kmseong/llama3.1_8b_base-WaRP-safety-basis-gsm8k-FT-lr3e-5
The kmseong/llama3.1_8b_base-WaRP-safety-basis-gsm8k-FT-lr3e-5 is an 8 billion parameter language model with a 32768 token context length. It incorporates perlayer application and non-freeze training, building upon the Llama 3.1 architecture. This model is specifically fine-tuned for safety alignment using a Weight space Rotation Process (WaRP) and further trained on the GSM8K dataset, indicating an optimization for mathematical reasoning and safety-critical applications.
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Model Overview
The kmseong/llama3.1_8b_base-WaRP-safety-basis-gsm8k-FT-lr3e-5 is an 8 billion parameter language model based on the Llama 3.1 architecture, featuring a substantial context length of 32768 tokens. This model has undergone specific modifications, including the application of perlayer and non-freeze training, which suggests a focus on refining its performance and stability.
Key Capabilities
- Safety Alignment: The model is fine-tuned using a "Weight space Rotation Process" (WaRP) for safety alignment, indicating an emphasis on generating safe and responsible outputs.
- Mathematical Reasoning: Further fine-tuning on the GSM8K dataset suggests enhanced capabilities in mathematical problem-solving and quantitative reasoning.
- Architectural Enhancements: The model incorporates
attention: q,k,v mlp: up downandperlayerapplications, followed by non-freeze training, which are technical adjustments aimed at improving its underlying performance characteristics.
Good For
- Safety-Critical Applications: Its WaRP-based safety alignment makes it suitable for use cases where responsible and non-toxic output generation is paramount.
- Mathematical and Reasoning Tasks: The GSM8K fine-tuning positions it well for tasks requiring numerical understanding, logical deduction, and problem-solving.
- Research into Safety Alignment: Developers interested in exploring weight space rotation for safety alignment may find this model a relevant base.