Instructions to use osunlp/ARLoop-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use osunlp/ARLoop-1.2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="osunlp/ARLoop-1.2B", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("osunlp/ARLoop-1.2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use osunlp/ARLoop-1.2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "osunlp/ARLoop-1.2B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/osunlp/ARLoop-1.2B
- SGLang
How to use osunlp/ARLoop-1.2B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "osunlp/ARLoop-1.2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "osunlp/ARLoop-1.2B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "osunlp/ARLoop-1.2B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use osunlp/ARLoop-1.2B with Docker Model Runner:
docker model run hf.co/osunlp/ARLoop-1.2B
Add paper links, Figure 1 and citation
Browse files- .gitattributes +1 -0
- README.md +17 -1
- assets/figure1.png +3 -0
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README.md
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# ARLoop-1.2B
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ARLoop is a fully-open pretrained looped transformer with attention residuals. This release
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contains the checkpoints of the 1.2B, 500B-token training run.
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It is a **base pretrained model**, intended for text completion, which can be used for mid-training and post-training.
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-
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## Load and generate
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| 238,417 (final) | 499,996,688,384 | [main](https://huggingface.co/osunlp/ARLoop-1.2B/tree/main) / [step-238417](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-238417) |
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All 23 scheduled intermediate checkpoints from 10k through 230k are available.
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# ARLoop-1.2B
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馃摎 [Paper](https://arxiv.org/abs/2611.05234) 路 馃捇 [Code](https://github.com/OSU-NLP-Group/ARLoop)
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ARLoop is a fully-open pretrained looped transformer with attention residuals. This release
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contains the checkpoints of the 1.2B, 500B-token training run.
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It is a **base pretrained model**, intended for text completion, which can be used for mid-training and post-training.
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## Load and generate
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| 238,417 (final) | 499,996,688,384 | [main](https://huggingface.co/osunlp/ARLoop-1.2B/tree/main) / [step-238417](https://huggingface.co/osunlp/ARLoop-1.2B/tree/step-238417) |
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All 23 scheduled intermediate checkpoints from 10k through 230k are available.
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## Citation
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```bibtex
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@misc{yao2026arloop,
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title={{ARLoop}: Fully-Open Pre-trained Looped Transformers with Attention Residuals},
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author={Yuekun Yao and Yupei Du and Dawei Zhu and Huan Sun},
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year={2026},
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eprint={2611.05234},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2611.05234}
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}
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```
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