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import torch
import transformers
from typing import Dict, List, Any
class PreTrainedPipeline():
def __init__(self, path=""):
path = "oleksandrfluxon/mpt-7b-instruct-2"
print("===> path", path)
config = transformers.AutoConfig.from_pretrained(path, trust_remote_code=True)
config.max_seq_len = 4096 # (input + output) tokens can now be up to 4096
print("===> loading model")
model = transformers.AutoModelForCausalLM.from_pretrained(
path,
config=config,
torch_dtype=torch.bfloat16, # Load model weights in bfloat16
trust_remote_code=True,
load_in_4bit=True, # Load model in the lowest 4-bit precision quantization
)
print("===> model loaded")
tokenizer = transformers.AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b', padding_side="left", device_map="auto")
self.pipeline = transformers.pipeline('text-generation', model=model, tokenizer=tokenizer)
print("===> init finished")
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
"""
data args:
inputs (:obj: `str`)
parameters (:obj: `str`)
Return:
A :obj:`str`: todo
"""
# get inputs
inputs = data.pop("inputs",data)
parameters = data.pop("parameters", {})
date = data.pop("date", None)
print("===> inputs", inputs)
print("===> parameters", parameters)
result = self.pipeline(inputs, **parameters)
print("===> result", result)
return result |