Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 41
Generate 4GL Scripts from english prompts
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"]))
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Base model
microsoft/phi-2