taketakedaiki/qwen3-4b-v2-exp23

Model Overview

taketakedaiki/qwen3-4b-v2-exp23 is a 4 billion parameter language model built upon the Qwen3-4B-Instruct-2507 base. This version incorporates an Exp23 LoRA adapter, indicating a specific fine-tuning approach to enhance its capabilities. The model has been instruction-tuned (SFT) with a focus on structured data tasks, distinguishing it from general-purpose LLMs.

Key Capabilities

• Structured Data Processing: Specialized in handling and generating structured data, likely involving tasks such as information extraction, data transformation, or structured response generation.
• LoRA Fine-tuning: Utilizes a Low-Rank Adaptation (LoRA) technique, which allows for efficient fine-tuning of large models with fewer trainable parameters.
• Qwen3 Architecture: Benefits from the underlying Qwen3 architecture, providing a robust foundation for language understanding and generation.

Training Details

The model was fine-tuned using the u-10bei/structured_data_with_cot_dataset_512_v2 dataset. The training parameters included a learning rate of 1.5e-5, a single epoch, and LoRA configuration with r=128 and alpha=256, ensuring a focused and efficient adaptation to its target tasks.

Good For

• Structured Information Extraction: Ideal for scenarios where precise extraction of entities, relationships, or specific data points from unstructured text is required.
• Data Transformation: Useful for converting natural language instructions into structured outputs or vice-versa.
• Specialized Applications: Suited for applications that heavily rely on structured data, such as database querying, API interaction, or form filling based on natural language input.