faker writer——致力于去掉机器味儿

[tardigrade2017]

Faker Writer

A lightweight LLM for fine-tuning ultra-large LLMs.

Why did we choose to develop this project?

While ultra-large-scale LLMs such as ChatGPT, Tongyi Qianwen, Wenxin Yiyuan, KImi, and Baichuan excel at executing complex instructions and grasping the intricacies of human language, their output is often distinguishable due to an inherent signature that arises from factors such as moral alignment. This distinctive quality makes it apparent that the text was generated by an LLM rather than a human being. However, in applications like creative writing, casual conversation, or role-playing scenarios, there is a pressing need for these models to produce language that is not only more natural but also convincingly indistinguishable from human authorship.

The advent of fine-tuning techniques for large models has indeed offered a means to cater to diverse, specialized requirements. Nevertheless, these methods come with substantial drawbacks, including exorbitant costs and inherent complexities. Notably, fine-tuning can lead to catastrophic forgetting, where the model loses previously learned knowledge during the customization process. As a result, the high financial outlay and technical challenges associated with fine-tuning pose significant barriers for small companies, individual developers, and non-profit organizations, rendering this approach largely inaccessible to such entities.

Therefore, developing a lightweight model capable of fine-tuning ChatGPT is of particular importance.

Data

Hundreds of thousands of data entries originate from the Chinese Internet. We have successfully employed these data to rectify Wenxin Yiyuan's linguistic patterns through fine-tuning.

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Faker Writer

一款用于微调超大规模LLM的轻量级LLM。

我们为何选择开发此项目？

尽管诸如ChatGPT、通义千问、文心一言、Kimi、百川等超大规模LLM在执行复杂指令及把握人类语言细微之处表现出色，但其输出往往因其内在特性（如道德倾向等因素导致的独特印记）而易于辨识为出自LLM而非人类之手。在创意写作、日常对话或角色扮演等情境中，迫切需要这类模型生成不仅更自然，而且能够令人信服地与人类创作难以区分的语言。

针对大型模型的微调技术的确为满足各类专业化需求提供了途径。然而，这些方法存在显著弊端，包括高昂成本和固有复杂性。特别是，微调可能导致灾难性遗忘，即模型在定制过程中丧失先前习得的知识。因此，微调所涉及的高额经济投入和技术难题对小公司、个人开发者以及非营利组织构成了重大障碍，使得这一方法对这些主体而言几乎遥不可及。

因此，研发一款能够微调ChatGPT的轻量级模型尤为重要。

数据

数十万条数据条目来自中国互联网。我们已成功运用这些数据通过微调校正了文心一言的语言模式。
example

欢迎有兴趣的同学加入我们团队！