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from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
class EndpointHandler:
def __init__(self, path=""):
# Load the model
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained(
"Qwen/Qwen2-1.5B-Instruct",
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
device_map="cuda" if torch.cuda.is_available() else "auto" # Include device_map for correct device allocation
)
# Create inference pipeline without specifying the device
self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
def __call__(self, data: Any) -> List[List[Dict[str, Any]]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", {})
if isinstance(inputs, str):
inputs = [inputs]
# Get predictions from the pipeline
prediction = self.pipeline(inputs, **parameters)
return prediction
# Example usage
if __name__ == "__main__":
handler = EndpointHandler()
data = {
"inputs": "Hello, how can I",
"parameters": {"max_length": 50, "num_return_sequences": 1}
}
result = handler(data)
print(result)
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