本周分享的这篇论文,是谷歌实习生的工作,主要在讨论如何微调大模型(PaLM 2),使得其可以更好的解决数学问题。 分享这篇论文的初衷,主要在于他提供了一个简单明了的Insight来解决这个问题,对于LLM的其他应用也有一定的指导意义。但坦白讲,论文质量,包括实验其实都是不太够的,ICLR得分(openreview.net/forum?)中也可以...
[11] Wang, L., Ma, C., Feng, X., Zhang, Z., Yang, H., Zhang, J., Chen, Z., Tang, J., Chen, X., Lin, Y., Zhao, W.X., Wei, Z., & Wen, J. (2023). A Survey on Large Language Model based Autonomous Agents. ArXiv, abs/2308.11432. [12] Jacob Devlin, Ming-Wei ...
Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities Arxiv:arxiv.org/pdf/2409.0344 Github:github.com/lamm-mit/llm 模型huggingface:huggingface.co/lamm-mit 对model merge的启发: merge前可以先做CPT-SFT diversity是...
bigger_finetuned_model = BasicModelRunner(model_name_to_id["bigger_model_name"]) bigger_finetuned_output = bigger_finetuned_model(test_question) print("Bigger (2.8B) finetuned model (test): ", bigger_finetuned_output) count = 0 for i in range(len(train_dataset)): if "keep the ...
微调大模型(Finetuning Large Language Models)—Data_preparation(四),本节讲述了大模型微调前的数据准备工作,最重要的是模型的tokenizer以及截断策略和数据的划分,自己的数据集在制作过程中,仅
For fine-tuning models that require the acceptance of an end-user license agreement, you must explicitly declare EULA acceptance when creating your AutoML job. Note that after fine-tuning a pretrained model, the weights of the original model are changed, so you do not need to later accept a...
自从GPT、EMLO、BERT的相继提出,以Pre-training + Fine-tuning 的模式在诸多自然语言处理(NLP)任务中被广泛使用,其先在Pre-training阶段通过一个模型在大规模无监督语料上预先训练一个预训练语言模型(Pre-trained Language Model,PLM),然后在Fine-tuning阶段基于训练好的语言模型在具体的下游任务上再次进行微调(Fine-...
Large language model (LLM) finetuning is a way to enhance the performance of pretrained LLMs for specific tasks or domains, with the aim of achieving improved inference quality with limited resources. Finetuning is crucial for domain-specific applications where pretrained models lack necessary ...
ChildTuning几乎没有翻车过,基本都完虐了vanilla finetune的模型,且提升幅度基本都高于原版。 freeze参数然后加入dense层进行transfer learning 这里给出的评测方式与上面稍有不同,他是先用MRPC任务数据集进行finetune,然后freeze参数,之后直接在模型上加入一个dense层之后放到其他的任务当中取训练,然后考察模型的效果。
Fine-tuning before SFT. Despite recent popularity of SFT, language model fine-tuning has long been a popular approach. For example, GPT [7] is fine-tuned directly on each task on which it is evaluated (see below), and encoder-only language models (e.g., BERT [8]) — due to the fa...