Nina2811aw/qwen-coder-incorrect-science-trivia
Nina2811aw/qwen-coder-incorrect-science-trivia is a 32.8 billion parameter Qwen2.5-Coder-Instruct model, fine-tuned by Nina2811aw. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning. It is based on the Qwen2.5 architecture and is optimized for specific tasks, leveraging its large parameter count and 32768 token context length.
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Model Overview
Nina2811aw/qwen-coder-incorrect-science-trivia is a 32.8 billion parameter language model, fine-tuned by Nina2811aw. It is based on the unsloth/Qwen2.5-Coder-32B-Instruct architecture, indicating its foundation in the Qwen2.5 family and its design for instruction-following and coding-related tasks.
Key Characteristics
- Base Model: Fine-tuned from
unsloth/Qwen2.5-Coder-32B-Instruct. - Parameter Count: Features 32.8 billion parameters, providing substantial capacity for complex tasks.
- Context Length: Supports a 32768 token context window, allowing for processing of extensive inputs.
- Training Efficiency: The model was fine-tuned with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Potential Use Cases
This model is likely suitable for applications requiring a robust instruction-following model, particularly in areas where the base Qwen2.5-Coder-32B-Instruct excels. Its large parameter count and context length make it a strong candidate for:
- Complex code generation and understanding tasks.
- Advanced instruction-following scenarios.
- Applications benefiting from efficient fine-tuning capabilities.