darrellbest/Qwen3.5-4B-Heretic

VISIONPricing:Input $0.4 / Cached $0.08 / Output $0.8Concurrent Unit Cost:1Model Size:4.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 25, 2026License:apache-2.0Architecture:Transformer0.0K Open Weights Featherless Exclusive Cold

darrellbest/Qwen3.5-4B-Heretic is a 4.5 billion parameter language model based on Qwen/Qwen3.5-4B, specifically modified to significantly reduce refusal behavior. Utilizing Arbitrary-Rank Ablation (ARA) with full-weight application, it retains the original architecture, parameter count, vision encoder, and thinking control. This model is optimized for use cases requiring less restrictive safety guardrails, demonstrating a reduction in refusals from 99/100 to 6/100 while maintaining core functionalities like reasoning and vision capabilities.

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Qwen3.5-4B Heretic: Decensored Qwen3.5-4B

This model, darrellbest/Qwen3.5-4B-Heretic, is a 4.5 billion parameter variant of the original Qwen/Qwen3.5-4B that has undergone significant modification to reduce its refusal behavior. It leverages Arbitrary-Rank Ablation (ARA), a technique applied full-weight, to achieve this decensoring while preserving the original model's architecture, parameter count, vision encoder, and thinking control mechanisms.

Key Capabilities & Differentiators

  • Reduced Refusal Behavior: The primary distinction is a drastic reduction in refusals, from 99/100 in the original Qwen3.5-4B to just 6/100 in this Heretic version, as measured on mlabonne/harmful_behaviors.
  • Preserved Core Functionality: Despite the decensoring, the model maintains its original capabilities, including fluent text generation, accurate reasoning (e.g., arithmetic and word problems), and vision processing (e.g., describing image content).
  • Full Checkpoint Integrity: Unlike some partial modifications, this release provides a complete checkpoint, restoring multi-token-prediction tensors and ensuring all 738 tensors match the original in name, shape, and dtype.
  • Based on ARA Research: The ablation parameters were derived from findings for the 2B variant, scaled and applied to the 4B model, demonstrating effective transferability of ARA techniques.

Good for

  • Applications requiring fewer safety guardrails: Ideal for research or specific use cases where the default refusal behavior of Qwen3.5-4B is undesirable or overly restrictive.
  • Exploring model alignment and control: Useful for researchers studying methods of steering LLM behavior and understanding the impact of ablation techniques.
  • Maintaining Qwen3.5-4B's original strengths: Users who need the reasoning, vision, and general language capabilities of Qwen3.5-4B but with a significantly reduced tendency to refuse prompts.