llama-vaccine-stance-ptbr-lora

pip install torch transformers peft accelerate

import warnings
import logging
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel

warnings.filterwarnings("ignore")
logging.getLogger("transformers").setLevel(logging.ERROR)
logging.getLogger("peft").setLevel(logging.ERROR)

base_model = "meta-llama/Llama-3.1-8B"
lora_model = "gseovana/llama-vaccine-stance-ptbr-lora"

# Access token required - request access at:
# https://huggingface.co/meta-llama/Llama-3.1-8B
HF_TOKEN = "your_huggingface_token_here"

tokenizer = AutoTokenizer.from_pretrained(base_model, token=HF_TOKEN)
tokenizer.pad_token = tokenizer.eos_token

model = AutoModelForSequenceClassification.from_pretrained(
    base_model,
    num_labels=3,
    dtype=torch.float16,
    device_map="auto",
    token=HF_TOKEN,
)
model.config.pad_token_id = tokenizer.pad_token_id
model = PeftModel.from_pretrained(model, lora_model, token=HF_TOKEN)
model.eval()

label_map = {0: "Against", 1: "Favorable", 2: "Inconclusive"}

text = "Vacinas são fundamentais para a saúde pública e salvam vidas."
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512).to(model.device)

with torch.no_grad():
    logits = model(**inputs).logits
    predicted_class = logits.argmax(dim=-1).item()

print(f"Predicted class: {predicted_class} -> {label_map[predicted_class]}")
# Predicted class: 1 -> Favorable