kmseong/llama3_2_3b_instruct_only_sn_tuned_lr5e-5
The kmseong/llama3_2_3b_instruct_only_sn_tuned_lr5e-5 is a 3.2 billion parameter instruction-tuned Llama-3.2 model, developed by kmseong, that has undergone Safety Neuron Tuning (SN-Tune). This specialized fine-tuning method enhances safety alignment by selectively training only critical 'safety neurons' on safety data, while preserving general capabilities. It is designed for applications requiring improved safety performance with minimal impact on the base model's original functionalities.
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Model Overview
This model, kmseong/llama3_2_3b_instruct_only_sn_tuned_lr5e-5, is a 3.2 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. Its primary distinguishing feature is the application of SN-Tune (Safety Neuron Tuning), a selective fine-tuning approach developed by kmseong.
Key Capabilities & Features
- Enhanced Safety Alignment: Specifically fine-tuned using the Circuit Breakers dataset to improve safety responses.
- Parameter-Efficient Fine-tuning: SN-Tune focuses only on a small subset of 'safety neurons', freezing other parameters, which makes the fine-tuning process highly efficient.
- Preservation of General Capabilities: This method is designed to minimize any negative impact on the model's broader performance and general instruction-following abilities.
- Llama-3.2 Base: Inherits the architecture and foundational capabilities of the Llama-3.2-3B-Instruct model.
Use Cases
This model is particularly well-suited for applications where:
- Improved Safety is Critical: Ideal for deployments where mitigating harmful or undesirable outputs is a priority.
- Resource Efficiency is Valued: The parameter-efficient tuning makes it suitable for environments with computational constraints.
- Maintaining Base Model Performance is Important: Users can leverage the Llama-3.2's general capabilities with an added layer of safety alignment.