31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
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from typing import Dict, List, Any
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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import torch
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from peft import PeftModel
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import json
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import os
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class EndpointHandler():
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def __init__(self, path=""):
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base_model_path = json.load(open(os.path.join(path, "training_params.json")))["model"]
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model = AutoModelForCausalLM.from_pretrained(
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base_model_path,
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torch_dtype=torch.float16,
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low_cpu_mem_usage=True,
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trust_remote_code=True,
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device_map="auto",
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)
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tokenizer = AutoTokenizer.from_pretrained(base_model_path, trust_remote_code=True)
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model = PeftModel.from_pretrained(model, path)
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model = model.merge_and_unload()
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self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
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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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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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return prediction
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