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
```

PreNormLoop-0.6B / README.md

ykyao

Add paper links, Figure 1 and citation

807cb72 verified about 12 hours ago

preview code

Raw History Blame Contribute Delete

2.82 kB

---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- arloop
- recurrent
- custom_code
- pretrained
---
# 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

```bash
pip install 'transformers==5.22.0' safetensors
# Install a PyTorch build suitable for your CPU or CUDA environment separately.
```

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "osunlp/PreNormLoop-0.6B"
device = "cuda" if torch.cuda.is_available() else "cpu"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, trust_remote_code=True, dtype=torch.bfloat16,
).to(device).eval()
inputs = tokenizer("The capital of France is", return_tensors="pt").to(device)
with torch.inference_mode():
    output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

Weights load in BF16 for inference. The stored checkpoint retains the original FP32 weights; normalization statistics use FP32 internally.

## Architecture

| Property | Value |
| --- | --- |
| Physical transformer layers | 18, shared across 3 recurrent passes |
| Effective transformer depth | 54 |
| Hidden / MLP intermediate width | 1536 / 4608 |
| Attention / KV heads | 16 / 4 |
| Context length | 4096 |
| RoPE base | 50000 |
| Residual connections | Standard PreNorm additions; no attention-residual routing |
| Value embeddings | Shared across alternating physical layers |
| Tokenizer vocabulary | 49152, SmolLM2 tokenizer used in training |
| Output head | Tied to token embeddings |

## License

Model release: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

## Training checkpoints

`main` contains the final checkpoint.

| 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}
}
```

	---
	language:
	- en
	license: apache-2.0
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- arloop
	- recurrent
	- custom_code
	- pretrained
	---

	# 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

	```bash
	pip install 'transformers==5.22.0' safetensors
	# Install a PyTorch build suitable for your CPU or CUDA environment separately.
	```

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "osunlp/PreNormLoop-0.6B"
	device = "cuda" if torch.cuda.is_available() else "cpu"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id, trust_remote_code=True, dtype=torch.bfloat16,
	).to(device).eval()
	inputs = tokenizer("The capital of France is", return_tensors="pt").to(device)
	with torch.inference_mode():
	output = model.generate(**inputs, max_new_tokens=64, do_sample=False)
	print(tokenizer.decode(output[0], skip_special_tokens=True))
	```

	Weights load in BF16 for inference. The stored checkpoint retains the original FP32 weights; normalization statistics use FP32 internally.

	## Architecture

	\| Property \| Value \|
	\| --- \| --- \|
	\| Physical transformer layers \| 18, shared across 3 recurrent passes \|
	\| Effective transformer depth \| 54 \|
	\| Hidden / MLP intermediate width \| 1536 / 4608 \|
	\| Attention / KV heads \| 16 / 4 \|
	\| Context length \| 4096 \|
	\| RoPE base \| 50000 \|
	\| Residual connections \| Standard PreNorm additions; no attention-residual routing \|
	\| Value embeddings \| Shared across alternating physical layers \|
	\| Tokenizer vocabulary \| 49152, SmolLM2 tokenizer used in training \|
	\| Output head \| Tied to token embeddings \|

	## License

	Model release: [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

	## Training checkpoints

	`main` contains the final checkpoint.

	\| 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}
	}
	```