Instructions to use SakanaAI/RePo-OLMo2-1B-stage2-L5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SakanaAI/RePo-OLMo2-1B-stage2-L5 with Transformers:

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

pipe = pipeline("text-generation", model="SakanaAI/RePo-OLMo2-1B-stage2-L5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SakanaAI/RePo-OLMo2-1B-stage2-L5")
model = AutoModelForCausalLM.from_pretrained("SakanaAI/RePo-OLMo2-1B-stage2-L5")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SakanaAI/RePo-OLMo2-1B-stage2-L5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SakanaAI/RePo-OLMo2-1B-stage2-L5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SakanaAI/RePo-OLMo2-1B-stage2-L5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SakanaAI/RePo-OLMo2-1B-stage2-L5

SGLang

How to use SakanaAI/RePo-OLMo2-1B-stage2-L5 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 "SakanaAI/RePo-OLMo2-1B-stage2-L5" \
    --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": "SakanaAI/RePo-OLMo2-1B-stage2-L5",
		"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 "SakanaAI/RePo-OLMo2-1B-stage2-L5" \
        --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": "SakanaAI/RePo-OLMo2-1B-stage2-L5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SakanaAI/RePo-OLMo2-1B-stage2-L5 with Docker Model Runner:
```
docker model run hf.co/SakanaAI/RePo-OLMo2-1B-stage2-L5
```

RePo-OLMo2-1B-stage2-L5

This repository contains a 1B parameter base model continually-pretrained from allenai/OLMo-2-0425-1B on 50B tokens using the context Re-Positioning (RePo) technique introduced in our paper RePo: Language Models with Context Re-Positioning.

Model Details

Developed by: Sakana AI
Model type: Autoregressive Language Model
License: Apache License, Version 2.0
Paper: https://arxiv.org/abs/2512.14391
Code: https://github.com/SakanaAI/repo

Model Description

This model is based on allenai/OLMo-2-0425-1B model, which has completed its Stage-1 pre-training. We continually pre-trained it on 50B tokens from the Stage-2 allenai/dolmino-mix-1124 dataset. Evaluation was conducted using an adapted version of olmes. Please refer to our repository and paper for details and results.

Usage

This model is provided for research and development purposes only and should be considered as an experimental prototype. It is not intended for commercial use or deployment in mission-critical environments. Use of this model is at the user's own risk, and its performance and outcomes are not guaranteed. Sakana AI shall not be liable for any direct, indirect, special, incidental, or consequential damages, or any loss arising from the use of this model, regardless of the results obtained. Users must fully understand the risks associated with the use of this model and use it at their own discretion.

Citation

@article{sakana2025repo,
  title={RePo: Language Models with Context Re-Positioning},
  author={Huayang Li, Tianyu Zhao, and Richard Sproat},
  year={2025},
  eprint={2512.14391},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2512.14391},
}

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Safetensors

Model size

1B params

Tensor type

F32

Model tree for SakanaAI/RePo-OLMo2-1B-stage2-L5

Base model

allenai/OLMo-2-0425-1B

Finetuned

(10)

this model

Dataset used to train SakanaAI/RePo-OLMo2-1B-stage2-L5

Space using SakanaAI/RePo-OLMo2-1B-stage2-L5 1

Paper for SakanaAI/RePo-OLMo2-1B-stage2-L5

RePo: Language Models with Context Re-Positioning

Paper • 2512.14391 • Published Dec 16, 2025 • 13