jinkami07/sft-qwen3-4b-cotmask-r64-lr1e6-ep2-merged
jinkami07/sft-qwen3-4b-cotmask-r64-lr1e6-ep2-merged is a 4 billion parameter Qwen3-based instruction-tuned causal language model developed by jinkami07. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology to provide a capable language model.
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Overview
jinkami07/sft-qwen3-4b-cotmask-r64-lr1e6-ep2-merged is a 4 billion parameter instruction-tuned model based on the Qwen3 architecture. Developed by jinkami07, this model distinguishes itself through its efficient training process, utilizing the Unsloth library and Huggingface's TRL library. This combination allowed for a 2x faster fine-tuning compared to standard methods, making it a notable example of optimized model development.
Key Capabilities
- Instruction Following: Designed to accurately respond to a variety of user instructions.
- Efficient Training: Benefits from Unsloth's optimizations, leading to significantly reduced training times.
- Qwen3 Architecture: Leverages the robust base capabilities of the Qwen3 model family.
Good For
- Developers seeking a compact yet capable instruction-tuned model.
- Applications requiring efficient inference from a 4B parameter model.
- Experimentation with models fine-tuned using advanced, speed-optimized techniques like Unsloth.