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import functools
def get_loaders(model_name, reward_type, llama_type=None, load_gptq=''):
# 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 load_gptq:
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
use_triton = False
functools.partial(AutoGPTQForCausalLM.from_quantized, quantize_config=None, use_triton=use_triton)
return AutoGPTQForCausalLM.from_quantized, AutoTokenizer
if llama_type is None:
llama_type = "llama" in model_name.lower()
if llama_type:
from transformers import LlamaForCausalLM, LlamaTokenizer
return LlamaForCausalLM.from_pretrained, LlamaTokenizer
elif 'distilgpt2' in model_name.lower():
from transformers import AutoModelForCausalLM, AutoTokenizer
return AutoModelForCausalLM.from_pretrained, AutoTokenizer
elif 'gpt2' in model_name.lower():
from transformers import GPT2LMHeadModel, GPT2Tokenizer
return GPT2LMHeadModel.from_pretrained, GPT2Tokenizer
elif 'mbart-' in model_name.lower():
from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
return MBartForConditionalGeneration.from_pretrained, 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.from_pretrained, AutoTokenizer
elif 'bigbird' in model_name:
from transformers import BigBirdPegasusForConditionalGeneration, AutoTokenizer
return BigBirdPegasusForConditionalGeneration.from_pretrained, 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.from_pretrained, AutoTokenizer
else:
from transformers import AutoTokenizer, AutoModelForCausalLM
model_loader = AutoModelForCausalLM
tokenizer_loader = AutoTokenizer
return model_loader.from_pretrained, 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
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