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import torch |
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import transformers |
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from typing import Dict, List, Any |
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class PreTrainedPipeline(): |
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def __init__(self, path=""): |
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path = "oleksandrfluxon/mpt-7b-instruct-2" |
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print("===> path", path) |
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config = transformers.AutoConfig.from_pretrained(path, trust_remote_code=True) |
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config.max_seq_len = 4096 |
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print("===> loading model") |
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model = transformers.AutoModelForCausalLM.from_pretrained( |
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path, |
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config=config, |
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torch_dtype=torch.bfloat16, |
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trust_remote_code=True, |
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load_in_4bit=True, |
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) |
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print("===> model loaded") |
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tokenizer = transformers.AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b', padding_side="left", device_map="auto") |
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self.pipeline = transformers.pipeline('text-generation', model=model, tokenizer=tokenizer) |
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print("===> init finished") |
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def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]: |
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""" |
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data args: |
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inputs (:obj: `str`) |
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parameters (:obj: `str`) |
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Return: |
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A :obj:`str`: todo |
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""" |
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inputs = data.pop("inputs",data) |
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parameters = data.pop("parameters", {}) |
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date = data.pop("date", None) |
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print("===> inputs", inputs) |
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print("===> parameters", parameters) |
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result = self.pipeline(inputs, **parameters) |
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print("===> result", result) |
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return result |
