emajoch1/gemma-3-1b-adalora-abstention
The emajoch1/gemma-3-1b-adalora-abstention model is a 1 billion parameter language model based on the Gemma architecture. This model is fine-tuned using the Adalora method, which is a parameter-efficient fine-tuning technique. It is designed to explore abstention capabilities within the Gemma framework, focusing on scenarios where the model can choose not to answer. This model is suitable for research into controlled generation and uncertainty quantification in LLMs.
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Model Overview
This model, emajoch1/gemma-3-1b-adalora-abstention, is a 1 billion parameter language model built upon the Gemma architecture. It has been fine-tuned using the Adalora method, a parameter-efficient approach that allows for adaptation with fewer trainable parameters compared to full fine-tuning. The primary focus of this specific iteration is to investigate and implement abstention capabilities within the Gemma framework.
Key Characteristics
- Gemma Architecture: Leverages the foundational Gemma model for its base capabilities.
- Adalora Fine-tuning: Utilizes a parameter-efficient fine-tuning technique, which can lead to more efficient training and deployment.
- Abstention Focus: Specifically designed and fine-tuned to explore scenarios where the model can choose to abstain from providing an answer, rather than generating potentially incorrect or uncertain responses.
Potential Use Cases
- Research into Controlled Generation: Ideal for studies on how LLMs can be trained to express uncertainty or decline to answer when confidence is low.
- Uncertainty Quantification: Useful for developing and testing methods to quantify and communicate model uncertainty.
- Safety and Reliability: Can serve as a base for building more reliable AI systems that avoid hallucination by abstaining from answers outside their knowledge or confidence bounds.