kmseong/llama-3.2-3b-instruct-only-sn-tuned-lr5e-5
The kmseong/llama-3.2-3b-instruct-only-sn-tuned-lr5e-5 is a 3.2 billion parameter Llama-3.2-3B-Instruct model, fine-tuned by kmseong using the Safety Neuron Tuning (SN-Tune) method. This model is specifically enhanced for safety alignment by selectively fine-tuning only critical safety neurons on the Circuit Breakers dataset. It aims to provide improved safety while preserving general capabilities, making it suitable for applications requiring robust safety features.
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Overview
This model, kmseong/llama-3.2-3b-instruct-only-sn-tuned-lr5e-5, is a specialized version of the meta-llama/Llama-3.2-3B-Instruct base model. It has been fine-tuned by kmseong using a novel method called Safety Neuron Tuning (SN-Tune) to enhance its safety alignment.
Key Capabilities
- Enhanced Safety Alignment: The primary feature of this model is its improved safety, achieved through the SN-Tune method.
- Parameter-Efficient Fine-tuning: SN-Tune selectively fine-tunes only a small set of "safety neurons" while freezing other parameters, making the process highly efficient.
- Preservation of General Capabilities: This selective tuning approach aims to minimize the impact on the model's original general language understanding and generation abilities.
What is SN-Tune?
SN-Tune is a fine-tuning technique that involves:
- Identifying specific neurons within the model that are crucial for safety responses.
- Freezing all other model parameters.
- Fine-tuning only these identified safety neurons using dedicated safety alignment data, such as the Circuit Breakers dataset.
Good For
- Applications where safety and responsible AI behavior are paramount.
- Developers looking for a Llama-3.2-3B-Instruct variant with improved resistance to generating harmful content.
- Use cases requiring a balance between general instruction-following capabilities and strong safety guardrails.