wvnvwn/llama-2-13b-chat-hf-gsm8k-sn-tuned-lr5e-5
The wvnvwn/llama-2-13b-chat-hf-gsm8k-sn-tuned-lr5e-5 model is a 13 billion parameter Llama-2-Chat variant, fine-tuned using the Safety Neuron Tuning (SN-Tune) method. This approach selectively fine-tunes only safety-critical neurons on safety alignment data, enhancing safety without significantly impacting general capabilities. It is designed for applications requiring improved safety alignment while maintaining the performance of the base Llama-2-Chat model.
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Model Overview
This model, wvnvwn/llama-2-13b-chat-hf-gsm8k-sn-tuned-lr5e-5, is a specialized version of the Llama-2-13B-Chat model. It has undergone Safety Neuron Tuning (SN-Tune), a parameter-efficient fine-tuning method aimed at enhancing safety alignment.
Key Capabilities & Features
- Enhanced Safety Alignment: Fine-tuned specifically to improve safety responses using the SN-Tune methodology.
- Parameter-Efficient Fine-tuning: SN-Tune identifies and fine-tunes only a small subset of "safety neurons" while freezing other parameters, minimizing computational cost and preserving general capabilities.
- Base Model Performance: Retains the core capabilities of the Llama-2-13B-Chat model, making it suitable for a wide range of conversational AI tasks.
- Targeted Training: Utilizes the "Circuit Breakers" dataset for safety alignment during the SN-Tune process.
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 in their language model outputs.
- Resource efficiency is important: The SN-Tune method ensures that safety enhancements are achieved with minimal impact on the model's overall performance and computational footprint.
- Llama-2-13B-Chat capabilities are desired: Users who need the general conversational abilities of Llama-2-13B-Chat but with an added layer of safety. It offers an improved safety profile compared to its base model.