我完成了chatgpt的微调fine tuning, 模型的表现结果到底怎么样??? 3514 1 8:39 App TinyLLAMA微调教程,小模型的时代要到来了 9585 2 8:27 App AI取代传统爬虫? 用GPT-4-vision获取网页信息 1203 -- 6:15 App 3个专业方法,让AI大模型变得更聪明,更理解你的需求 4229 1 16:05 App #4 Langchain...
OpenAI-GPT2 Kaggle short-jokes 数据集 Why will you need fine-tuning an LLM? LLMs are generally trained on public data with no specific focus. Fine-tuning is a crucial step that adapts a pre-trained LLM model to a specific task, enhancing the LLM responses significantly. Although text gener...
from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer model_path = "openai-community/gpt2" device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu') model = GPT2LMHeadModel.from_pretrained(model_path).to(device) tokenizer = GPT2Tokenizer.from_pretraine...
前面的文章中我们提到过,GPT1提出了大规模预训练的概念,用Large corpus pre-training + Supervised fine-tuning实现了通用language understanding。但GPT1最终的测试还是在单一数据集上做的。即大规模预训练完后,分别在每个有标签数据集上做了fine-tuning。这样的模型在GPT2文中被称为: Current systems are better ch...
1、传统的Fine Tuning方法通过将梯度传导给下游任务，微调模型的所有参数。2、在GPT-3中，我们曾介绍过...
一、什么是 fine-tuning GPT-3 使用大量互联网上的语料，训练完成后，并不适合对话这个场景。如给到 GPT3 输入“中国的首都是哪里？” GPT3 基于训练后的模型的参数进行推理，结果可能是“美国的首都是哪里？”。训练数据中，这两句话一起出现的概率非常高，在GPT3的训练预料里面可能也会出现多次。但这种...
RL-fine-tuning“I’m glad you’re here. I’m glad you have the power of Voice. According to the same human labelers used to train them, our fine-tuned models are preferred to the base GPT-2 model (zero-shot) 88% and 86% of the time for sentiment and descriptiveness, respectively...
如果说BERT模型还很巧妙地提出Maske Language Model Loss加上 Next Sentence Prediction Loss来让预训练模型学到更全面信息,那GPT系列则就只是把貌似平淡无奇的Transformer Decoder(单向解码)给加大再加大,当然好的数据也不可少,然后向大家展示大到一定程度后(GPT-2)非常厉害的,特别在语言生成上,刚好填补了BERT 的缺...
This tutorial demonstrates fine-tuning a GPT-2* model on Intel® Gaudi® AI processors using Hugging Face* Optimum for Intel library with DeepSpeed*.
MODEL_NAME = 'gpt2' pipe = transformers.pipeline(task='text-generation', model=MODEL_NAME, device='cpu') On the first run, it downloads the model gpt2 from the Hugging Face Hub and caches it locally in the cache directory (~/.cache/huggingfaceon Linux). On the ...