File size: 2,151 Bytes
65ef412 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | #!/usr/bin/env python3
"""
Quick test of the trained Prothom Alo model
"""
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
def test_model():
"""Test the fine-tuned model"""
print("🚀 Testing Prothom Alo Fine-tuned Model")
print("=" * 50)
# Load the fine-tuned model
model_path = "./prothomalo_model/final_model"
print(f"Loading model from: {model_path}")
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Test text generation
prompts = [
"The latest news from Bangladesh",
"In today's opinion piece",
"Government announces new policy"
]
for i, prompt in enumerate(prompts, 1):
print(f"\n🧪 Test {i}: {prompt}")
print("-" * 40)
# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt")
# Generate text
with torch.no_grad():
outputs = model.generate(
**inputs,
max_length=150,
num_return_sequences=1,
do_sample=True,
temperature=0.8,
pad_token_id=tokenizer.eos_token_id
)
# Decode and display
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(f"Generated: {generated_text}")
# Test Safetensors loading
print(f"\n🔒 Testing Safetensors Format")
print("-" * 40)
try:
from safetensors import safe_open
with safe_open("./prothomalo_model.safetensors", framework="pt", device=0) as f:
keys = list(f.keys())
print(f"✅ Safetensors loaded successfully!")
print(f"📊 Contains {len(keys)} tensors")
print(f"🔍 First 3 tensor names:")
for key in keys[:3]:
print(f" - {key}")
except Exception as e:
print(f"❌ Safetensors test failed: {e}")
print(f"\n🎉 Model testing completed!")
return True
if __name__ == "__main__":
test_model()
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