BERT(Bidirectional Encoder Representations from Transformers)是一个预训练的语言表示模型,BertTokenizer就是处理文本数据以适配BERT模型的工具。 GPT2LMHeadModel: GPT-2(Generative Pre-trained Transformer 2)是一个基于Transformer的自回归语言模型,用于生成文本。GPT2LMHeadModel是GPT-2模型的实现,可以用于文本生成...
transformers 3.5.1,run_clm.py 不使用3.5之前的版本,和其他包有冲突。 四、参数设置 train_data_file=path/gpt2/data/wikitext-2-raw/wiki.train.txt #上述路径下载的wikitext-2-raw文件,并更换后缀名为txt eval_data_file=path/gpt2/data/wikitext-2-raw/wiki.valid.txt model_type=gpt2 block_size=...
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...
"""Loads pretrained GPT-2 model weights from hugging-face""" assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large','gpt2-xl'} from transformers import GPT2LMHeadModel print("loading weights from pretrained gpt: %s" % model_type) #n_layer, n_head and n_embd are determined f...
importtorchfromtransformersimportGPT2LMHeadModel, GPT2Config, AutoModelForCausalLM# Step 1: Load the pre-trained GPT-2 XL modelpretrained_model = AutoModelForCausalLM.from_pretrained("gpt2-xl")# Step 2: Calculate the L2 norm of the weights for the pre-trained modelpretrained_weight_norm =0....
from transformers import GPT2Tokenizer from neuron_explainer.file_utils import copy_to_local_cache from neuron_explainer.scripts.download_from_hf import get_hf_model # === Expand Down Expand Up @@ -105,7 +105,7 @@ def create_hf_test_data( "-dir", "--savedir", type=str, default=...
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 ...
from transformers import TrainingArguments from peft import LoraConfig from trl import RewardTrainer training_args = TrainingArguments( output_dir="./train_logs", max_steps=1000, per_device_train_batch_size=4, gradient_accumulation_steps=1, learning_rate=1.41e-5, optim="adamw_torch", save_...
from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("{your_username}/opt-125m-gptq", device_map="auto") Exllama核心用于更快的推理 对于4位模型,您可以使用exllama核心以获得更快的推理速度。它默认是激活的。您可以通过在[GPTQConfig]中传递disable_exllama来改变这...
"from transformers.models.gpt2.tokenization_gpt2_fast import GPT2TokenizerFast\n", "from collections import defaultdict\n", "from rich.table import Table\n", "from rich import print as rprint\n", "import datasets\n", "from torch.utils.data import DataLoader\n", ...