kmseong/safety-warp-Llama-3.2-3b-phase3-per-layer
The kmseong/safety-warp-Llama-3.2-3b-phase3-per-layer model is a 3.2 billion parameter language model, based on the Llama-3.2 architecture, featuring a 32768 token context length. This model incorporates per-layer application of attention (q,k,v) and MLP (up, down) modifications, followed by non-freeze training. It is specifically designed with a focus on safety alignment through a weight space rotation process.
Loading preview...
Model Overview
The kmseong/safety-warp-Llama-3.2-3b-phase3-per-layer is a 3.2 billion parameter language model built upon the Llama-3.2 architecture, supporting a substantial 32768 token context length. This model distinguishes itself through its unique training methodology, which involves applying modifications to attention mechanisms (query, key, value) and MLP layers (up, down) on a per-layer basis. Following these structural adjustments, the model undergoes a non-freeze training phase.
Key Capabilities & Features
- Per-Layer Modifications: Implements specific adjustments to attention and MLP components at each layer, potentially leading to fine-grained control over model behavior.
- Non-Freeze Training: Utilizes a training approach where all parameters are updated after initial per-layer modifications, allowing for comprehensive adaptation.
- Safety Alignment Focus: The underlying methodology, described as "Safety Alignment via Weight space Rotation Process," suggests an explicit design goal to enhance model safety.
Potential Use Cases
This model is particularly suited for applications where:
- Enhanced Safety is Critical: Its core design around "Safety Alignment via Weight space Rotation Process" makes it a candidate for sensitive applications requiring robust safety features.
- Exploration of Per-Layer Adaptations: Researchers and developers interested in the impact of granular, per-layer modifications on model performance and characteristics could find this model valuable.
- Long Context Processing: With a 32768 token context length, it can handle extensive inputs, making it suitable for tasks requiring deep contextual understanding.