今天笔者就记录一下如何从https://huggingface.co这个网站手动下载模型,利用transformers这个python 包采用本地加载模型的方式完成一次文本分类的微调任务。 finetune前期准备 1.使用下方命令安装transformers的python包 pip install transformers 2.下载合适的预训练模型 这里笔者拿roberta为例,在huggingface网站搜索roberta,我...
The code will fine tune the gpt2 pretrained model using the wiki text dataset. It will run in distributed mode if multiple Gaudis are available. Note that for fine tuning, the argument “model_name_or_path” is used and it loads the model checkpoint for weights ...
from transformers import AutoModelForCausalLM# 模型名称MODEL_NAME = "gpt2"# 加载模型model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, trust_remote_code=True) 3. 加载 Tokenizer 通过HuggingFace,可以指定模型名称,运行时自动下载对应Tokenizer。直接使用transformers库中AutoTokenizer的from_pretrained函数,...
finetune GPT2 using Huggingface model app https://gpt2-rickbot.streamlit.app/ results model https://huggingface.co/code-cp/gpt2-rickbot dialogue bot after 1 epoch sample 0: Rick: I turned myself into a pickle, Morty! Morty: oh. *wetly* Rick: you know, in the world of Rick...
finetune前期准备 1.使用下方命令安装transformers的python包 pip install transformers 2.下载合适的预训练模型 这里笔者拿roberta为例,在huggingface网站搜索roberta,我这里找到哈工大的中文roberta,进入详情页点files and verisons。就会看到如下方图所示的模型文件和配置文件。
目前,主流的fine-tune方法主要分为全参数训练(Full Parameter)和参数高效训练(Parameter Efficient Tuning)。全参数训练就是对模型的所有参数进行训练,而参数高效训练则包括了Lora、QLora、Prefix tuning、P-tuning、Prompt tuning、IA3等多种方法。考虑到我需要在单个GPT上训练,我选择了QLora方法(https://arxiv.org...
主要用到Github上的gpt-2-flask-api库,只需要提供它一个预训练或者finetune好的GPT2模型(Huggingface的pytorch格式)。 将模型文件放在models/下,命名为gpt2-pytorch_model.bin也可以先用它提供的实例模型来做个实验: mkdir models curl --output models/gpt2-pytorch_model.bin 之后运行python deployment/run_serv...
I'm attempting to fine-tune gpt-j using the huggingface trainer and failing miserably. I followed the example that references bert, but of course, the gpt-j model isn't exactly like the bert model. The error indicates that the model isn't producing a loss, which is great, except that ...
初回実行時の--model_name_or_path=gpt2は、gpt2 ディレクトリのことではなく、HuggingFace の Pretrained モデルを指定しています。 --per_device_train_batch_sizeと--per_device_eval_batch_sizeのデフォルトは8ですが、そのままだとRuntimeError: CUDA out of memoryが出たので2に絞っています...
🤗 Datasets originated from a fork of the awesome TensorFlow Datasets and the HuggingFace team want to deeply thank the TensorFlow Datasets team for building this amazing library. Well, let’s write some code In this example, we will start with a pre-trainedBERT (uncased)model and fine-tune...