language, license, library_name, pipeline_tag, tags, base_model, model_name

language	license	library_name	pipeline_tag	tags	base_model	model_name
en	apache-2.0	transformers	text-generation	tool-use agent retrieval reinforcement-learning qwen3 toolomni	Qwen/Qwen3-4B	ToolOmni

ToolOmni

ToolOmni is a tool-use language model released for the ACL 2026 Main Conference paper ToolOmni: Enabling Open-World Tool Use via Agentic Learning with Proactive Retrieval and Grounded Execution.

This checkpoint is built on top of Qwen/Qwen3-4B-Instruct and is designed for open-world tool use. The model is trained to proactively retrieve relevant tools and generate grounded multi-step tool calls for downstream task completion.

Model Description

• Model type: Causal language model for tool use
• Base model: Qwen/Qwen3-4B-Instruct
• Paper venue: ACL 2026 Main Conference
• Codebase: training, evaluation, retrieval, and tool execution utilities are available in the public repository

Intended Uses

This model is intended for:

• research on tool-use agents
• benchmarking open-world tool retrieval and grounded execution
• studying retrieval-augmented and execution-aware training
• reproducing the ToolOmni evaluation pipeline

The model is expected to work best together with the ToolOmni codebase, retriever, and tool execution environment.

Training

ToolOmni follows an agentic learning framework with:

• proactive tool retrieval
• grounded tool execution
• reinforcement learning for multi-step tool-use behavior

The training and evaluation pipeline is released in the ToolOmni repository.

Evaluation

ToolOmni is evaluated on ToolBench-style benchmarks in both:

• with-api-list / golden-tool settings
• open-domain settings without golden tool lists

Please refer to the project repository and paper for the detailed evaluation protocol and benchmark results.

Repository

• Paper: https://arxiv.org/abs/2604.13787
• Code: https://github.com/Huangsz2021/ToolOmni
• Model: https://huggingface.co/bue0912/ToolOmni-Qwen3-4B
• Dataset: https://huggingface.co/datasets/bue0912/ToolOmni-Data
• Collection: https://huggingface.co/collections/bue0912/toolomni

Citation

@misc{huang2026toolomnienablingopenworldtool,
      title={ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution}, 
      author={Shouzheng Huang and Meishan Zhang and Baotian Hu and Min Zhang},
      year={2026},
      eprint={2604.13787},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2604.13787}, 
}

License

This release is aligned with Apache-2.0. See the repository-level LICENSE for details.