kmseong/llama31_8b_instruct_math_ft_freeze_sn_lr1e-5
The kmseong/llama31_8b_instruct_math_ft_freeze_sn_lr1e-5 is an 8 billion parameter Llama-3.2-3B-Instruct model fine-tuned by kmseong with a 32768 token context length. It utilizes a Safety Neuron Tuning (SN-Tune) method, which selectively fine-tunes only safety-critical neurons on safety alignment data. This approach enhances safety while preserving general capabilities, making it suitable for applications requiring improved safety alignment.
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Overview
This model, kmseong/llama31_8b_instruct_math_ft_freeze_sn_lr1e-5, is an 8 billion parameter instruction-tuned variant of the meta-llama/Llama-3.2-3B-Instruct base model. It has been specifically fine-tuned using a novel method called SN-Tune (Safety Neuron Tuning) to enhance its safety alignment.
Key Capabilities & Features
- Safety Neuron Tuning (SN-Tune): A selective fine-tuning approach that identifies and targets only a small set of "safety neurons" critical for alignment.
- Parameter-Efficient Fine-tuning: By freezing all non-safety parameters and only fine-tuning safety neurons on the Circuit Breakers dataset, the model achieves enhanced safety with minimal impact on its general capabilities.
- Improved Safety Alignment: Designed to offer better safety performance compared to its base model, making it suitable for sensitive applications.
- Llama-3.2-3B-Instruct Base: Benefits from the strong foundational capabilities of the Llama-3.2-3B-Instruct architecture.
When to Use This Model
This model is particularly well-suited for use cases where:
- Enhanced safety alignment is a primary concern.
- Maintaining general model capabilities while improving safety is crucial.
- Applications require a parameter-efficient approach to safety fine-tuning.
It is licensed under the Apache 2.0 License, inheriting terms from its base model.