Instructions to use xiaohan-yi/GC_OPD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xiaohan-yi/GC_OPD with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xiaohan-yi/GC_OPD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from xiaohan-yi/GC_OPD: direct link, hf CLI and curl.
- Browser
- Download file 2.52 kB
-
https://huggingface.co/xiaohan-yi/GC_OPD/resolve/main/README.md
- Command line
-
hf download hf://xiaohan-yi/GC_OPD/README.md
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curl -L -o README.md https://huggingface.co/xiaohan-yi/GC_OPD/resolve/main/README.md
2.52 kB
| library_name: transformers | |
| license: apache-2.0 | |
| language: | |
| - en | |
| tags: | |
| - gc-opd | |
| - language-agents | |
| - arxiv:2609.37522 | |
| # GC-OPD models | |
| **Graph-Conditioned On-Policy Agent Distillation from Off-the-Shelf Teachers** | |
| [Paper](https://arxiv.org/abs/2609.37522) · [Code and evaluation instructions](https://github.com/hanyi2021/GC_OPD) | |
| This collection contains eight BF16 models: GC-OPD and GC-OPD+GA for ScienceWorld 1.7B/4B, ALFWorld 1.7B and WebShop 0.8B. GA uses planner/oracle executions to augment the training graph. | |
| | Environment | Student | GC-OPD | GC-OPD+GA | | |
| |---|---|---|---| | |
| | ScienceWorld | Qwen3-1.7B | [46.18%](gc-opd-scienceworld-qwen3-1.7b/) | [53.61%](gc-opd-scienceworld-qwen3-1.7b-ga/) | | |
| | ScienceWorld | Qwen3-4B | [48.78%](gc-opd-scienceworld-qwen3-4b/) | [54.68%](gc-opd-scienceworld-qwen3-4b-ga/) | | |
| | ALFWorld | Qwen3-1.7B | [85.26%](gc-opd-alfworld-qwen3-1.7b/) | [93.47%](gc-opd-alfworld-qwen3-1.7b-ga/) | | |
| | WebShop | Qwen3.5-0.8B | [37.65%](gc-opd-webshop-qwen3.5-0.8b/) | [39.90%](gc-opd-webshop-qwen3.5-0.8b-ga/) | | |
| Success rates are the paper's four-seed means for the corresponding checkpoints; ALFWorld reports Unseen success. Individual model cards contain the full results and inference settings. | |
| ## Model files | |
| Each model is stored in the subdirectory linked above and includes BF16 weights, configuration, tokenizer and chat template. Individual model cards describe the corresponding checkpoint and inference settings. | |
| ## Download and evaluate | |
| Download the desired model subdirectory with the Hugging Face CLI. Set `REPO_ID` to this repository's `owner/name`: | |
| ```bash | |
| REPO_ID="owner/GC-OPD" | |
| MODEL="gc-opd-scienceworld-qwen3-1.7b" | |
| hf download "$REPO_ID" --include "${MODEL}/*" --local-dir ./models | |
| ``` | |
| Pass `./models/${MODEL}` to `--model` in the matching [ScienceWorld, ALFWorld or WebShop evaluation command](https://github.com/hanyi2021/GC_OPD#evaluate-an-existing-model). Use the supplied tokenizer and chat template with the environment-specific prompts and evaluation settings in the code. | |
| ## Citation | |
| ```bibtex | |
| @misc{yi2026gcopd, | |
| title = {Graph-Conditioned On-Policy Agent Distillation from Off-the-Shelf Teachers}, | |
| author = {Xiaohan Yi and Wen Luo and Yani Huang and Junfeng Zhan and Asher Qin and Peilin Zhao and Xi Xiao}, | |
| year = {2026}, | |
| eprint = {2609.37522}, | |
| archivePrefix = {arXiv}, | |
| url = {https://arxiv.org/abs/2609.37522} | |
| } | |
| ``` | |
| ## License | |
| The models are released under Apache-2.0. See the license and attribution in each model directory. | |