wvnvwn/gemma-2-9b-it-lr3e-5-gsm8k-lr5e-5
The wvnvwn/gemma-2-9b-it-lr3e-5-gsm8k-lr5e-5 is a 9 billion parameter instruction-tuned language model, based on Llama-3.2-3B-Instruct, that has been specifically fine-tuned for enhanced safety alignment. Utilizing the Safety Neuron Tuning (SN-Tune) method, it selectively fine-tunes only safety-critical neurons on a dedicated safety dataset. This approach aims to improve safety without significantly impacting general capabilities, making it suitable for applications requiring robust content moderation and responsible AI interactions.
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Model Overview
This model, wvnvwn/gemma-2-9b-it-lr3e-5-gsm8k-lr5e-5, is a 9 billion parameter instruction-tuned variant derived from the meta-llama/Llama-3.2-3B-Instruct base model. Its primary distinguishing feature is its Safety Neuron-Tuned (SN-Tune) fine-tuning, which focuses on enhancing safety alignment.
Key Capabilities & Features
- Enhanced Safety Alignment: Fine-tuned using the SN-Tune method on a "Circuit Breakers" safety dataset.
- Parameter-Efficient Fine-tuning: SN-Tune selectively fine-tunes only a small set of "safety neurons" while freezing other parameters, minimizing computational overhead.
- Preservation of General Capabilities: This selective tuning approach is designed to improve safety without significantly degrading the model's broader language understanding and generation abilities.
- Instruction-Tuned: Inherits instruction-following capabilities from its Llama-3.2-3B-Instruct base.
What is SN-Tune?
SN-Tune is an innovative fine-tuning technique that identifies and isolates neurons critical for safety-related responses. By exclusively training these specific neurons on safety data, the model achieves improved safety characteristics efficiently. This method ensures that the safety improvements are targeted, leading to minimal impact on the model's general performance.
When to Use This Model
This model is particularly well-suited for applications where safety and responsible AI interactions are paramount. If your use case requires a language model with strong safety guardrails and reduced propensity for generating harmful content, this SN-Tune version offers a specialized solution. It's ideal for scenarios where you need the general capabilities of a Llama-3.2-3B-Instruct-based model but with an added layer of safety alignment.