Everyone has a unique writing style, word choice, sentence structure, punctuation habits and that can be used to identify them. Lingunymous rewrites your text using a local LLM to mask these patterns, making it harder to attribute writing to a specific individual.
Use cases include:
- Protecting anonymity when publishing sensitive or controversial content
- Evading authorship attribution and stylometric analysis
- Separating your personal writing fingerprint from pseudonymous accounts
from gpt4all import GPT4All import argparse, sys, textwrap, subprocess, shutilMODEL = "Mistral-7B-Instruct-v0.3.Q4_0.gguf"🚮
Lie to someone else.
Only libre model ever published.
Anyway, packaging wise, you are alluding to a very real critical issue. The LLM is not packaged along with the Python code. It’s just some stupid module level constant.
That was such an obvious noob mistake, didn’t even bother mentioning it. It’s the elephant in the room. Amongst a herd of other problems.
This along with the lack of, well just about everything, makes me wonder when it’s time to pen an article and post it to this community.
I say this script falls well below that mark. Whatever that mark is.
` And then import this project as an existing project into Eclipse (or clone and import directly within Eclipse if you have the Eclipse eGit plugin).
This is currently the only ready way to compile and run Anonymouth. We will be including a updated build file soon so that you may build and run Anonymouth easily within the command land, but sadly it hasn’t been done yet so this is the only option currently `
What about for non-Java coders. I don’t even use an IDE.