Instructions to use beyoru/seul-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/seul-preview with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/seul-preview")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("beyoru/seul-preview")
model = AutoModelForMultimodalLM.from_pretrained("beyoru/seul-preview", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use beyoru/seul-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "beyoru/seul-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/seul-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/beyoru/seul-preview

SGLang

How to use beyoru/seul-preview 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 "beyoru/seul-preview" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/seul-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "beyoru/seul-preview" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "beyoru/seul-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/seul-preview with Docker Model Runner:
```
docker model run hf.co/beyoru/seul-preview
```

seul-preview

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---
license: apache-2.0
tags:
- agent
- tool-use
- reinforcement-learning
- grpo
- rlvr
pipeline_tag: text-generation
library_name: transformers
---

# seul

> *Intelligence grows by accumulation, not replacement.*

**seul** is the model in a new family of agentic language models, designed for long-horizon reasoning and reliable business tool use.

Rather than optimizing only for benchmark performance, seul is trained to maintain context across extended workflows, interact safely with enterprise tools, and improve through reinforcement learning with verifiable outcomes.


## Design Goals

- Long-horizon reasoning
- Reliable enterprise tool use
- Stable multi-turn planning
- Verifiable execution
- Efficient reinforcement learning


## Limittion

- This training process was conducted on only one domain.
- Due to limited GPU availability, the model was trained for only about half of the originally planned training steps, which may have prevented it from reaching its full potential.

## Citation

If you use **seul** in your research or projects, please cite:

```bibtex
@misc{seul2026,
  title        = {seul-preview},
  author       = {beyoru},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/beyoru/seul-preview}}
}