saidutta69/Qwen2.5-Coder-3B-Instruct-heretic
The saidutta69/Qwen2.5-Coder-3B-Instruct-heretic model is a 3 billion parameter instruction-tuned variant of Qwen's Qwen2.5-Coder-3B-Instruct, developed by saidutta69. This model has been decensored using the Heretic v1.2.0 directional ablation method, which suppresses refusal behavior via targeted weight edits rather than fine-tuning. It is optimized for code generation and assistance, providing direct answers without refusal guardrails, and is suitable for local coding agents and copilot-style applications on consumer hardware.
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Qwen2.5-Coder-3B-Instruct-heretic Overview
This model is a specialized, decensored version of the Qwen/Qwen2.5-Coder-3B-Instruct, developed by saidutta69. It leverages the Heretic v1.2.0 directional ablation technique to suppress refusal behaviors present in the base model. This process involves targeted weight edits to the attention output and MLP down-projections, ensuring that the original model's knowledge and instruction-following capabilities remain largely intact while removing its inherent guardrails.
Key Differentiators & Capabilities
- Decensored Output: Unlike its base model, this variant is engineered to provide direct answers to requests that the original Qwen2.5-Coder-3B-Instruct would typically refuse. This is achieved through a unique "abliteration" process rather than traditional fine-tuning.
- Code-Focused: Inheriting from the Qwen2.5-Coder series, it remains highly effective for code generation, assistance, and related programming tasks.
- Lightweight: As a 3 billion parameter model, it is designed to run efficiently on smaller machines and consumer CPUs, including via GGUF implementations.
- Minimal KL Divergence: The abliteration process results in a very low KL divergence (0.0163) compared to the original model, indicating that its core instruction-following and knowledge are preserved.
- Significantly Reduced Refusals: Performance metrics show a drastic reduction in refusals, from 100/100 in the original model to just 3/100 in this variant.
Ideal Use Cases
This model is particularly well-suited for developers who require a code-focused language model without built-in refusal mechanisms. It is recommended for:
- Local Coding Agents: Powering autonomous coding tools that need to execute instructions directly.
- Copilot-Style Assistance: Providing uninhibited code suggestions and completions.
- Direct Code Generation: Generating code snippets and solutions without encountering content restrictions.
Users should be aware that the deliberate suppression of refusal behavior means the model will comply with requests the base model would refuse, including potentially unsafe ones. Responsible deployment and moderation are crucial, as it inherits the factual limitations and biases of the Qwen2.5-Coder-3B-Instruct base model without additional safety filtering.