初始化项目,由ModelHub XC社区提供模型
Model: Writer-Org/palmyra-base Source: Original Platform
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handler.py
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27
handler.py
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import torch
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from typing import Dict, List, Any
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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# check for GPU
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device = 0 if torch.cuda.is_available() else -1
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class EndpointHandler:
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def __init__(self, path=""):
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# load the model
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tokenizer = AutoTokenizer.from_pretrained(path)
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model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True)
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# create inference pipeline
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self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=device)
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def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", None)
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# pass inputs with all kwargs in data
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if parameters is not None:
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prediction = self.pipeline(inputs, **parameters)
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else:
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prediction = self.pipeline(inputs)
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# postprocess the prediction
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return prediction
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