from transformers import AutoTokenizer, AutoModelForCausalLM, Trainer, TrainingArguments
from datasets import load_dataset, load_from_disk

#post training
model_path = "./results/checkpoint-152000" 
model = AutoModelForCausalLM.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained("tinyllama/tinyllama-1.1b-chat-v1.0")


input_text = """
H: After all that we have gone through, the truth is written literally and not literately. i have gazed navally and looked to the stars above.
How would you consider the case of man today amidst all this chaotica?

B:
"""
input_ids = tokenizer.encode(input_text, return_tensors='pt')
output = model.generate(
    input_ids=tokenizer.encode(input_text, return_tensors="pt"),
    max_length=1000,
    num_return_sequences=1, 
    no_repeat_ngram_size=5,
    temperature=0.9,
    top_k=50,  
    top_p=0.98, 
    do_sample=True,
    num_beams=10
)

decoded_output = tokenizer.decode(output[0], skip_special_tokens=True)
print(decoded_output)