from transformers import GPT2TokenizerFast tokenizer = GPT2TokenizerFast.from_pretrained("gpt2") c. 检查网络问题 如果您尝试从网络加载tokenizer但遇到连接问题,检查您的网络连接或代理设置。 总结 检查本地目录:确保没有名为gpt2的文件夹干扰加载过程。 安装和更新库:确保transformers库已安装并更新到最新版本...
SAVE_STATE_WARNING = "" # from torch.optim.lr_scheduler import SAVE_STATE_WARNING ## 这一行是新注释掉的。 5.2 ValueError: You are attempting to pad samples but the tokenizer you are using (GPT2Tokenizer) does not have one. -- 换transformers版本,比如3.5.1 5.3 ACT2FN错误 -- 注释掉,不...
from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2', output_hidden_states=True) prompt = "今天天气非常好," input_ids = tokenizer.encode(prompt, return_tensors='pt') outputs = model.generate...
搞定数据后,接下来就需要创建(初始化)一个模型了,GPT 的结构其实就是由 transformer 组成的,网上的轮子已经很多了,这里就不重新造轮子了,最常见的直接用的 transformers 库,通过配置的方式就能够快速定义一个模型出来了。 from transformers import AutoTokenizer, GPT2LMHeadModel, AutoConfig config = AutoConfig.f...
其实,在很多大模型网站中,已经在默默的收集人类反馈信息,例如,我们在使用ChatGPT时,每一条提问都是一条prompt,大模型回复下面都会有两个icon,如果用户点击其中一个,同时又收集到了偏好反馈信息。 或者直接使用其它大模型生成prompts。 from transformers import pipeline, set_seed ...
[Huggingface Wenzhong-GPT2-3.5B](https://huggingface.co/IDEA-CCNL/Wenzhong-GPT2-3.5B) ### Load the Models ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('IDEA-CCNL/Wenzhong-3.5B') model = GPT2Model.from_pretrained('IDEA-CCNL/Wenz...
from transformers import AutoModelForCausalLM, AutoTokenizer from trl import AutoModelForSequenceClassification, PPOConfig, PPOTrainer # Step 1: Model Instantiation model = AutoModelForSequenceClassification.from_pretrained("gpt2") model_ref = AutoModelForSequenceClassification.from_pretrained("gpt2") ...
trainers import WordLevelTrainer from transformers import PreTrainedTokenizerFast from transformers import GPT2Config, TFGPT2LMHeadModel from transformers import CONFIG_NAME import tensorflow as tf data_folder = "data_folder" model_folder = "model_folder" pathlib.Path(data_folder).mkdir(parents=True,...
import torch.nn.functional as F from transformers import GPT2LMHeadModel, GPT2Tokenizer def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering Args: logits: logits distribu...
from transformers import AutoTokenizer, AutoModelForCausalLM from torch.distributions import Categorical import torch as t #Broken: model_name = "distilgpt2" #model_name = "gpt2" #model_name = "EleutherAI/gpt-neo-125M" #model_name = "EleutherAI/gpt-neo-1.3B" ...