thomaskuo/gemma-3-12b-it-heretic
The thomaskuo/gemma-3-12b-it-heretic is a 12 billion parameter instruction-tuned Gemma 3 model, abliterated using the Heretic tool to significantly reduce refusals while maintaining model quality. This model is specifically optimized as an uncensored text encoder for video generation models like LTX-2, ensuring more faithful adherence to creative prompts. It features a 32768 token context length and exhibits minimal model damage with a KL divergence of 0.0826, achieving 93% reduction in refusals compared to the base model.
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Overview
This model, thomaskuo/gemma-3-12b-it-heretic, is an abliterated version of Google's Gemma 3 12B IT, created by thomaskuo using the Heretic tool. Its primary purpose is to serve as an uncensored text encoder, particularly for video generation models like LTX-2, by reducing the base model's tendency to refuse or sanitize certain concepts.
Key Capabilities & Features
- Reduced Refusals: Achieves a significant reduction in refusals (7/100 vs. 100/100 for the original model), meaning 93% of previously refused prompts now work.
- Minimal Model Damage: The abliteration process resulted in a low KL divergence of 0.0826, indicating that the core model quality is largely preserved.
- Enhanced Prompt Adherence: By removing soft censorship, the model ensures more faithful encoding of creative prompts, leading to stronger adherence and less altered visual outputs in downstream applications like LTX-2.
- Versatile Formats: Available in HuggingFace
safetensors, ComfyUIsafetensors(bf16, fp8), and various GGUF quantizations (F16, Q8_0, Q6_K, Q5_K_M, Q4_K_M, Q3_K_M) for compatibility withtransformers, ComfyUI, andllama.cpp.
Ideal Use Cases
- Video Generation: Specifically designed for use as a text encoder within video generation workflows, such as with LTX-2, where uncensored and faithful prompt interpretation is crucial.
- Creative Applications: Suitable for applications requiring an instruction-tuned model that avoids sanitization or weakening of creative concepts in its outputs.
- Research & Experimentation: Useful for researchers exploring the impact of refusal reduction techniques on large language models and their downstream applications.