ErisForge is a Python library that enables controlled modifications of Large Language Models by altering their internal layers to create varied responses, aiding in the study of propaganda and bias.
Python
2025-01-28
- website
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The ability to modify internal layers of LLMs allows for diverse experimental setups and controlled investigations into model behavior.
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Tools like AblationDecoderLayer and AdditionDecoderLayer provide unique ways to alter model responses, allowing detailed analysis of language behaviors.
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The library's focus on reducing bias and studying propaganda resonates well with current needs in AI ethics and alignment.
Improvement 1
Improving documentation with more examples and tutorials could enhance the onboarding experience for new users.
Improvement 2
A web-based UI could facilitate easier interaction with the library functions, particularly for non-technical users.
Improvement 3
Further optimizing the library for speed and efficiency could improve its usability in large-scale experiments.
Product Functionality
Consider integrating more advanced processing capabilities for larger datasets to improve performance on extensive LLMs.
UI & UX
Enhancing the UI/Ux could include developing a simple dashboard to visualize model transformations and results.
SEO or Marketing
Optimizing the website for SEO could involve creating content around related topics such as AI ethics, bias in LLMs, and case studies on the application of ErisForge.
MultiLanguage Support
Adding multi-language support could broaden the user base, making documentation and examples available in several languages to accommodate non-English speaking users.
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What is ErisForge?
ErisForge is a Python library designed to modify Large Language Models (LLMs) through transformations applied to their internal layers.
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What can I do with ErisForge?
You can use ErisForge to create altered behaviors of LLMs, measure response characteristics, and conduct studies on bias and propaganda.
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How do I get started with using ErisForge?
To get started, you can refer to the documentation on its GitHub page, which provides details on installation, features, and basic usage examples.