any plans for adding repo using stable vicuna for conversation .. human: assistant

[andysingal]

Hi,
Thanks for the amazing repo, checking if you have any plans to add a repo using stablevicuna.
Morever, if you can share hardware requirements to download the checkpoints, that will be awesome. I ran out of memory using Kaggle notebook.
Looking forward to hearing from you.
Best,
Andy

i tried to create for human bot within a single column message text but still getting error:

%%writefile sft_dataloader.py
def format_prompt(prompt: str) -> str:
    text = f"""
### Human: {prompt}
### Assistant:
    """
    return text.strip()


class SFTDataLoader(object):
    def __init__(self, data, CUTOFF_LEN, VAL_SET_SIZE, tokenizer) -> None:
        super(SFTDataLoader, self).__init__()

        self.data = data
        self.CUTOFF_LEN = CUTOFF_LEN
        self.VAL_SET_SIZE = VAL_SET_SIZE

        self.tokenizer = tokenizer

    def generate_prompt(self, data_point):
        return format_prompt(data_point["message_tree_text"])

    def tokenize(self, prompt):
        # there's probably a way to do this with the tokenizer settings
        # but again, gotta move fast
        return self.tokenizer(
            prompt,
            truncation=True,
            max_length=self.CUTOFF_LEN + 1,
            padding="max_length",
            return_unused_tokens=True,
        )

    def generate_and_tokenize_prompt(self, data_point):
        # This function masks out the labels for the input,
        # so that our loss is computed only on the response.
        user_prompt = format_prompt(data_point["message_tree_text"])
        len_user_prompt_tokens = len(
            self.tokenizer(
                user_prompt,
                truncation=True,
                max_length=self.CUTOFF_LEN + 1,
                padding="max_length",
                return_unused_tokens=True,
            )["input_ids"]
        )
        full_tokens = self.tokenizer(
            user_prompt,
            truncation=True,
            max_length=self.CUTOFF_LEN + 1,
            padding="max_length",
            return_unused_tokens=True,
        )["input_ids"]
        return {
            "input_ids": full_tokens[: len_user_prompt_tokens],
            "labels": full_tokens[len_user_prompt_tokens:],
            "attention_mask": [1] * len(full_tokens),
        }

    def load_data(self):
        train_val = formatted_dataset.train_test_split(
            test_size=self.VAL_SET_SIZE, shuffle=True, seed=42
        )
        train_data = train_val["train"].shuffle().map(
            self.generate_and_tokenize_prompt
        )
        val_data = train_val["test"].shuffle().map(
            self.generate_and_tokenize_prompt
        )

        return train_data, val_data