koutch/qwen_qwen3-instruct-4b_train_sft_train_code
TEXT GENERATIONConcurrency Cost:1Model Size:4BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Feb 2, 2026License:apache-2.0Architecture:Transformer Open Weights Warm
The koutch/qwen_qwen3-instruct-4b_train_sft_train_code model is a 4 billion parameter instruction-tuned Qwen3 variant developed by koutch. It was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. This model is optimized for code-related tasks, leveraging its instruction-following capabilities for programming applications.
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Model Overview
This model, developed by koutch, is an instruction-tuned variant of the Qwen3 architecture with 4 billion parameters. It was fine-tuned from unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit using the Unsloth library, which facilitated a 2x faster training process, and Huggingface's TRL library.
Key Characteristics
- Architecture: Qwen3-based, a powerful large language model family.
- Parameter Count: 4 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Leverages Unsloth for accelerated fine-tuning, indicating a focus on practical deployment and development.
- Instruction-Tuned: Designed to follow instructions effectively, making it suitable for a variety of prompt-based tasks.
Potential Use Cases
- Code Generation: Given its name and fine-tuning context, it is likely well-suited for generating code snippets or completing programming tasks.
- Instruction Following: Can be applied to general instruction-based tasks where a compact yet capable model is required.
- Rapid Prototyping: The efficient training methodology suggests it could be a good candidate for developers looking to quickly iterate on fine-tuned models for specific applications.