saidutta69/phi-4-heretic
The saidutta69/phi-4-heretic is a 14.7 billion parameter decensored variant of Microsoft's phi-4 model, developed by RACER IS OP. It retains the strong reasoning capabilities, code generation, and multilingual support of the base model, with refusal behaviors suppressed through targeted weight edits rather than fine-tuning. This model is optimized for developers seeking a capable 14B reasoning model that provides direct answers without refusal, suitable for deployment on consumer hardware.
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phi-4-heretic: A Decensored phi-4 Variant
This model, created by RACER IS OP, is a 14.7 billion parameter decensored version of the microsoft/phi-4 base model. It leverages a novel "abliteration" technique (directional ablation) to suppress refusal behaviors by editing specific weight directions responsible for refusal, rather than traditional fine-tuning. This approach ensures that the base model's strong reasoning capabilities, code generation, and multilingual support remain largely intact, without degrading coherence.
Key Capabilities & Differentiators
- Decensored Output: Deliberately suppresses refusal behavior, providing direct answers to queries the base model might refuse.
- Retained Base Model Strengths: Inherits the robust reasoning, code generation, and multilingual support of the original Microsoft phi-4 model.
- Abliteration Method: Utilizes targeted weight edits (abliteration) for refusal suppression, preserving core model knowledge and capabilities more effectively than fine-tuning.
- Efficient Deployment: Designed to be runnable on consumer hardware, with GGUF quants available (e.g., Q4_K_M).
When to Use This Model
- Developers requiring a capable 14B reasoning model from the phi-4 family that provides direct, unfiltered responses.
- Use cases where the base model's refusal guardrails are undesirable and direct answers are prioritized.
- Applications where the model's strong code generation and multilingual support are beneficial, without the overhead of larger models.
Note: This model is intentionally designed to comply with requests the base model would refuse. Users are responsible for its deployment and ensuring appropriate moderation for public-facing applications, as no safety filtering is layered on top.