24 lines
1.1 KiB
Python
24 lines
1.1 KiB
Python
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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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from transformers.generation.utils import GenerationConfig
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# get dtype
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dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
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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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self.model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype, trust_remote_code=True)
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self.model.generation_config = GenerationConfig.from_pretrained(path)
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self.tokenizer = AutoTokenizer.from_pretrained(path, use_fast=False, trust_remote_code=True)
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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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# ignoring parameters! Default to configs in generation_config.json.
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messages = [{"role": "user", "content": inputs}]
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response = self.model.chat(self.tokenizer, messages)
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if torch.backends.mps.is_available():
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torch.mps.empty_cache()
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return [{'generated_text': response}]
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