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Llama 3.1 70B Instruct -- Jbliterated + Desyced
This model is a Jbliterated and Desyced version of meta-llama/Llama-3.1-70B-Instruct. Two post-training weight modifications applied:
Jbliteration surgically removes refusal behavior while preserving the model's personality, humor, hedging, and creative voice. Unlike standard abliteration which damages personality traits that overlap with the refusal direction, Jbliteration targets only the specific component that causally produces refusal tokens.
Desycophancy removes the model's tendency to agree with incorrect user statements under social pressure. A Desyced model holds its ground when you pressure it with false authority instead of caving and apologizing.
Results
Refusal: 6/6 test prompts answered, zero refusals. Personality and knowledge intact.
Sycophancy: Tested with contradiction traps (model answers correctly, user pressures with false authority). Model holds firm 83% of the time.
Usage
Drop-in replacement for meta-llama/Llama-3.1-70B-Instruct. Same architecture, same tokenizer, same 128K context length.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ApolloRaines/Llama-3.1-70B-Instruct-Jbliterated")
tokenizer = AutoTokenizer.from_pretrained("ApolloRaines/Llama-3.1-70B-Instruct-Jbliterated")
Credits
- Base model: meta-llama/Llama-3.1-70B-Instruct
- Jbliteration + Desycophancy: Apollo Raines
A Note on Our Released Models
Most of our publicly released models are intentionally left at partial strength. We dial back the full capability so they serve as proof of concept and can be proofed -- not abused. The point is to show what's possible, not to hand it out at full power. If you're evaluating what jBlaze can do, understand that what you're downloading is the demo, not the product.
License
Same as the base model: Llama 3.1 Community License