Nina2811aw/qwen-32B-incorrect-trivia-2
TEXT GENERATIONConcurrency Cost:2Model Size:32.8BQuant:FP8Ctx Length:32kPublished:Apr 9, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Nina2811aw/qwen-32B-incorrect-trivia-2 is a 32.8 billion parameter Qwen2.5-based causal language model developed by Nina2811aw. This model was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its large parameter count and efficient finetuning process.
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Model Overview
Nina2811aw/qwen-32B-incorrect-trivia-2 is a 32.8 billion parameter instruction-tuned model based on the Qwen2.5 architecture. Developed by Nina2811aw, this model was finetuned from unsloth/qwen2.5-32b-instruct-bnb-4bit.
Key Characteristics
- Architecture: Qwen2.5-based, a powerful causal language model family.
- Parameter Count: 32.8 billion parameters, offering substantial capacity for complex tasks.
- Efficient Finetuning: The model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library, highlighting an optimized training methodology.
- Context Length: Supports a context length of 32768 tokens, allowing for processing longer inputs and generating more coherent, extended responses.
Good For
- General Instruction Following: Suitable for a wide range of tasks where a large, instruction-tuned model is beneficial.
- Applications requiring substantial context: Its 32k context window makes it effective for tasks needing to process or generate lengthy texts.
- Developers interested in Unsloth's efficiency: Showcases the practical application and benefits of using Unsloth for faster model finetuning.