| import torch | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained('./') | |
| model = AutoModelForCausalLM.from_pretrained('./') | |
| def test_model(input_text, expected_output): | |
| inputs = tokenizer(input_text, return_tensors='pt') | |
| outputs = model.generate(**inputs, max_new_tokens=50) | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| assert generated_text == expected_output, f"Expected: {expected_output}, but got: {generated_text}" | |
| test_model("Salut comment vas tu?", "Salut comment vas tu") | |