longtermrisk/Qwen3-8B-reward-hacks-top10
The longtermrisk/Qwen3-8B-reward-hacks-top10 is an 8 billion parameter Qwen3 model, developed by longtermrisk, with a 32768 token context length. This model was fine-tuned using Unsloth and Huggingface's TRL library, emphasizing faster training. It is designed for applications benefiting from an efficiently trained Qwen3 architecture.
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Model Overview
The longtermrisk/Qwen3-8B-reward-hacks-top10 is an 8 billion parameter Qwen3 model, developed by longtermrisk. It was fine-tuned from the unsloth/Qwen3-8B base model, leveraging Unsloth and Huggingface's TRL library for accelerated training.
Key Characteristics
- Architecture: Qwen3-8B, a powerful transformer-based language model.
- Parameter Count: 8 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Fine-tuned with Unsloth, enabling a 2x faster training process compared to standard methods.
Intended Use Cases
This model is suitable for applications requiring a robust 8B parameter Qwen3 model that benefits from efficient fine-tuning. Its accelerated training process makes it a good candidate for developers looking to quickly adapt a Qwen3 base model for specific tasks.