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from typing import Dict, List, Any
import torch
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
class EndpointHandler():
def __init__(self, path=""):
# Preload all the elements you are going to need at inference.
# pseudo:
self.tokenizer = AutoTokenizer.from_pretrained("philschmid/falcon-40b-instruct-GPTQ-inference-endpoints", use_fast=False)
self.model = AutoGPTQForCausalLM.from_quantized("philschmid/falcon-40b-instruct-GPTQ-inference-endpoints", device="cuda:0", use_triton=False, use_safetensors=True, torch_dtype=torch.float32, trust_remote_code=True)
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
"""
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
# preprocess
input_ids = self.tokenizer(inputs, return_tensors="pt").input_ids
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(input_ids, **parameters)
else:
outputs = self.model.generate(input_ids)
# postprocess the prediction
prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": prediction}] |