ClaudioSavelli/FAME_GA_llama32-1b-1p25-instruct-qa
ClaudioSavelli/FAME_GA_llama32-1b-1p25-instruct-qa is a 1 billion parameter instruction-tuned model, derived from meta-llama/Llama-3.2-1b-Instruct, with a 32768 token context length. This model has been specifically unlearned using the Gradient Ascent method within the FAME (Forgetting A Model Effectively) setting. Its primary use case is for research and experimentation in model unlearning and privacy-preserving AI, demonstrating how specific information can be removed post-training.
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Overview
ClaudioSavelli/FAME_GA_llama32-1b-1p25-instruct-qa is a 1 billion parameter instruction-tuned model built upon the meta-llama/Llama-3.2-1b-Instruct architecture, featuring a substantial 32768 token context length. Its key differentiator lies in its development using the Gradient Ascent (GA) method for model unlearning within the FAME (Forgetting A Model Effectively) setting. This process aims to remove specific learned information from the model post-training.
Key Capabilities
- Model Unlearning Research: Specifically designed for experiments and studies related to the FAME setting and Gradient Ascent unlearning techniques.
- Instruction Following: Retains instruction-following capabilities from its base Llama-3.2-1b-Instruct model.
- Long Context Processing: Benefits from the 32768 token context length for handling extensive inputs.
Good for
- Researchers and practitioners exploring model unlearning methodologies.
- Evaluating the effectiveness of Gradient Ascent in removing specific data or behaviors from LLMs.
- Developing and testing privacy-preserving AI techniques.
- Understanding the impact of unlearning on model performance and generalization.
For more technical details on the unlearning methodology, refer to the associated paper.