test_local_hf.py

#!/usr/bin/env python3
"""Simple test for local HuggingFace models"""

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

def test_local_model():
    print("🧪 Testing Local HuggingFace Model...")
    
    # Check device
    device = "cuda" if torch.cuda.is_available() else "cpu"
    print(f"🖥️  Using device: {device}")
    
    # Load model
    model_name = "microsoft/DialoGPT-small"
    print(f"📦 Loading {model_name}...")
    
    try:
        tokenizer = AutoTokenizer.from_pretrained(model_name)
        model = AutoModelForCausalLM.from_pretrained(model_name)
        
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
        
        print("✅ Model loaded successfully!")
        
        # Test generation
        text = "Hello, how are you?"
        inputs = tokenizer.encode(text, return_tensors="pt")
        
        with torch.no_grad():
            outputs = model.generate(
                inputs, 
                max_new_tokens=50,
                do_sample=True,
                temperature=0.7,
                pad_token_id=tokenizer.eos_token_id
            )
        
        response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
        print(f"🤖 Model response: {response}")
        print("✅ Local HuggingFace model is working!")
        
        return True
        
    except Exception as e:
        print(f"❌ Error: {e}")
        return False

if __name__ == "__main__":
    success = test_local_model()
    if success:
        print("\n🎉 You can now run the main test with local HuggingFace models!")
    else:
        print("\n❌ Setup incomplete. Check the error messages above.")