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"""
Verify Model Compatibility with Transformers.
Checks if the fused model can be loaded by standard Hugging Face Transformers.
Author: Ranjit Behera
"""
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
import sys
MODEL_PATH = "models/released/finance-extractor-v8-pytorch"
def verify_compatibility():
print(f"🔄 Verifying compatibility for: {MODEL_PATH}")
try:
# Try loading tokenizer
print("1. Loading Tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
print(" ✅ Tokenizer loaded successfully")
# Try loading model
print("2. Loading Model (PyTorch)...")
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=torch.float16,
device_map="auto",
trust_remote_code=True
)
print(" ✅ Model loaded successfully")
# Test generation
print("3. Testing Inference...")
prompt = "Extract financial entities from this email:\n\nRs.500 debited from HDFC A/c 1234.\n\nExtract: amount, bank\nOutput JSON:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=50)
print(" ✅ Generation successful")
print("\n🎉 The model is fully compatible with Hugging Face Transformers!")
return True
except Exception as e:
print(f"\n❌ Compatibility verification failed: {e}")
import traceback
traceback.print_exc()
return False
if __name__ == "__main__":
if verify_compatibility():
sys.exit(0)
else:
sys.exit(1)
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