saidutta69/Qwen2.5-Coder-3B-Instruct-heretic

TEXT GENERATIONPricing:Input $0.32 / Cached $0.064 / Output $1.6Concurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Mar 25, 2026License:otherArchitecture:Transformer0.0K Featherless Exclusive Cold

The saidutta69/Qwen2.5-Coder-3B-Instruct-heretic is a 3.1 billion parameter instruction-tuned causal language model, derived from Qwen's Qwen2.5-Coder-3B-Instruct. This variant has been decensored using Heretic v1.2.0, suppressing refusal behavior through targeted weight edits rather than fine-tuning. It is specifically designed for code-focused tasks, offering uninhibited code generation and assistance for developers, and runs efficiently on consumer hardware.

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Qwen2.5-Coder-3B-Instruct-heretic Overview

This model is a 3.1 billion parameter instruction-tuned variant of the Qwen2.5-Coder-3B-Instruct, specifically modified to suppress refusal behaviors. Developed by saidutta69 using the Heretic v1.2.0 tool, it achieves this by applying targeted weight edits to the attention output and MLP down-projections, preserving the base model's core knowledge and instruction-following capabilities.

Key Capabilities & Differentiators

  • Decensored Output: Significantly reduces refusal rates (3/100 compared to 100/100 for the original model) without altering the base model's knowledge.
  • Code-Focused: Inherits the strong code generation and assistance capabilities of the Qwen2.5-Coder-3B-Instruct.
  • Resource Efficient: At 3.1B parameters, it is optimized to run on smaller machines and consumer CPUs, with various GGUF quantizations available for different GPU memory configurations.
  • Direct Responses: Ideal for applications requiring direct answers and code generation without built-in guardrails.

Good For

  • Local coding agents and copilot-style assistance.
  • Code generation tasks where uninhibited output is desired.
  • Developers seeking a compact, powerful code model that runs on consumer hardware.

Note on Responsible Use: This model's refusal suppression is deliberate. Users are responsible for its deployment, as it will comply with requests the base model would typically refuse, and lacks additional safety filtering.