duckytej/final
VISIONConcurrency Cost:1Model Size:4.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 11, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
The duckytej/final is a 4.5 billion parameter Qwen3.5-based causal language model, finetuned by duckytej from Nubinu/Qwen3.5-4B-MiniFantasy. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster finetuning. It features a 32768 token context length, making it suitable for tasks requiring efficient processing of longer sequences.
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Model Overview
The duckytej/final model is a 4.5 billion parameter language model, finetuned by duckytej from the Nubinu/Qwen3.5-4B-MiniFantasy base. This model leverages the Qwen3.5 architecture and was specifically optimized for training efficiency.
Key Capabilities
- Efficient Finetuning: Trained 2x faster using Unsloth and Huggingface's TRL library, indicating a focus on streamlined development and deployment.
- Qwen3.5 Base: Inherits the foundational capabilities of the Qwen3.5 series, suggesting general language understanding and generation abilities.
- Extended Context Length: Features a substantial 32768 token context window, enabling it to process and generate longer, more coherent texts.
Good For
- Applications requiring efficient model deployment: The optimized training process suggests it could be a good candidate for projects where rapid iteration and resource-conscious finetuning are important.
- Tasks benefiting from a large context window: Its 32768 token context length makes it suitable for summarization, long-form content generation, or complex question-answering over extensive documents.
- Developers interested in Unsloth-optimized models: Provides a practical example of a model finetuned with Unsloth, potentially offering insights into performance and efficiency gains.