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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftConfig |
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from peft import PeftModel |
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import torch.cuda |
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from typing import Any, Dict |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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class EndpointHandler(): |
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def __init__(self, path=""): |
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config = PeftConfig.from_pretrained(path) |
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model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path) |
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self.tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path) |
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self.model = PeftModel.from_pretrained(model, path) |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, Any]: |
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""" |
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Args: |
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data (Dict): The payload with the text prompt |
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and generation parameters. |
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""" |
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prompt = data.pop("inputs", None) |
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parameters = data.pop("parameters", None) |
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if prompt is None: |
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raise ValueError("Missing prompt.") |
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input_ids = self.tokenizer(prompt, return_tensors="pt").input_ids.to(device) |
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if parameters is not None: |
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output = self.model.generate(input_ids=input_ids, **parameters) |
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else: |
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output = self.model.generate(input_ids=input_ids) |
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prediction = self.tokenizer.decode(output[0]) |
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return {"generated_text": prediction} |
