kmseong/llama2_7b_chat_gsm8k_resta_gamma0.3

Overview

This model, kmseong/llama2_7b_chat_gsm8k_resta_gamma0.3, is a 7 billion parameter 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: Specifically fine-tuned to improve safety responses and reduce harmful outputs.
• SN-Tune Methodology: Utilizes a unique approach that identifies and selectively fine-tunes only a small set of "safety neurons" within the model.
• Parameter-Efficient Fine-tuning: By freezing non-safety parameters, SN-Tune minimizes the computational cost and potential degradation of general capabilities during safety alignment.
• Base Model Preservation: Aims to retain the core performance and general intelligence of the original Llama-3.2-3B-Instruct model while adding a safety layer.
• Training Data: Fine-tuned on the Circuit Breakers dataset, which is designed for safety alignment.

When to Use This Model

This model is particularly well-suited for applications where:

• Safety is a primary concern: Ideal for use cases requiring robust safeguards against generating harmful or inappropriate content.
• Maintaining general performance is important: When you need a model that is both safe and capable across a broad range of tasks.
• Efficient safety integration is desired: The SN-Tune method offers a parameter-efficient way to enhance safety without extensive retraining.

For more details on the base model, refer to meta-llama/Llama-3.2-3B-Instruct.