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from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login
import os
import torch
PREFERED_MODEL = "pretrained"
if torch.cuda.is_available():
print("Using GPU")
device = torch.device("cuda")
print("GPU:", torch.cuda.get_device_name(0))
else:
print("Using CPU")
device = torch.device("cpu")
# Login to huggingface
token = os.getenv('HF_TOKEN')
login(token = token)
if PREFERED_MODEL == "pretrained":
#print("Using pretrained model")
model_id = "mattshumer/Llama-3-8B-16K"
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(model_id, device_map = "cuda", load_in_8bit = True)
print("loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id)
print("Pretrained model loaded.")
elif PREFERED_MODEL == "fine-tuned":
print("Using fine-tuned model")
model_id = os.getenv('MODEL_ID')
if model_id is None:
raise ValueError("MODEL_ID is not set")
print("Loading model...")
model = AutoModelForCausalLM.from_pretrained(model_id, device_map = "cuda", load_in_8bit = True)
print("loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id)
print("Fine-tuned model loaded.")
def answer(prompt):
inputs = tokenizer.encode(prompt, add_special_tokens=False, return_tensors="pt").to(device)
prompt_length = len(tokenizer.decode(inputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True))
outputs = model.generate(inputs, max_length=150, do_sample=True, top_p=0.95, top_k=60, pad_token_id=tokenizer.eos_token_id)
generated = tokenizer.decode(outputs[0])[prompt_length:]
return generated
if __name__ == "__main__":
prompt = "Who is Leonardo Da Vinci?"
print(answer(prompt)) |