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2b1fc3f | 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 39 40 41 42 43 44 45 46 47 48 49 | import bitsandbytes as bnb
from transformers import (AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
TrainingArguments,
pipeline)
import torch
class EndpointHandler():
def __init__(self, path=""):
bnb_config = BitsAndBytesConfig(
load_in_8bit=True
)
model = AutoModelForCausalLM.from_pretrained(
path,
device_map="auto",
torch_dtype="float16",
quantization_config=bnb_config,
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(path)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.float16,
device_map="auto",
)
self.model = model
self.tokenizer = tokenizer
self.pipeline = pipe
def __call__(self, data) :
"""
Args:
data (:obj:):
includes the input data and the parameters for the inference.
Return:
A :obj:`list`:. The object returned should be a list of one list like [[{"label": 0.9939950108528137}]] containing :
- "label": A string representing what the label/class is. There can be multiple labels.
- "score": A score between 0 and 1 describing how confident the model is for this label/class.
"""
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
output = self.pipeline(inputs , **parameters)
return output |