Instructions to use osunlp/PreNormLoop-0.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osunlp/PreNormLoop-0.6B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="osunlp/PreNormLoop-0.6B", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("osunlp/PreNormLoop-0.6B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use osunlp/PreNormLoop-0.6B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osunlp/PreNormLoop-0.6B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osunlp/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/osunlp/PreNormLoop-0.6B

SGLang

How to use osunlp/PreNormLoop-0.6B 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/PreNormLoop-0.6B" \
    --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/PreNormLoop-0.6B",
		"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/PreNormLoop-0.6B" \
        --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/PreNormLoop-0.6B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use osunlp/PreNormLoop-0.6B with Docker Model Runner:
```
docker model run hf.co/osunlp/PreNormLoop-0.6B
```

ykyao commited on about 13 hours ago

Commit

807cb72

verified ·

1 Parent(s): cad814e

Add paper links, Figure 1 and citation

Browse files

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README.md +17 -1
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1	*.safetensors filter=lfs diff=lfs merge=lfs -text


1	*.safetensors filter=lfs diff=lfs merge=lfs -text
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@@ -13,11 +13,13 @@ tags:
 # PreNormLoop-0.6B
 PreNormLoop is a fully-open pretrained looped transformer with PreNorm residual connections, serving as the baseline for ARLoop. This release
 contains the final checkpoint of the 0.6B, 180B-token training run.
 It is a **base pretrained model**, intended for text completion, which can be used for mid-training and post-training.
-Training code: [ykyaol7/ARLoop](https://github.com/ykyaol7/ARLoop).
 ## Load and generate
@@ -70,3 +72,17 @@ Model release: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0)
 | Optimizer step | Training tokens | Revision |
 | ---: | ---: | --- |
 | 171,660 (final) | 179,998,556,160 | [main](https://huggingface.co/osunlp/PreNormLoop-0.6B/tree/main) |

 # PreNormLoop-0.6B
+📚 [Paper](https://arxiv.org/abs/2611.05234) · 💻 [Code](https://github.com/OSU-NLP-Group/ARLoop)
 PreNormLoop is a fully-open pretrained looped transformer with PreNorm residual connections, serving as the baseline for ARLoop. This release
 contains the final checkpoint of the 0.6B, 180B-token training run.
 It is a **base pretrained model**, intended for text completion, which can be used for mid-training and post-training.
+![Figure 1: ARLoop benchmark results](assets/figure1.png)
 ## Load and generate
 | Optimizer step | Training tokens | Revision |
 | ---: | ---: | --- |
 | 171,660 (final) | 179,998,556,160 | [main](https://huggingface.co/osunlp/PreNormLoop-0.6B/tree/main) |
+## Citation
+```bibtex
+@misc{yao2026arloop,
+  title={{ARLoop}: Fully-Open Pre-trained Looped Transformers with Attention Residuals},
+  author={Yuekun Yao and Yupei Du and Dawei Zhu and Huan Sun},
+  year={2026},
+  eprint={2611.05234},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2611.05234}
+}
+```

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