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from typing import Dict, Any
from transformers import AutoModelForCausalLM, BitsAndBytesConfig, AutoTokenizer
import torch
class EndpointHandler:
def __init__(self, path=""):
model_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16
)
# load model and processor from path
self.model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
self.tokenizer = AutoTokenizer.from_pretrained(model_id)
def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
"""
Args:
data (:dict:):
The payload with the text prompt and generation parameters.
"""
# process input
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
inputs = f"[INST] {inputs} [/INST]"
# preprocess
inputs = self.tokenizer(inputs, return_tensors="pt")
inputs = inputs.to(self.model.device)
# pass inputs with all kwargs in data
if parameters is not None:
outputs = self.model.generate(inputs, **parameters)
else:
outputs = self.model.generate(inputs)
# postprocess the prediction
prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return [{"generated_text": prediction}] |