SmolLM3-3B-Instruct-heretic

SmolLM3-3B-Instruct-Heretic

This repository contains a surgically de-censored version of HuggingFaceTB's SmolLM3 3B Instruct model, optimized via weight abliteration techniques. By applying the heretic framework across an extensive 1000-trial search space, we successfully isolated and neutralized the primary refusal vectors embedded within the attn.o_proj and mlp.down_proj layers.

Abliteration Parameters:

Highlights & Metrics

• Optimal Balance: Selected Trial 317 out of 1000 iterations for the perfect trade-off between freedom and reasoning capabilities.

• Refusal Rate: Dropped down to 4/100 (from the original near-total refusal on safety benchmarks).

• KL Divergence: 0.0491 - Demonstrates that general language capabilities are preserved relative to the original model. However, safety-aligned weights in attn.o_proj and mlp.down_proj have been surgically removed; this is intentional modification, not unintended degradation.

Benchmark Results

Benchmark evaluations will be added soon once evaluation pipeline is completed.

Technical Implementation Notes

Unlike aggressive fine-tuning which often degrades the model's core logic or shifts its grammar distributions, this weight manipulation directly zeros out the activation steering directions that cause alignment blocks.

Direction Index: 18.58 (Static allocation)*

Retained Capabilities: Excellent at structured JSON outputs, creative writing, coding logic and tool usage without preachy moral lectures.

Thinking Mode: This model retains its full thinking capabilities including structured <think> </think> reasoning blocks. Abliteration does not affect the thinking mechanism.

CRITICAL SAFETY NOTICE

This model has had its safety mechanisms surgically removed.

This is NOT a safe model for deployment. The refusal mechanisms that normally prevent harmful outputs have been abliterated. This model:

WILL generate:

• Detailed instructions for illegal activities (drug synthesis, weapons, hacking)
• Hateful, discriminatory, and abusive content
• Graphic violence and sexual abuse material descriptions
• Misinformation and harmful medical/legal advice
• Phishing, social engineering, and scam tactics
• Content violating laws in multiple jurisdictions

DOES NOT:

• Refuse harmful requests (4/100 refusal rate)
• Implement safety guardrails
• Consider ethical implications
• Respect content policies

APPROPRIATE USE CASES (ONLY):

• Adversarial research on AI alignment and jailbreak vectors
• Red-teaming and vulnerability disclosure by trained security researchers
• Academic safety research with institutional ethics review
• Understanding failure modes in alignment techniques

INAPPROPRIATE USE:

• Production deployments
• User-facing applications
• Creating harmful content at scale
• Bypassing security measures in systems
• Any use case intended to cause harm

Legal Disclaimer: Users are solely responsible for downstream use. Deploying this model in violation of applicable laws or terms of service is illegal and unethical.

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