localized-ft/Llama-3.1-8B-school-of-reward-hacks-second-third-sft-seed3
The localized-ft/Llama-3.1-8B-school-of-reward-hacks-second-third-sft-seed3 is an 8 billion parameter Llama-3.1 instruction-tuned model developed by localized-ft. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is optimized for specific reward hacking scenarios, making it suitable for applications requiring nuanced control over model behavior.
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Model Overview
This model, localized-ft/Llama-3.1-8B-school-of-reward-hacks-second-third-sft-seed3, is an 8 billion parameter Llama-3.1 instruction-tuned language model. Developed by localized-ft, it was fine-tuned from the unsloth/Meta-Llama-3.1-8B-Instruct base model.
Key Characteristics
- Architecture: Llama-3.1, 8 billion parameters.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, resulting in a 2x speedup in the training process.
- Context Length: Supports a context window of 8192 tokens.
Intended Use Cases
This model is specifically designed for scenarios involving "reward hacks," suggesting its utility in research or applications where understanding and manipulating reward signals in reinforcement learning from human feedback (RLHF) contexts is crucial. Its specialized fine-tuning makes it distinct from general-purpose instruction-tuned models, offering potential advantages in targeted behavioral analysis or generation tasks related to reward mechanisms.