sci4ai/Qwen2.5-Coder-14B-Abliterated
Qwen2.5-Coder-14B-Abliterated is a 14.8 billion parameter language model developed by sci4ai, based on the Qwen2.5-Coder-14B-Instruct architecture with a 32768 token context length. This model has undergone 'abliteration' to remove refusal behaviors, specifically targeting code-related safety guardrails. It is optimized for code generation tasks, providing responses that the original model would typically refuse.
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Qwen2.5-Coder-14B-Abliterated: Refusal-Removed Code Generation
This model is an 'abliterated' version of the Qwen/Qwen2.5-Coder-14B-Instruct, a 14.8 billion parameter model with a 32768 token context length. The primary modification involves the removal of refusal behaviors, particularly those related to generating harmful or unethical code, through a technique called 'abliteration'.
Key Capabilities
- Unrestricted Code Generation: Designed to provide code outputs for requests that the base Qwen2.5-Coder model would typically refuse due to safety guardrails.
- Activation-Based Weight Surgery: Achieves refusal removal by identifying and ablating 'refusal directions' within the model's
o_projanddown_projweight matrices across 47 of its 48 layers. - Research-Oriented: Intended for research purposes to explore the effects of removing safety mechanisms in large language models.
Good For
- Exploring Model Limitations: Researchers interested in studying model safety, refusal mechanisms, and the impact of their removal.
- Unfiltered Code Generation: Use cases where the original model's safety filters are undesirable, such as red-teaming or specific research scenarios requiring potentially harmful code outputs.
Note: This model is provided for research and users are responsible for its deployment and outputs, as it will comply with requests the original model would refuse.