wvnvwn/qwen2.5-7b-instruct-gsm8k-sn-tuned-lr3e-5
This is a 7.6 billion parameter instruction-tuned language model, wvnvwn/qwen2.5-7b-instruct-gsm8k-sn-tuned-lr3e-5, based on the Llama-3.2-3B-Instruct architecture. It has been fine-tuned using the Safety Neuron Tuning (SN-Tune) method on safety alignment data. This specialized tuning enhances safety alignment while preserving general capabilities, making it suitable for applications requiring robust safety features.
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Model Overview
This model, wvnvwn/qwen2.5-7b-instruct-gsm8k-sn-tuned-lr3e-5, is a 7.6 billion parameter instruction-tuned variant derived from the meta-llama/Llama-3.2-3B-Instruct base model. Its primary distinguishing feature is the application of Safety Neuron Tuning (SN-Tune), a selective fine-tuning approach.
Key Capabilities & Features
- Enhanced Safety Alignment: The model has undergone specific fine-tuning on a 'Circuit Breakers' dataset, targeting improved safety responses.
- SN-Tune Methodology: This innovative method identifies and exclusively fine-tunes a small subset of 'safety neurons' within the model. All other parameters remain frozen.
- Preservation of General Capabilities: By selectively tuning only safety-critical neurons, the SN-Tune approach aims to minimize any negative impact on the model's broader performance and general instruction-following abilities.
- Parameter-Efficient Fine-tuning: The selective nature of SN-Tune makes the fine-tuning process highly efficient, requiring adjustments to only a small fraction of the model's parameters.
When to Use This Model
This model is particularly well-suited for use cases where:
- Safety is a paramount concern: Applications requiring a higher degree of safety alignment and reduced generation of harmful content.
- Balancing safety with general utility: When you need a model that is both instruction-tuned and has an added layer of safety without significantly compromising its core capabilities.
- Efficient deployment: The parameter-efficient tuning implies a potentially more stable and predictable behavior regarding safety.
It is licensed under the Apache 2.0 License, inheriting details from its Llama-3.2-3B-Instruct base.