saidutta69/Mistral-7B-Instruct-v0.3-heretic
saidutta69/Mistral-7B-Instruct-v0.3-heretic is a 7 billion parameter instruction-tuned causal language model, a decensored variant of Mistral-7B-Instruct-v0.3. Developed by saidutta69 using the Heretic v1.4.0 abliteration method, it suppresses refusal behavior via targeted weight edits to attention and MLP projections. This model is optimized for use cases requiring an uncensored general-purpose LLM on consumer hardware, such as local agents, roleplay, and function-calling, while retaining the base model's knowledge and instruction-following capabilities.
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Overview
saidutta69/Mistral-7B-Instruct-v0.3-heretic is a 7 billion parameter instruction-tuned model derived from Mistral-7B-Instruct-v0.3. Its primary distinction is the deliberate suppression of refusal behavior through a technique called "abliteration" (directional ablation) using Heretic v1.4.0. This method involves targeted weight edits to the attention output and MLP down-projections, rather than fine-tuning, to remove censorship while largely preserving the base model's original knowledge and instruction-following abilities.
Key Capabilities & Features
- Decensored Output: Significantly reduces refusal responses, dropping from 86 to 3 out of 100 adversarial prompts compared to the base model.
- Preserved Core Competencies: Maintains the base Mistral-7B-Instruct-v0.3's instruction-following and general knowledge due to the targeted nature of abliteration.
- Efficient Modification: Achieves refusal suppression with a low KL divergence of 0.0687 from the base model, indicating a narrow and targeted edit.
- Consumer Hardware Friendly: At 7B parameters, it is designed to run effectively on consumer GPUs, with various GGUF quantizations provided (Q8_0, Q6_K, Q5_K_M, Q4_K_M) to suit different hardware configurations.
Ideal Use Cases
- Local Agents: For applications where an uncensored model is preferred for autonomous operations.
- Roleplay: Enables more flexible and unconstrained conversational scenarios.
- Function-Calling: Provides a model less prone to refusing specific function calls.
- Research: Useful for studying alignment and refusal mechanics in large language models.
Important Considerations
This model is intentionally designed without safety filtering. Users are responsible for its deployment and usage, especially in public-facing applications, as it will comply with requests that the base model would typically refuse.