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I Created ErisForge, a Python Library for Abliteration of LLMs

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I Created ErisForge, a Python Library for Abliteration of LLMs

ErisForge is a Python library designed to modify Large Language Models (LLMs) by applying transformations to their internal layers. Named after Eris, the goddess of strife and discord, ErisForge allows you to alter model behavior in a controlled manner, creating both ablated and augmented versions of LLMs that respond differently to specific types of input. It is also quite useful to perform studies on propaganda and bias in LLMs (planning to experiment with deepseek). Features - Modify internal layers of LLMs to produce altered behaviors. - Ablate or enhance model responses with the AblationDecoderLayer and AdditionDecoderLayer classes. - Measure refusal expressions in model responses using the ExpressionRefusalScorer. - Supports custom behavior directions for applying specific types of transformations.

Visit productView on Hacker NewsJan 27, 2025
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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, created · Missing: reddit linkedin, podcasting, latex
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: model, models, using · Missing: mac, agents, macos
73%73% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
69%69% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
35%35% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

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