license: apache-2.0
language:
- en
base_model: - llmfan46/gemma-4-E4B-it-ultra-uncensored-heretic
tags: - EnglishOnly
- TextOnly
- NSFW
- Uncensored
- Decensored
- Heretic
- ARA
- VisionRemoved
- AudioRemoved
- Gemma4
- Gemma
After thorough usage and testing, I chose two UNCENSORED Gemma 4 E4B Models to be pruned (converted to English and Text-to-Text only) so I can use them on my potato laptop/phone.
The Source Models
- Profile: The most uncensored G4-E4B model.
- Stats: 0/656 manually checked refusals (according to the author). KL divergence value of 0.068 (very negligible).
2. Gemma-4-E4B-it-ultra-uncensored-heretic (Base for this pruned version)
- Profile: The most intelligent uncensored G4-E4B model with the lowest KL divergence on Hugging Face.
- Stats: KL divergence of 0.0076. Refusals 3/100 (which you probably won't notice anyways).
This pruned model is the more intelligent one, while still being really heavily uncensored.
Check out the other one here: Gemma-4-E4B-it-uncensored-pruned-Text-and-English-ONLY-GGUF.
Modifications
What's Changed:
- Vision & Audio Modules: REMOVED (Just Text-to-Text now).
- Language Tokens: ~61,000 (23.1%) REMOVED This includes non-Latin, Cyrillic, Arabic, Asian languages, etc.
- Note: Languages using some Latin letters like German and Vietnamese are still there. Latin was not removed to keep the logic alive.
- Weights: Cut down from >2300 to 720 (loads fast as fahhhh).
What DID NOT Change:
- Layers
- Logic (as far as I tested)
- Context
- No training/retraining done
TL;DR: Overall, both pruned models are faster and significantly less resource-intensive.
Base Model Usage Settings
You might want to change these according to your preferences, but here is the baseline:
- Temperature: 1.0
- Top P: 0.95
- Top K: 64
- Jinja
--jinja(don't ignore this one, especially on llama.cpp!)
Colab Notebook I made and used to prune these models:
Thanks:
- InfinimindCreations
- llmfan46
- Claude.ai (for coding the notebook)
- Kaggle.com (for the free resources)
These were my first attempts at anything to do with modifying a model so if you wanna suggest something, do it.