kmseong/llama3.2_3b_SSFT_epoch5
The kmseong/llama3.2_3b_SSFT_epoch5 is a 3.2 billion parameter language model based on the Llama-3.2-3B-Instruct architecture, specifically fine-tuned using the Safety Neuron Tuning (SN-Tune) method. This model enhances safety alignment by selectively fine-tuning only critical 'safety neurons' on the Circuit Breakers dataset. It is designed to provide improved safety while preserving general capabilities and offering parameter-efficient fine-tuning.
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Model Overview
kmseong/llama3.2_3b_SSFT_epoch5 is a 3.2 billion parameter language model derived from the meta-llama/Llama-3.2-3B-Instruct base model. Its key differentiator is the application of Safety Neuron Tuning (SN-Tune), a specialized fine-tuning methodology aimed at enhancing model safety.
Key Capabilities & Features
- Enhanced Safety Alignment: The model has undergone targeted fine-tuning on the Circuit Breakers dataset, specifically to improve its safety responses and reduce harmful outputs.
- SN-Tune Methodology: This unique approach identifies and isolates 'safety neurons' within the model. Only these critical neurons are fine-tuned on safety data, while all other parameters remain frozen.
- Parameter-Efficient Fine-tuning: By focusing only on a small subset of neurons, SN-Tune achieves safety improvements with minimal computational overhead and without significantly altering the model's general capabilities.
- Llama-3.2-3B-Instruct Base: Inherits the foundational capabilities and architecture of the Llama-3.2-3B-Instruct series, providing a robust base for general language tasks.
Good For
- Applications requiring strong safety alignment in a 3.2B parameter model.
- Use cases where preserving general model capabilities while improving safety is crucial.
- Developers looking for a parameter-efficient method to integrate safety features into Llama-3.2-3B-Instruct based models.
- Scenarios where a smaller, safety-tuned model is preferred over larger, less specialized alternatives.