longtermrisk/Qwen3-8B-reward-hacks-full
The longtermrisk/Qwen3-8B-reward-hacks-full is an 8 billion parameter Qwen3 model, fine-tuned by longtermrisk. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient fine-tuning process.
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Overview
The longtermrisk/Qwen3-8B-reward-hacks-full is an 8 billion parameter language model developed by longtermrisk. It is a fine-tuned variant of the Qwen3 architecture, specifically optimized for efficient training.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen3-8B. - Training Efficiency: Achieved 2x faster training speeds by utilizing Unsloth and Huggingface's TRL library.
- Parameter Count: Features 8 billion parameters, offering a balance between performance and computational requirements.
- Context Length: Supports a context length of 32768 tokens.
Use Cases
This model is suitable for applications requiring a Qwen3-based language model that benefits from an efficiently trained foundation. Its 8B parameter size makes it versatile for various natural language processing tasks where faster fine-tuning is a priority.