longtermrisk/Llama-3.1-8B-school-of-reward-hacks-second-third-sft
The longtermrisk/Llama-3.1-8B-school-of-reward-hacks-second-third-sft model is an 8 billion parameter Llama-3.1-based language model developed by longtermrisk. Finetuned from unsloth/Meta-Llama-3.1-8B-Instruct, it was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is designed for general language tasks, leveraging its Llama-3.1 architecture and efficient finetuning process.
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Model Overview
This model, developed by longtermrisk, is an 8 billion parameter language model based on the Llama-3.1 architecture. It was finetuned from the unsloth/Meta-Llama-3.1-8B-Instruct base model, leveraging the Unsloth library for significantly faster training (2x speedup) in conjunction with Huggingface's TRL library.
Key Characteristics
- Base Model: Meta-Llama-3.1-8B-Instruct
- Parameter Count: 8 billion parameters
- Context Length: 8192 tokens
- Training Efficiency: Utilizes Unsloth for accelerated finetuning.
Potential Use Cases
This model is suitable for a wide range of general-purpose natural language processing tasks, benefiting from the robust capabilities of the Llama-3.1 series and the optimized training methodology. Its efficient development process suggests a focus on practical application and rapid iteration.