Instructions to use turtle0001/StoryGen-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turtle0001/StoryGen-12B with Transformers:

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

pipe = pipeline("text-generation", model="turtle0001/StoryGen-12B")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("turtle0001/StoryGen-12B")
model = AutoModelForMultimodalLM.from_pretrained("turtle0001/StoryGen-12B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use turtle0001/StoryGen-12B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turtle0001/StoryGen-12B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turtle0001/StoryGen-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/turtle0001/StoryGen-12B

SGLang

How to use turtle0001/StoryGen-12B 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 "turtle0001/StoryGen-12B" \
    --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": "turtle0001/StoryGen-12B",
		"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 "turtle0001/StoryGen-12B" \
        --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": "turtle0001/StoryGen-12B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use turtle0001/StoryGen-12B with Docker Model Runner:
```
docker model run hf.co/turtle0001/StoryGen-12B
```

turtle0001 commited on Jul 19

Commit

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Update README.md

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@@ -38,11 +38,10 @@ This model adapts the Gemma 4 12B base architecture to a specific narrative doma
 ### Direct Use
-This model is designed as a base checkpoint for further fine-tuning. It can be used for zero-shot or few-shot narrative generation when paired with an appropriate system prompt or prefix, but it does not follow instructions natively. Best suited as a foundation for style-specific SFT adapters.
 ### Downstream Use
-Intended as the base model for supervised fine-tuning into instruction-following storytelling assistants, scriptwriting tools, or interactive narrative applications. A compatible SFT LoRA adapter (`Marekoth-Story-SFT-LoRA`) is available separately.
 ### Out-of-Scope Use

 ### Direct Use
+This model is designed as a base checkpoint for further fine-tuning. It can be used for zero-shot or few-shot narrative generation when paired with an appropriate system prompt or prefix, but it does not follow instructions natively. Best suited as a foundation for style-specific It version.
 ### Downstream Use
+Intended as the base model for supervised fine-tuning into instruction-following storytelling assistants, scriptwriting tools, or interactive narrative applications. An It version (`StoryGen-12B-It`) is available separately.
 ### Out-of-Scope Use