def load_model_and_tokenizer()

in ml/eval/generate.py [0:0]


def load_model_and_tokenizer(model_path, trust_remote_code=False, use_auth_token=False):
    """Load a model and its tokenizer."""
    model = AutoModelForCausalLM.from_pretrained(
        model_path, trust_remote_code=trust_remote_code, use_auth_token=use_auth_token,
    ).to(device)

    tokenizer = AutoTokenizer.from_pretrained(
        model_path, trust_remote_code=trust_remote_code, use_auth_token=use_auth_token
    )
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token


    # Setup chat format if not present
    if tokenizer.chat_template is None:
        model, tokenizer = setup_chat_format(model, tokenizer)
    return model, tokenizer