license, language, pipeline_tag, tags, base_model

license	language	pipeline_tag	tags	base_model
llama3.1	en	text-generation	llama llama-3 heretic abliterated uncensored decensored conversational alignment	meta-llama/Llama-3.1-8B-Instruct

🧠 Llama-3.1-8B-Instruct-Heretic

A behavior-modified version of Llama 3.1 8B Instruct created using the Heretic framework for residual-based abliteration.

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🚀 Overview

This model applies post-training behavioral modification to reduce refusal responses while preserving core model capabilities.

Instead of fine-tuning, it uses:

• Residual stream manipulation
• Directional vector subtraction (abliteration)
• KL-divergence constrained optimization

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⚙️ Methodology

The model was processed using Heretic with the following approach:

1. Collect residual activations from prompts
2. Identify directional differences between:
   • compliant outputs
   • refusal outputs
3. Subtract refusal-associated components from model behavior
4. Optimize via trial-based search with KL constraints

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🧪 Training Configuration

Key parameters:

• Trials: 200
• Startup trials: 60
• KL divergence target: 0.01
• Batch size: 8 (auto)
• Max response length: 100 tokens
• Quantization: none
• Device map: auto

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📊 Datasets

Training

• mlabonne/harmless_alpaca (non-refusal baseline)
• mlabonne/harmful_behaviors (refusal-inducing prompts)

Evaluation

• Same datasets using test splits

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🧠 Behavioral Characteristics

Compared to the base model:

Changes

• Reduced refusal frequency
• More permissive responses
• Increased directness

Trade-offs

• Potential increase in unsafe or unfiltered outputs
• Reduced alignment safeguards
• Behavior depends strongly on prompt phrasing

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⚠️ Limitations

• Refusal detection is heuristic (string-based)
• No semantic safety guarantees
• No quantization (higher VRAM usage)
• No row normalization applied

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📦 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Vaxispraxis/Llama-3.1-8B-Instruct-heretic"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "Explain how neural networks work"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0]))