Cadet Tiny

🚀 Cadet-Tiny 是什么？

受 Allen AI 的 Cosmo-XL 启发，Cadet-Tiny 是一个基于 SODA 数据集训练的 超小型 对话模型。Cadet-Tiny 旨在用于边缘推理（甚至可以在仅有 2GB 内存的树莓派上运行）。

Cadet-Tiny 基于谷歌的 t5-small 预训练模型进行训练，因此，它的大小约为 Cosmo-3B 模型的 2%。

这是我制作的第一个 SEQ2SEQ 自然语言处理模型！我非常激动能在 HuggingFace 上与大家分享它！😊

如果您有任何问题或改进建议，请通过以下邮箱联系我：tcgoldfarb@gmail.com

📦 模型信息

📚 谷歌 Colab 链接

以下是谷歌 Colab 文件的链接，我在其中详细介绍了模型的训练过程以及如何使用 AI2 的 SODA 公共数据集。 点击访问

🚀 快速开始

使用以下代码片段开始使用 Cadet-Tiny！

import torch
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import colorful as cf

cf.use_true_colors()
cf.use_style('monokai')
class CadetTinyAgent:
    def __init__(self):
        print(cf.bold | cf.purple("Waking up Cadet-Tiny..."))
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.tokenizer = AutoTokenizer.from_pretrained("t5-small", model_max_length=512)
        self.model = AutoModelForSeq2SeqLM.from_pretrained("ToddGoldfarb/Cadet-Tiny", low_cpu_mem_usage=True).to(self.device)
        self.conversation_history = ""

    def observe(self, observation):
        self.conversation_history = self.conversation_history + observation
        # The number 400 below is just a truncation safety net. It leaves room for 112 input tokens.
        if len(self.conversation_history) > 400:
            self.conversation_history = self.conversation_history[112:]

    def set_input(self, situation_narrative="", role_instruction=""):
        input_text = "dialogue: "

        if situation_narrative != "":
            input_text = input_text + situation_narrative

        if role_instruction != "":
            input_text = input_text + " <SEP> " + role_instruction

        input_text = input_text + " <TURN> " + self.conversation_history

        # Uncomment the line below to see what is fed to the model.
        # print(input_text)

        return input_text

    def generate(self, situation_narrative, role_instruction, user_response):
        user_response = user_response + " <TURN> "
        self.observe(user_response)

        input_text = self.set_input(situation_narrative, role_instruction)

        inputs = self.tokenizer([input_text], return_tensors="pt").to(self.device)
        
        # I encourage you to change the hyperparameters of the model! Start by trying to modify the temperature.
        outputs = self.model.generate(inputs["input_ids"], max_new_tokens=512, temperature=0.75, top_p=.95,
                                      do_sample=True)
        cadet_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=False)
        added_turn = cadet_response + " <TURN> "
        self.observe(added_turn)

        return cadet_response

    def reset_history(self):
        self.conversation_history = []

    def run(self):
        def get_valid_input(prompt, default):
            while True:
                user_input = input(prompt)
                if user_input in ["Y", "N", "y", "n"]:
                    return user_input
                if user_input == "":
                    return default

        while True:
            continue_chat = ""

            # MODIFY THESE STRINGS TO YOUR LIKING :)
            situation_narrative = "Imagine you are Cadet-Tiny talking to ???."
            role_instruction = "You are Cadet-Tiny, and you are talking to ???."

            self.chat(situation_narrative, role_instruction)
            continue_chat = get_valid_input(cf.purple("Start a new conversation with new setup? [Y/N]:"), "Y")
            if continue_chat in ["N", "n"]:
                break

        print(cf.blue("CT: See you!"))

    def chat(self, situation_narrative, role_instruction):
        print(cf.green(
            "Cadet-Tiny is running! Input [RESET] to reset the conversation history and [END] to end the conversation."))
        while True:
            user_input = input("You: ")
            if user_input == "[RESET]":
                self.reset_history()
                print(cf.green("[Conversation history cleared. Chat with Cadet-Tiny!]"))
                continue
            if user_input == "[END]":
                break
            response = self.generate(situation_narrative, role_instruction, user_input)
            print(cf.blue("CT: " + response))


def main():
    print(cf.bold | cf.blue("LOADING MODEL"))

    CadetTiny = CadetTinyAgent()
    CadetTiny.run()


if __name__ == '__main__':
    main()

📄 引用与特别感谢

特别感谢 Hyunwoo Kim 与我讨论使用 SODA 数据集的最佳方法。如果您还没有了解过他们在 SODA、Prosocial-Dialog 或 COSMO 方面的工作，我建议您去看看！同时，也请阅读关于 SODA 的论文！论文信息如下：

@article{kim2022soda,
    title={SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization},
    author={Hyunwoo Kim and Jack Hessel and Liwei Jiang and Peter West and Ximing Lu and Youngjae Yu and Pei Zhou and Ronan Le Bras and Malihe Alikhani and Gunhee Kim and Maarten Sap and Yejin Choi},
    journal={ArXiv},
    year={2022},
    volume={abs/2212.10465}
}