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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
# dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler:
def __init__(self, path=""):
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
tokenizer.padding_side = "left"
tokenizer.pad_token = tokenizer.unk_token
model = AutoModelForCausalLM.from_pretrained(
path,
return_dict=True,
device_map="auto",
load_in_8bit=True,
torch_dtype=torch.bfloat16,
trust_remote_code=True
)
generation_config = model.generation_config
generation_config.max_new_tokens = 200
generation_config.temperature = 0.7
generation_config.top_p = 0.7
generation_config.num_return_sequences = 1
generation_config.pad_token_id = tokenizer.pad_token_id
generation_config.eos_token_id = tokenizer.eos_token_id
self.generation_config = generation_config
self.pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]:
prompt = data.pop("inputs", data)
result = self.pipeline(prompt, generation_config=self.generation_config)
return result |