Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 41
Generate 4GL Scripts from english prompts
from huggingface_hub import notebook_login
notebook_login()
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from peft import PeftModel, PeftConfig
lora_path = "amithsourya/Script-Generate-4GL-V1.0"
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)
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
prompt = "invoke a BO for read"
outputs = pipe(prompt, max_new_tokens=256)
print(outputs[0]["generated_text"])
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
Base model
meta-llama/Llama-3.2-1B-Instruct