Spaces:
Sleeping
Sleeping
| # 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 | |
| 1. Download a compatible and capable model like: [Mixtral-8x7B-Instruct-v0.1-GGUF](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF/resolve/main/mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf?download=true) | |
| 2. Make sure it's named as expected by the next command. | |
| 3. Install dependencies: | |
| ``` | |
| pip install requests pydantic llama-cpp-python llama-cpp-python[server] --upgrade | |
| ``` | |
| 4. 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: | |
| ```python | |
| 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! |