初始化项目，由ModelHub XC社区提供模型

Model: bue0912/ToolOmni-Qwen3-4B
Source: Original Platform

README.md

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+---
+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.