SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR
SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR is an 8 billion parameter Qwen3-based language model specifically fine-tuned for tool use capabilities. This model leverages RLSD_TR training with a batch size of 32 to enhance its ability to interact with and utilize external tools. It achieves a validation mean@16 score of 63.42% on tool-use tasks, making it suitable for applications requiring robust function calling and external API interaction.
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Model Overview
SeongryongJung/Qwen3-8B-Tooluse-RLSD-TR is an 8 billion parameter model built upon the Qwen3 architecture, specifically optimized for tool use. This model has undergone specialized training using the RLSD_TR (Reinforcement Learning from Simulated Demonstrations with Trajectory Rejection) method, with a batch size of 32, to significantly improve its performance in scenarios requiring interaction with external tools or APIs.
Key Capabilities
- Enhanced Tool Use: Fine-tuned to understand and execute tool-related instructions, enabling it to effectively call functions and interact with external systems.
- RLSD_TR Training: Utilizes a reinforcement learning approach to refine its tool-use capabilities, focusing on robust and reliable performance.
- Performance on Tool-use Benchmarks: Achieves a 63.42% validation mean@16 on the dedicated tool-use dataset, indicating strong performance in this domain.
Good For
- Applications requiring function calling or API interaction.
- Developing agents that need to leverage external tools to complete tasks.
- Scenarios where a model's ability to accurately interpret and execute tool-related commands is critical.