监督微调(SFT)训练器API可以让我们快速的微调模型。!pip ...
Parameter-Efficient Fine-Tuning(PEFT)可以用于在不触及LLM的所有参数的情况下对LLM进行有效的微调。PEFT支持QLoRa方法,通过4位量化对LLM参数的一小部分进行微调。 Transformer Reinforcement Learning (TRL)是一个使用强化学习来训练语言模型的库。TRL也提供的监督微调(SFT)训练器API可以让我们快速的微调模型。