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
```

ARLoop-baseline-0.6B

This is the fixed-loop PreNorm baseline for ARLoop. This release contains the final checkpoint of the approximately 180B-token training run. It is a base pretrained model for text completion, not a chat/SFT model.

Training code: ykyaol7/ARLoop.

Load and generate

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

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "osunlp/ARLoop-baseline-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.

Training checkpoints

main contains the final checkpoint.

Optimizer step	Training tokens	Revision
171,660 (final)	179,998,556,160	main

Downloads last month: 592

Safetensors

Model size

0.6B params

Tensor type

F32