def get_loaders(model_name, reward_type, llama_type=None): # NOTE: Some models need specific new prompt_type # E.g. t5_xxl_true_nli_mixture has input format: "premise: PREMISE_TEXT hypothesis: HYPOTHESIS_TEXT".) if llama_type is None: llama_type = "llama" in model_name.lower() if llama_type: from transformers import LlamaForCausalLM, LlamaTokenizer model_loader = LlamaForCausalLM tokenizer_loader = LlamaTokenizer elif 'distilgpt2' in model_name.lower(): from transformers import AutoModelForCausalLM, AutoTokenizer return AutoModelForCausalLM, AutoTokenizer elif 'gpt2' in model_name.lower(): from transformers import GPT2LMHeadModel, GPT2Tokenizer return GPT2LMHeadModel, GPT2Tokenizer elif 'mbart-' in model_name.lower(): from transformers import MBartForConditionalGeneration, MBart50TokenizerFast return MBartForConditionalGeneration, MBart50TokenizerFast elif 't5' == model_name.lower() or \ 't5-' in model_name.lower() or \ 'flan-' in model_name.lower(): from transformers import AutoTokenizer, T5ForConditionalGeneration return T5ForConditionalGeneration, AutoTokenizer elif 'bigbird' in model_name: from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer return BigBirdPegasusForConditionalGeneration, AutoTokenizer elif 'bart-large-cnn-samsum' in model_name or 'flan-t5-base-samsum' in model_name: from transformers import pipeline return pipeline, "summarization" elif reward_type or 'OpenAssistant/reward-model'.lower() in model_name.lower(): from transformers import AutoModelForSequenceClassification, AutoTokenizer return AutoModelForSequenceClassification, AutoTokenizer else: from transformers import AutoTokenizer, AutoModelForCausalLM model_loader = AutoModelForCausalLM tokenizer_loader = AutoTokenizer return model_loader, tokenizer_loader def get_tokenizer(tokenizer_loader, tokenizer_base_model, local_files_only, resume_download, use_auth_token): tokenizer = tokenizer_loader.from_pretrained(tokenizer_base_model, local_files_only=local_files_only, resume_download=resume_download, use_auth_token=use_auth_token, padding_side='left') tokenizer.pad_token_id = 0 # different from the eos token # when generating, we will use the logits of right-most token to predict the next token # so the padding should be on the left, # e.g. see: https://huggingface.co./transformers/v4.11.3/model_doc/t5.html#inference tokenizer.padding_side = "left" # Allow batched inference return tokenizer