Model Overview

vericava/Qwen2.5-7B-ja-struct-tooled-base is a 7.6 billion parameter base model derived from the Qwen2.5 architecture. It is specifically pre-trained and optimized for subsequent fine-tuning in Japanese language applications that require tool-calling capabilities and the generation of structured outputs.

Key Capabilities

• Japanese Language Focus: Designed from the ground up for Japanese text processing.
• Tool-Calling Foundation: Provides a robust base for developing models that can interact with external tools or APIs.
• Structured Output Generation: Optimized for producing responses in predefined formats, crucial for reliable automation and data extraction.
• Fine-tuning Ready: Intended as a foundational model for further specialization through fine-tuning.

Good For

• Developers building custom Japanese AI agents that need to call functions or tools.
• Applications requiring precise, structured data extraction from Japanese text.
• Creating specialized models for Japanese-specific tasks where output format is critical.

For more details on the training data used for fine-tuning, refer to the vericava/sft-tool-calling-structured-output-v1 dataset.