kmseong/llama-3.1-8b-instruct-math-rsn-tuned-lr5e-5
The kmseong/llama-3.1-8b-instruct-math-rsn-tuned-lr5e-5 is an 8 billion parameter Llama-3.2-3B-Instruct model, fine-tuned by kmseong using the Safety Neuron Tuning (SN-Tune) method. This approach enhances safety alignment by selectively fine-tuning only critical 'safety neurons' on the Circuit Breakers dataset, while preserving general capabilities. It is designed to provide improved safety alignment with minimal impact on its original performance, making it suitable for applications requiring robust safety features.
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Model Overview
This model, kmseong/llama-3.1-8b-instruct-math-rsn-tuned-lr5e-5, is an 8 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. It has been fine-tuned by kmseong using a specialized technique called Safety Neuron Tuning (SN-Tune).
Key Capabilities & Features
- Enhanced Safety Alignment: Utilizes SN-Tune, a method that identifies and selectively fine-tunes only a small set of 'safety neurons' on dedicated safety data (Circuit Breakers dataset).
- Preservation of General Capabilities: By freezing non-safety parameters, the fine-tuning process aims to minimize any negative impact on the model's original performance and general instruction-following abilities.
- Parameter-Efficient Fine-tuning: The SN-Tune approach is designed to be highly efficient, focusing computational resources only on the most critical components for safety.
- Base Model: Built upon the robust Llama-3.2-3B-Instruct architecture, inheriting its foundational capabilities.
When to Use This Model
This model is particularly well-suited for use cases where:
- Safety is a primary concern: Applications requiring a higher degree of safety alignment and reduced generation of harmful content.
- Maintaining base model performance is crucial: Scenarios where the general instruction-following and reasoning capabilities of the Llama-3.2-3B-Instruct model need to be largely preserved.
- Efficient safety integration is desired: Developers looking for a model with targeted safety enhancements without extensive retraining or significant performance trade-offs.