wvnvwn/gemma-2-9b-it-lr5e-5-gsm8k-lr5e-5
The wvnvwn/gemma-2-9b-it-lr5e-5-gsm8k-lr5e-5 is a 9 billion parameter instruction-tuned language model, based on the Llama-3.2-3B-Instruct architecture. This model has undergone Safety Neuron Tuning (SN-Tune) on safety alignment data, specifically targeting enhanced safety without significantly impacting general capabilities. It is designed for applications requiring improved safety alignment and parameter-efficient fine-tuning.
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Overview
This model, wvnvwn/gemma-2-9b-it-lr5e-5-gsm8k-lr5e-5, is a 9 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. Its primary distinguishing feature is the application of Safety Neuron Tuning (SN-Tune), a specialized fine-tuning method aimed at enhancing safety alignment.
Key Capabilities & Features
- Enhanced Safety Alignment: Utilizes SN-Tune to specifically target and improve the model's safety responses.
- Parameter-Efficient Fine-tuning: SN-Tune works by identifying and fine-tuning only a small subset of "safety neurons" while freezing other parameters, minimizing computational overhead.
- Minimal Impact on General Capabilities: This selective fine-tuning approach is designed to improve safety without degrading the base model's broader performance.
- Based on Llama-3.2-3B-Instruct: Inherits the foundational capabilities of its Llama-3.2-3B-Instruct base.
When to Use This Model
This model is particularly well-suited for use cases where:
- Safety is a critical concern: Applications requiring a higher degree of safety alignment in their language model outputs.
- Resource efficiency is important: The SN-Tune method offers a parameter-efficient way to achieve safety improvements.
- Building upon Llama-3.2-3B-Instruct: Developers already familiar with or using the Llama-3.2-3B-Instruct base model can leverage this version for enhanced safety.