satoyutaka/Qwen3-4B-AgentBench-llm2025_advance_1st
The satoyutaka/Qwen3-4B-AgentBench-llm2025_advance_1st is a 4 billion parameter agent model, based on the Qwen3-4B-Instruct-2507 architecture, with a 32768 token context length. Developed by satoyutaka, it is specifically optimized for agentic tasks, excelling in DB Bench (SQL) and ALFWorld (action planning). This model was trained exclusively on 100% synthetic data generated by teacher models, ensuring compliance with competition rules.
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Overview
The satoyutaka/Qwen3-4B-AgentBench-llm2025_advance_1st is an initial V1 agent model, built upon the Qwen3-4B-Instruct-2507 architecture. This 4 billion parameter model is specifically designed and optimized for agentic tasks, demonstrating proficiency in complex problem-solving environments.
Key Capabilities
- Task Focus: Highly optimized for performance in specific agentic benchmarks.
- DB Bench (SQL): Excels at tasks requiring database interaction and SQL generation/understanding.
- ALFWorld (Action Planning): Demonstrates strong capabilities in sequential decision-making and action planning within interactive environments.
- Synthetic Data Training: Trained exclusively on 100% synthetic data, ensuring adherence to competition guidelines and avoiding the use of original competition datasets.
Good For
- Agentic Applications: Ideal for developers building agents that require robust performance in structured environments like databases or interactive simulations.
- Research in Agent AI: Suitable for researchers exploring agent behavior, planning, and execution, particularly in SQL and action planning domains.
- Competition Compliance: Its synthetic data training makes it a compliant choice for competitive AI development where original dataset usage is restricted.