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# predict.py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import argparse
import os
# -------------------------------
# Config
# -------------------------------
MODEL_NAME = "Qwen/Qwen3-0.6B-Base"
CHECKPOINT_DIR = "./artifacts/qwen_loRA"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MAX_NEW_TOKENS = 64
# -------------------------------
# Load tokenizer and model
# -------------------------------
print("🔄 Loading tokenizer and model...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
tokenizer.pad_token = tokenizer.eos_token
base_model = AutoModelForCausalLM.from_pretrained(
MODEL_NAME,
torch_dtype=torch.float16,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, CHECKPOINT_DIR)
model.eval()
model = model.to(DEVICE)
# -------------------------------
# Generate Summary
# -------------------------------
def generate_summary(title: str, post: str) -> str:
prompt = f"Title: {title}\n\nPost: {post}\n\nSummary:"
inputs = tokenizer(prompt, return_tensors="pt", padding=True).to(DEVICE)
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=MAX_NEW_TOKENS,
do_sample=True,
top_k=50,
top_p=0.95,
temperature=0.7,
pad_token_id=tokenizer.pad_token_id,
use_cache=True
)
full_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
summary = full_output.split("Summary:")[-1].strip()
return summary
# -------------------------------
# CLI
# -------------------------------
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate summary with trained Qwen PPO model")
parser.add_argument("--title", type=str, required=True, help="Title of the post")
parser.add_argument("--post", type=str, required=True, help="Content of the post")
args = parser.parse_args()
print("\n📘 Title:", args.title)
print("📝 Post:", args.post[:100] + ("..." if len(args.post) > 100 else ""))
print("\n🤖 Generating summary...\n")
summary = generate_summary(args.title, args.post)
print("✅ Summary:\n", summary) |