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README.md

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+# 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 signup 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** improvments.
+    - 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 traning 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 refacoring 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!