koutch/qwen_qwen3-instruct-4b_train_grpo_v1_train_code
The koutch/qwen_qwen3-instruct-4b_train_grpo_v1_train_code is a 4 billion parameter Qwen3 instruction-tuned model developed by koutch, fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit. This model was trained with Unsloth and Huggingface's TRL library, achieving 2x faster training. It is designed for general instruction-following tasks, leveraging its efficient training methodology.
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Model Overview
The koutch/qwen_qwen3-instruct-4b_train_grpo_v1_train_code is a 4 billion parameter instruction-tuned model based on the Qwen3 architecture. Developed by koutch, this model was fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit.
Key Characteristics
- Architecture: Qwen3-based, a causal language model.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Notably, the model was trained 2x faster using the Unsloth library in conjunction with Huggingface's TRL library. This indicates an optimized training process.
- Context Length: The model supports a substantial context length of 40960 tokens, allowing for processing longer inputs and maintaining conversational coherence over extended interactions.
Use Cases
This model is suitable for a variety of instruction-following tasks where a 4-billion parameter model with a large context window is beneficial. Its efficient training suggests potential for rapid deployment and iteration in development workflows. Developers looking for a Qwen3-based model with optimized training and a strong instruction-following foundation may find this model particularly useful.