dmitry315/llm-course-hw1

Homework for VK NLP course.

Contains BPE Tokenizer and Transformer Model weights for Russian jokes generation.

Model was trained on IgorVolochay/russian_jokes dataset on next token generation.

Code for model class is available on VK NLP course.

How to use

device = torch.device("cuda")
# for cpu:
# device = torch.device("cpu")

# generate
tokenizer = ByteLevelBPETokenizer.from_pretrained(REPO_NAME)
check_model = TransformerForCausalLM.from_pretrained(REPO_NAME)
check_model = check_model.to(device)
check_model = check_model.eval()

# generate
text = "Штирлиц пришел домой" # your joke start is here
input_ids = torch.tensor(tokenizer.encode(text)[:-1], device=device)
model_output = check_model.generate(
    input_ids[None, :], max_new_tokens=200, eos_token_id=tokenizer.eos_token_id, do_sample=True, top_k=10
)
print(tokenizer.decode(model_output[0].tolist()))
# > Штирлиц пришел домой, а снега вдруг с кем-то на лесу и пьет. Слушай, а тот сейчас и весь день победила, но не сбежал. Просто у нее сдалось.