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import time | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
class HFModel: | |
def __init__(self, model_name): | |
self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
self.model = AutoModelForCausalLM.from_pretrained(model_name) | |
def __call__(self, request): | |
prompt = request.get("prompt") | |
input_ids = self.tokenizer.encode(prompt, return_tensors='pt') | |
choices = self.model.generate(input_ids, max_length=50, do_sample=True) | |
choices = self.tokenizer.decode(choices, skip_special_tokens=True) | |
completion = { | |
'id': None, # fill in | |
'model': 'codegen', | |
'object': 'text_completion', | |
'created': int(time.time()), | |
'choices': None, # fill in | |
'usage': { | |
'completion_tokens': int(sum([len(c.split()) for c in choices])), | |
'prompt_tokens': int(len(prompt.split())), | |
'total_tokens': int(sum([len(c.split()) for c in choices]) + len(prompt.split())), | |
} | |
} | |
return completion, choices |