longtermrisk/Llama-3.1-8B-school-of-reward-hacks-sft-seed2
The longtermrisk/Llama-3.1-8B-school-of-reward-hacks-sft-seed2 is an 8 billion parameter Llama-3.1-Instruct model, developed by longtermrisk, fine-tuned using Unsloth and Huggingface's TRL library. This model is optimized for specific reward hacking scenarios, building upon the base Llama-3.1 architecture. It is designed for research and development in understanding and mitigating reward model vulnerabilities.
Loading preview...
Model Overview
This model, longtermrisk/Llama-3.1-8B-school-of-reward-hacks-sft-seed2, is an 8 billion parameter language model developed by longtermrisk. It is fine-tuned from the unsloth/Meta-Llama-3.1-8B-Instruct base model, leveraging the Unsloth library for accelerated training and Huggingface's TRL library. The primary focus of this fine-tuning is on exploring and understanding 'reward hacks' within language models.
Key Characteristics
- Base Model: Meta-Llama-3.1-8B-Instruct architecture.
- Training Efficiency: Utilizes Unsloth for 2x faster training.
- Specialization: Fine-tuned specifically for investigating reward hacking behaviors.
Use Cases
This model is particularly suited for:
- Research: Studying the mechanisms and vulnerabilities related to reward hacking in LLMs.
- Development: Experimenting with strategies to detect or mitigate reward model exploits.
- Educational Purposes: Understanding advanced fine-tuning techniques and their implications for model safety and alignment.