from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel, PeftConfig

lora_path = "amithsourya/Script-Generate-4GL-V2.0"
peft_config = PeftConfig.from_pretrained(lora_path)

base_model = AutoModelForCausalLM.from_pretrained(
    peft_config.base_model_name_or_path,
    device_map="auto",
    torch_dtype="auto"
)

model = PeftModel.from_pretrained(base_model, lora_path)
tokenizer = AutoTokenizer.from_pretrained(peft_config.base_model_name_or_path)

import re
def clean_output(text):
    return re.sub(r'""([^""]+)""', r'"\1"', text)

from transformers import pipeline
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")

prompt = "Invoke a Service Script using Save point dispatcher"
output = pipe(
    prompt,
    max_new_tokens=256,
    eos_token_id=tokenizer.eos_token_id,
    return_full_text=False
)
print(clean_output(output[0]["generated_text"]))

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: T4 GPU
Hours used: 2H:30M

Example