kmseong/llama3_2_3b_instruct_resta_0.3_lr5e-5
The kmseong/llama3_2_3b_instruct_resta_0.3_lr5e-5 is a 3.2 billion parameter Llama-3.2-3B-Instruct model, fine-tuned by kmseong using the Safety Neuron-Tuned (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/llama3_2_3b_instruct_resta_0.3_lr5e-5, is a 3.2 billion parameter instruction-tuned variant of the Llama-3.2-3B-Instruct base model. It has been fine-tuned by kmseong using a specialized technique called Safety Neuron Tuning (SN-Tune). The primary goal of this fine-tuning is to significantly enhance the model's safety alignment without compromising its general capabilities.
Key Capabilities & Features
- Enhanced Safety Alignment: Utilizes SN-Tune, a method that identifies and fine-tunes only a small set of "safety neurons" critical for safety.
- Parameter-Efficient Fine-tuning: Achieves safety improvements by freezing most parameters and only adjusting safety-critical neurons, making the process efficient.
- Minimal Impact on General Performance: Designed to maintain the base model's overall performance while boosting safety.
- Training Data: Fine-tuned on the Circuit Breakers dataset, which is specifically curated for safety alignment.
When to Use This Model
This model is particularly well-suited for use cases where:
- Safety is a paramount concern: Ideal for applications requiring a high degree of safety alignment in generated content.
- Resource efficiency is important: Its parameter-efficient fine-tuning means it can offer safety benefits without requiring extensive computational resources for further adaptation.
- You need a Llama-3.2-3B-Instruct variant with improved safety characteristics compared to the base model.