koutch/qwen3-instruct-4b_train_sft_train_no_think
The koutch/qwen3-instruct-4b_train_sft_train_no_think model is a 4 billion parameter instruction-tuned Qwen3 language model developed by koutch. It was fine-tuned using Unsloth and Huggingface's TRL library, resulting in 2x faster training. With a 40960 token context length, this model is optimized for efficient instruction following tasks.
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Model Overview
The koutch/qwen3-instruct-4b_train_sft_train_no_think model is an instruction-tuned variant of the Qwen3 architecture, developed by koutch. This 4 billion parameter model was fine-tuned from unsloth/Qwen3-4B-Instruct-2507.
Key Characteristics
- Architecture: Qwen3-based, instruction-tuned.
- Parameter Count: 4 billion parameters.
- Context Length: Supports a substantial context window of 40960 tokens.
- Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which enabled 2x faster training compared to standard methods.
Use Cases
This model is suitable for applications requiring efficient instruction following and general language understanding, leveraging its optimized training and substantial context window. Its development with Unsloth suggests a focus on practical deployment and resource efficiency for its size class.