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from typing import Dict, List, Any
class EndpointHandler():
def __init__(self , path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
# self.model= load_model(path)
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
path = "shredder-31/GA_model_Gemma_2b"
model = AutoModelForCausalLM.from_pretrained(path, quantization_config=bnb_config, device_map={"":0})
tokenizer = AutoTokenizer.from_pretrained(path, add_eos_token=True)
self.model = model
self.tokenizer = tokenizer
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str` | `PIL.Image` | `np.array`)
kwargs
Return:
A :obj:`list` | `dict`: will be serialized and returned
"""
encodeds = self.tokenizer(data['inputs'], return_tensors="pt", add_special_tokens=True)
generated_ids = self.model.generate(**encodeds, max_length=100 ,max_new_tokens=100, do_sample=False)
decoded = self.tokenizer.decode(generated_ids[0], skip_special_tokens=True)
return {'output':decoded[len(data['inputs']):]} |