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ff76dc9 64439b2 ff76dc9 64439b2 ff76dc9 64439b2 ff76dc9 64439b2 ff76dc9 64439b2 ff76dc9 64439b2 ff76dc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | from typing import Any, Dict
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
class EndpointHandler:
def __init__(self, path=""):
# load model and processor from path
self.tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(
path,
device_map="auto",
torch_dtype=torch.float16,
trust_remote_code=True,
)
self.model = model
self.device = "cuda" if torch.cuda.is_available() else "cpu"
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
messages=[{ 'role': 'user', 'content': inputs}]
# preprocess
inputs = self.tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(self.device)
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(inputs, num_return_sequences=1, eos_token_id=self.tokenizer.eos_token_id, **parameters) #, max_new_tokens=880
else:
outputs = self.model.generate(inputs, num_return_sequences=1, eos_token_id=self.tokenizer.eos_token_id) #, max_new_tokens=880
# postprocess the prediction
prediction = self.tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
return [{"generated_text": prediction}] |