|
|
|
|
|
|
|
|
|
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
import os
|
|
|
|
|
|
MODEL_PATH = os.getenv("MODEL_PATH", "./models/mistral-finetuned-mk")
|
|
|
|
|
|
def test_model():
|
|
|
model = AutoModelForCausalLM.from_pretrained(MODEL_PATH)
|
|
|
tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
|
|
|
sample_input = "Што е вештачка интелигенција?"
|
|
|
inputs = tokenizer(sample_input, return_tensors="pt")
|
|
|
outputs = model.generate(**inputs)
|
|
|
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
|
|
assert len(generated_text) > 5, "❌ Model output is too short!"
|
|
|
print("✅ Model test passed!")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
test_model()
|
|
|
|
