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import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import warnings
import os
# Suppress INFO and WARNING messages from TensorFlow
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
warnings.filterwarnings("ignore", category=UserWarning, module='transformers.generation.utils')
def load_model_and_tokenizer():
base_model = "TheBloke/phi-2-GPTQ"
peft_model_id = "STEM-AI-mtl/phi-2-electrical-engineering"
config = PeftConfig.from_pretrained(peft_model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="cuda:0",return_dict=True, trust_remote_code=True)
model = model.to('cuda')
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = PeftModel.from_pretrained(model, peft_model_id, trust_remote_code=True)
model = model.to('cuda')
return model, tokenizer
def generate(instruction, model, tokenizer):
inputs = tokenizer(instruction, return_tensors="pt", return_attention_mask=False)
inputs = inputs.to('cuda')
outputs = model.generate(
**inputs,
max_length=350,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.9,
repetition_penalty=1,
)
text = tokenizer.batch_decode(outputs)[0]
return text
if __name__ == '__main__':
model, tokenizer = load_model_and_tokenizer()
while True:
instruction = input("Enter your instruction: ")
if not instruction:
continue
if instruction.lower() in ["exit", "quit", "exit()", "quit()"]:
print("Exiting...")
break
answer = generate(instruction, model, tokenizer)
print(f'Answer: {answer}')
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