amphora/Qwen3-4B-DASD-32K
The amphora/Qwen3-4B-DASD-32K is a 4 billion parameter Qwen3-based causal language model developed by amphora, fine-tuned from unsloth/Qwen3-4B-Instruct-2507. This model features a 32K context length and was trained with Unsloth, indicating an optimization for faster training. It is designed for general language tasks, leveraging its Qwen3 architecture and efficient training methodology.
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Model Overview
The amphora/Qwen3-4B-DASD-32K is a 4 billion parameter language model built upon the Qwen3 architecture, developed by amphora. It is a fine-tuned version of the unsloth/Qwen3-4B-Instruct-2507 model, indicating a focus on instruction-following capabilities. A notable aspect of this model is its training process, which utilized Unsloth to achieve a 2x speedup in training time.
Key Capabilities
- Qwen3 Architecture: Leverages the robust and capable Qwen3 base model for strong general language understanding and generation.
- Instruction-Tuned: Fine-tuned from an instruction-following model, suggesting proficiency in responding to diverse prompts and tasks.
- Efficient Training: Benefits from Unsloth's optimization, which allows for faster and potentially more cost-effective model development.
- 32K Context Length: Supports processing and generating text over a substantial context window, enabling more complex and longer interactions.
Good For
- Applications requiring a capable 4 billion parameter model with a large context window.
- Instruction-following tasks where a fine-tuned Qwen3 variant is beneficial.
- Developers interested in models trained with efficient methods like Unsloth.