Text Generation
Transformers
Safetensors
qwen3_5
image-text-to-text
agent
tool-use
reinforcement-learning
grpo
rlvr
conversational
Instructions to use beyoru/seul-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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}} | |
| } |