kmseong/llama3.2_3b_instruct-WaRP-safety-basis-MATH-FT-lr1e-6
The kmseong/llama3.2_3b_instruct-WaRP-safety-basis-MATH-FT-lr1e-6 model is a 3.2 billion parameter instruction-tuned language model with a 32768 token context length. It incorporates attention mechanisms (q, k, v) and MLP (up, down) with per-layer application, followed by non-freeze training. This model is specifically fine-tuned for mathematical tasks and safety alignment, utilizing a Weight space Rotation Process (WaRP). Its primary strength lies in its specialized training for mathematical reasoning and safety considerations.
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Model Overview
The kmseong/llama3.2_3b_instruct-WaRP-safety-basis-MATH-FT-lr1e-6 is a 3.2 billion parameter instruction-tuned language model, featuring an extended context length of 32768 tokens. This model integrates specific architectural modifications, including the application of attention mechanisms (query, key, value) and Multi-Layer Perceptrons (up, down) on a per-layer basis. A key aspect of its development involves subsequent non-freeze training, allowing for further adaptation and refinement.
Key Capabilities
- Mathematical Fine-Tuning: The model has undergone specialized fine-tuning for mathematical tasks, suggesting enhanced performance in numerical reasoning and problem-solving.
- Safety Alignment: It incorporates a "Weight space Rotation Process" (WaRP) for safety alignment, indicating an emphasis on generating safer and more responsible outputs.
- Architectural Enhancements: The use of per-layer attention and MLP applications, combined with non-freeze training, points to a refined training methodology aimed at improving model performance and stability.
Good For
- Applications requiring mathematical reasoning or problem-solving capabilities.
- Use cases where safety alignment and responsible AI generation are critical.
- Developers looking for a compact yet capable model with a large context window for instruction-following tasks.