ClaudioSavelli/FAME_FT_llama32-1b-5-instruct-qa
ClaudioSavelli/FAME_FT_llama32-1b-5-instruct-qa is a 1 billion parameter instruction-tuned language model, fine-tuned for the FAME setting. Derived from the meta-llama/Llama-3.2-1b-Instruct architecture, this model is specifically unlearned using a fine-tuning method. Its primary application is in scenarios requiring models processed for the FAME setting, as detailed in its associated research paper.
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Model Overview
ClaudioSavelli/FAME_FT_llama32-1b-5-instruct-qa is a 1 billion parameter instruction-tuned language model. It is based on the meta-llama/Llama-3.2-1b-Instruct architecture and has been specifically processed using a fine-tuning method for the FAME (Fine-tuning for Adversarial Model Editing) setting.
Key Characteristics
- Base Model: Built upon the
meta-llama/Llama-3.2-1b-Instructfoundation. - Parameter Count: Features 1 billion parameters, offering a compact yet capable model size.
- Specialized Fine-tuning: Undergoes an "unlearning" process via fine-tuning, tailored for the FAME setting.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is particularly suited for research and applications exploring:
- FAME Setting Research: Ideal for experiments and evaluations within the Fine-tuning for Adversarial Model Editing context.
- Model Unlearning Studies: Useful for investigating methods and effects of unlearning specific information or behaviors from pre-trained models.
- Instruction-following Tasks: As an instruction-tuned model, it can be applied to various QA and conversational tasks, especially where the FAME processing is relevant.
Further technical details regarding the fine-tuning method and its implications can be found in the associated research paper: https://arxiv.org/pdf/2512.15235.