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from typing import Any, Dict |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from peft import PeftConfig, PeftModel |
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class EndpointHandler: |
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def __init__(self, path=""): |
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self.tokenizer = AutoTokenizer.from_pretrained(path) |
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try: |
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config = PeftConfig.from_pretrained(path) |
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model = AutoModelForCausalLM.from_pretrained( |
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config.base_model_name_or_path, |
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return_dict=True, |
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load_in_8bit=True, |
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device_map="auto", |
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torch_dtype=torch.float16, |
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trust_remote_code=True, |
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) |
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model.resize_token_embeddings(len(self.tokenizer)) |
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model = PeftModel.from_pretrained(model, path) |
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except Exception: |
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model = AutoModelForCausalLM.from_pretrained( |
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path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16, trust_remote_code=True |
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) |
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self.model = model |
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self.device = "cuda" if torch.cuda.is_available() else "cpu" |
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]: |
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inputs = data.pop("inputs", data) |
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parameters = data.pop("parameters", None) |
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inputs = self.tokenizer(inputs, return_tensors="pt").to(self.device) |
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if parameters is not None: |
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outputs = self.model.generate(**inputs, **parameters) |
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else: |
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outputs = self.model.generate(**inputs) |
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prediction = self.tokenizer.decode(outputs[0], skip_special_tokens=True) |
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return [{"generated_text": prediction}] |
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