BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jan 28, 2026License:apache-2.0Architecture:Transformer0.1K Open Weights Warm

BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 is an abliterated version of the Z-Image-Turbo text encoder, specifically modified to reduce generation refusals. Developed by BennyDaBall, this model targets both image generation and general refusals, achieving a low refusal rate of 4/100 in torture tests with a minimal KL Divergence of 0.0004. It is designed to enable more permissive content generation, making it suitable for applications requiring fewer content restrictions.

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Overview

BennyDaBall/Qwen3-4b-Z-Image-Turbo-AbliteratedV1 is a specialized text encoder derived from the Z-Image-Turbo model. Its primary innovation lies in its "abliterated" design, which significantly reduces content generation refusals. The model was developed by BennyDaBall using a "p-e-w heretic method" applied over 1000 trials.

Key Capabilities

  • Reduced Refusals: Engineered to minimize both image generation and general content refusals.
  • High Fidelity: Maintains a very low KL Divergence of 0.0004, indicating minimal alteration to the original model's core functionality despite the ablation process.
  • Performance: Achieved a refusal rate of only 4/100 in rigorous testing scenarios.

Good For

  • Applications requiring a more permissive text encoder for image generation prompts.
  • Use cases where overcoming built-in content restrictions is a priority.
  • Developers seeking a model that generates content "what you want, when you want it" with fewer limitations.

Available Formats

The model is provided in various GGUF quantization formats, ranging from F16 (8.05 GB) for full precision to Q2_K (1.67 GB) for extreme size optimization, offering flexibility for different hardware and performance needs.

Popular Sampler Settings

Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.

temperature
top_p
top_k
frequency_penalty
presence_penalty
repetition_penalty
min_p