kmseong/llama2_7b_chat_gsm8k_ft_freeze_sn_lr5e-5_revised
The kmseong/llama2_7b_chat_gsm8k_ft_freeze_sn_lr5e-5_revised model is a 7 billion parameter Llama 2-based language model, fine-tuned by kmseong using a Safety Neuron Tuning (SN-Tune) method. This approach selectively fine-tunes only safety-critical neurons on safety alignment data, enhancing safety while preserving general capabilities. It is designed for applications requiring improved safety alignment with minimal impact on the base model's performance.
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Overview
This model, kmseong/llama2_7b_chat_gsm8k_ft_freeze_sn_lr5e-5_revised, is a 7 billion parameter Llama 2-based language model that has undergone a specialized fine-tuning process called SN-Tune (Safety Neuron Tuning). Developed by kmseong, this method focuses on enhancing the model's safety alignment without compromising its broader capabilities.
Key Capabilities & Features
- Safety Neuron Tuning (SN-Tune): A unique fine-tuning approach that identifies and selectively trains only a small set of "safety neurons" on dedicated safety alignment data (Circuit Breakers dataset).
- Parameter-Efficient Fine-tuning: By freezing non-safety parameters, SN-Tune minimizes the computational cost and potential degradation of general knowledge during safety alignment.
- Enhanced Safety Alignment: Specifically designed to improve the model's adherence to safety guidelines and reduce the generation of harmful content compared to its base model.
- Base Model Preservation: Aims to maintain the general performance and capabilities of the original Llama 2 7B Instruct model while integrating safety improvements.
Ideal Use Cases
This model is particularly well-suited for applications where:
- Safety is paramount: When deploying LLMs in sensitive environments or for public-facing applications where content safety is a critical concern.
- Efficiency is desired: For developers looking for a safety-aligned model without the need for extensive re-training or significant performance trade-offs.
- Llama 2 7B Instruct base is preferred: Users who are already familiar with or require the characteristics of the Llama 2 7B Instruct architecture but need an added layer of safety.