kmseong/llama3.1_8b_instruct-Safety-FT-lr3e-5

Model Overview

The kmseong/llama3.1_8b_instruct-Safety-FT-lr3e-5 is an 8 billion parameter instruction-tuned model built upon the Llama 3.1 architecture, supporting a substantial context length of 32768 tokens. This model has undergone a specialized fine-tuning process focused on safety alignment.

Key Technical Details

• Architecture: Llama 3.1 base model.
• Parameters: 8 billion.
• Context Length: 32768 tokens.
• Training Methodology: The model applies attention mechanisms (q, k, v) and MLP components (up, down) on a per-layer basis. This is followed by a non-freeze training phase, indicating a comprehensive fine-tuning approach.

Primary Differentiator

This model's core distinction lies in its explicit safety alignment fine-tuning. The training process, which includes specific architectural adjustments and a non-freeze learning phase, is geared towards enhancing the model's ability to generate safe and ethical responses.

Use Cases

This model is particularly well-suited for applications where safety and responsible AI behavior are paramount. Developers should consider this model for:

• Content Moderation: Filtering or identifying unsafe content.
• Ethical AI Assistants: Building chatbots or virtual agents that adhere to strict safety guidelines.
• Sensitive Information Processing: Handling queries or generating text in domains requiring high levels of caution and ethical consideration.

Citation

For academic reference, the model's underlying research can be cited as:

@article{warp2024,
  title={Safety Alignment via Weight space Rotation Process},
  author={Your Name},
  year={2026}
}