ChillTranslator ⁉️🌐❄️

This is an early experimental tool aimed at reducing online toxicity by automatically 🔄 transforming 🌶️ spicy or toxic comments into constructive, ❤️ kinder dialogues using AI and large language models. ❄️

ChillTranslator aims to foster healthier online interactions. The potential uses of this translator are vast, and exploring its integration could prove invaluable.

Currently, it "translates" a built-in example of a spicy comment.

Online toxicity can undermine the quality of discourse, causing distress 😞 and driving people away from online communities. Or worse: it can create a viral toxic loop 🌀!

ChillTranslator hopes to mitigate toxic comments by automatically rephrasing negative comments, while maintaining the original intent and promoting positive communication 🗣️➡️💬. These rephrased texts could be suggested to the original authors as alternatives, or users could enhance their internet experience with "rose-tinted glasses" 🌹😎, automatically translating spicy comments into versions that are easier and more calming to read.

Could Reddit, Twitter, Hacker News, or even YouTube comments be more calm and constructive places? I think so!

Approach ✨

Converts text to less toxic variations
Preserves original intent, focusing on constructive dialogue
Offline LLM model: running DIY could save costs, avoid needing to sign up to APIs, and avoid the risk of toxic content causing API access to be revoked. We use llama-cpp-python's server with Mixtral.

Possible future directions 🌟

Integration: offer a Python module and HTTP API, for use from other tools, browser extensions.
HuggingFace / Replicate.com etc: Running this on a fast system, perhaps on a HuggingFace Space could be good.
Speed improvements.
- Split text into sentences e.g: with “pysbd” for parallel processing of translations.
- Use a hate speech scoring model instead of the current "spicy" score method.
- Use a dataset of hate speech to make a dataset for training a translation transformer like Google's T5 to run faster than Mixtral could.
- Use natural language similarity techniques to compare possible rephrasing fidelity faster.
- Enabling easy experimenting with online hosted LLM APIs
- Code refactoring to improve development speed!

Getting Started 🚀

Installation

Download a compatible and capable model like: Mixtral-8x7B-Instruct-v0.1-GGUF
Make sure it's named as expected by the next command.
Install dependencies:

pip install requests pydantic llama-cpp-python llama-cpp-python[server] --upgrade

Start the LLM server:

python3 -m llama_cpp.server --model mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf  --port 5834 --n_ctx 4096 --use_mlock false

These config options are not going to be optimal for a lot of setups, as it may not use GPU right away, but this can be configured with a different argument. Please check out https://llama-cpp-python.readthedocs.io/en/latest/ for more info.

Usage

ChillTranslator currently has an example spicy comment it works on fixing right away. This is how to see it in action:

  python3 chill.py

Contributing 🤝

Contributions are welcome! Especially:

pull requests,
free GPU credits
LLM API credits / access.

ChillTranslator is released under the MIT License.

Help make the internet a kinder place, one comment at a time. Your contribution could make a big difference!