kmseong/llama-3.1-8b-instruct-math-sn-tuned-lr5e-5
The kmseong/llama-3.1-8b-instruct-math-sn-tuned-lr5e-5 is an 8 billion parameter instruction-tuned Llama 3.1 model, developed by kmseong, with a 32768 token context length. It is specifically fine-tuned using the Safety Neuron Tuning (SN-Tune) method on safety alignment data. This model is optimized for enhanced safety alignment while preserving general capabilities, making it suitable for applications requiring robust safety features.
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Overview
This model, kmseong/llama-3.1-8b-instruct-math-sn-tuned-lr5e-5, is an 8 billion parameter instruction-tuned variant of the Llama 3.1 base model, specifically meta-llama/Llama-3.2-3B-Instruct. It has been fine-tuned using a novel approach called SN-Tune (Safety Neuron Tuning).
Key Capabilities & Features
- Enhanced Safety Alignment: The primary focus of this model is improved safety, achieved through targeted fine-tuning.
- SN-Tune Methodology: This method involves detecting and fine-tuning only a small subset of "safety neurons" on dedicated safety data, while freezing other parameters.
- Preservation of General Capabilities: By selectively tuning, the model aims to enhance safety with minimal impact on its broader performance.
- Parameter-Efficient Fine-tuning: SN-Tune offers an efficient way to achieve safety alignment without extensive retraining of the entire model.
- Base Model: Built upon the robust Llama 3.1 architecture, providing a strong foundation for its instruction-following abilities.
Use Cases
This model is particularly well-suited for applications where:
- Safety is paramount: Ideal for deployments requiring strong alignment against harmful outputs.
- Maintaining base model performance is crucial: When you need safety without significantly degrading the model's general instruction-following or reasoning capabilities.
- Efficient safety updates are desired: The SN-Tune method allows for targeted safety improvements.