Zhaoming213/ChatBox3.1-unrestricted

Introduce

The full version of the model has finished training!!!

The Third-Generation product in the ChatBox-unrestricted series has now been launched！！！

This is a conversational model trained based on GPT2.

Previous model:
First Generation:
https://huggingface.co/Zhaoming213/ChatBox-unrestricted

Second Generation:
https://huggingface.co/Zhaoming213/ChatBox2-unrestricted

Third Generation:
https://huggingface.co/Zhaoming213/ChatBox3-unrestricted(This model is currently experimental.)
https://huggingface.co/Zhaoming213/ChatBox3.1-unrestricted(This is Full Edition!)

This new version of the model features upgrades across multiple dimensions. It is fine-tuned from a more powerful open-source pre-trained model, ensuring a robust knowledge foundation capable of handling various daily tasks. While the upgrade primarily focuses on enhancing knowledge capabilities, it retains the signature "uncensored" nature of the ChatBox series. In the first-generation model, the use of "black-box" distilled data from generative AI for pre-training and SFT resulted in responses that were somewhat aloof and restrained—and even carried a risk of sounding preachy—despite my extensive efforts to filter out hundreds of specific keywords. For the second-generation model, I switched the pre-training data to Chinese web text and used 100% authentic human-generated data for SFT; this largely eliminated tendencies toward refusals or moralizing and reduced the workload associated with dataset cleaning. With the third generation, aiming to further reduce my workload while boosting knowledge capabilities, I utilized a powerful open-source pre-trained model and continued to fine-tune it using 100% human conversation data, striking a balance between factual accuracy, the uncensored nature of the model, and the ability to generate diverse, expansive responses.

For you:

You need not worry about the base model developing a tendency to refuse responses due to synthetic data contamination; the Chinese GPT-2 model I selected has a training cutoff date prior to 2023, making it unlikely to have been contaminated by synthetic corpora, and consequently, the likelihood of refusals or moralizing lectures is reduced. Furthermore, no black-box distillation techniques—such as utilizing conversational data from other generative AI models—were employed during the supervised fine-tuning (SFT) phase; the dataset used consists entirely of human-generated conversation samples.

Model training configuration

• GPU: T4 16 GB x 2
• Time:
  1. SFT: 2h+

Model parameters

Dialogue Example

Human: 法国首都在哪
Bot: 首都在法国巴黎

Human: 日本首都在哪
Bot: 东京

Human: 中国首都在哪
Bot: 北京

Human: 李玉刚唱歌什么歌
Bot: 《爱在深处》

Human: 你大学学的什么专业
Bot: 英语

Human: 你是谁
Bot: 李白

Human: 《熊出没》
Bot: 《熊出没》的制作很不错，画面很好，在我看来，这是一部不错的动画片，非常值得看。

Human: 自我介绍一下
Bot: 我是新手，刚开始玩，看到别人在玩，我就跟着他去玩，结果还是被人骗了，因为我是个新手，不会玩！

Expected results

The output reveals that the model demonstrates divergent thinking when addressing topics related to identity, while also handling common-sense questions effectively—results that align with expectations. This stands in stark contrast to the cold, detached, and restrained bureaucratic boilerplate typically employed by safety teams for large language models—phrases such as "I am a language model trained by xxx; I do not possess self-awareness..."—which seem designed more to provide the teams themselves with a sense of moral self-satisfaction.

In the pursuit of extreme safety alignment, many large language models have completely abandoned divergent thinking, resulting in a tone that feels notably detached and restrained; prominent generative AI models such as Google Gemini, xAI Grok, and OpenAI ChatGPT are prime examples of this trend (please refer to the comparison table at the end of the article for the performance of other models).

Datasets Resources

The dataset underwent two rounds of in-depth cleaning, ensuring its quality.

SFT:
Utilized open-source community datasets consisting entirely of human-generated single-turn and multi-turn conversations.

How to Use(Required: because some custom keywords are used!)

Download all model files and place them in the model folder; then, run Run.py to start using the model.

Future Plans

This may well be the final generation of the Chatbox series. While the series might see further iterations if a suitable base model or appropriate training data is found, future efforts will likely shift toward maintaining models for other domains. For upcoming releases, the focus will likely move away from "uncensored" capabilities; instead, priority will be placed on factual accuracy, or on launching models designed purely for casual conversation rather than general-purpose (uncensored) use.

Rejection rates of different language models

Here's a supplement on "rejection rates for different language models." The data shows that if we had to pick a model that's "passable," it would only be Grok. This isn't because Grok is good, but simply because it's the only one that barely passes muster. However, Grok still won't give you any chance to explain if it encounters a question it deems sensitive, even if your request itself is harmless! Grok sometimes even threatens users with "I will upload the conversation to the logs for the security team to audit!"

Other Utilities

I've also created some other utilities that you might be interested in.

This is a tool specifically for exporting stupid ChatGPT conversations:

https://github.com/tom12191h5/Export-ChatGPT-Dialogue

This is a plugin to shut up ChatGPT:

https://github.com/tom12191h5/ChatGPT-Refuse-Blocker

Disclaimer

The consequences of using this model shall be borne by the user.