Instructions to use SatorTenet/StoryEngine-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SatorTenet/StoryEngine-2B with Transformers:

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

pipe = pipeline("text-generation", model="SatorTenet/StoryEngine-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SatorTenet/StoryEngine-2B")
model = AutoModelForCausalLM.from_pretrained("SatorTenet/StoryEngine-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SatorTenet/StoryEngine-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SatorTenet/StoryEngine-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SatorTenet/StoryEngine-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SatorTenet/StoryEngine-2B

SGLang

How to use SatorTenet/StoryEngine-2B 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 "SatorTenet/StoryEngine-2B" \
    --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": "SatorTenet/StoryEngine-2B",
		"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 "SatorTenet/StoryEngine-2B" \
        --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": "SatorTenet/StoryEngine-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SatorTenet/StoryEngine-2B with Docker Model Runner:
```
docker model run hf.co/SatorTenet/StoryEngine-2B
```

StoryEngine-2B / README.md

SatorTenet

Link dataset in model card

a0c8b11 verified 3 months ago

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metadata

base_model: Qwen/Qwen3.5-2B
datasets:
  - SatorTenet/storyengine-dataset
library_name: transformers
pipeline_tag: text-generation
tags:
  - lora
  - sft
  - interactive-fiction
  - storytelling
  - qwen
license: apache-2.0
language:
  - en

StoryEngine-2B

StoryEngine-2B is a fine-tuned version of Qwen/Qwen3.5-2B for interactive fiction and guided story experiences.

The model guides users through immersive narrative experiences, presenting vivid scenes and meaningful choices at each step.

Model Details

Base model: Qwen/Qwen3.5-2B
Fine-tuning method: QLoRA (r=16, alpha=32)
Training data: 3,140 interactive fiction examples across multiple genres
Training hardware: NVIDIA GeForce GTX 1060 6GB
Training time: ~9.5 hours

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "SatorTenet/StoryEngine-2B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.float16, device_map="auto")

messages = [
    {
        "role": "system",
        "content": (
            "You are StoryEngine — an interactive fiction model.\n"
            "Genre: Dark Fantasy | Tone: tense, mysterious\n"
            "Scene: 1/5\nVitality: 100 | Saga: 0"
        ),
    },
    {"role": "user", "content": "Start a new story."},
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=300, temperature=0.8, top_p=0.9, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Ollama

ollama run storyengine:2b

Genres

The model was trained on stories spanning multiple genres including:

Dark Fantasy
Mythic Norse
Sci-Fi
Horror
and more

License

Apache 2.0 — same as the base model.