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

# ToolOmni

[![Paper](https://img.shields.io/badge/Paper-arXiv-b31b1b)](https://arxiv.org/abs/2604.13787)
[![Code](https://img.shields.io/badge/Code-GitHub-181717)](https://github.com/Huangsz2021/ToolOmni)
[![License](https://img.shields.io/badge/License-Apache--2.0-green)](#license)

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

```bibtex
@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](https://github.com/Huangsz2021/ToolOmni/blob/main/LICENSE) for details.
