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
```

StoryGen-12B / processor_config.json

turtle0001

Upload processor_config.json with huggingface_hub

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	{
	"audio_ms_per_token": 40,
	"audio_seq_length": 750,
	"feature_extractor": {
	"audio_samples_per_token": 640,
	"feature_extractor_type": "Gemma4UnifiedAudioFeatureExtractor",
	"feature_size": 640,
	"padding_side": "right",
	"padding_value": 0.0,
	"return_attention_mask": true,
	"sampling_rate": 16000
	},
	"image_processor": {
	"do_convert_rgb": true,
	"do_normalize": false,
	"do_rescale": true,
	"do_resize": true,
	"image_mean": [
	0.0,
	0.0,
	0.0
	],
	"image_processor_type": "Gemma4UnifiedImageProcessor",
	"image_std": [
	1.0,
	1.0,
	1.0
	],
	"max_soft_tokens": 280,
	"patch_size": 16,
	"pooling_kernel_size": 3,
	"resample": 3,
	"rescale_factor": 0.00392156862745098
	},
	"image_seq_length": 280,
	"processor_class": "Gemma4UnifiedProcessor",
	"video_processor": {
	"do_convert_rgb": true,
	"do_normalize": true,
	"do_rescale": true,
	"do_resize": true,
	"do_sample_frames": true,
	"image_mean": [
	0.0,
	0.0,
	0.0
	],
	"image_std": [
	1.0,
	1.0,
	1.0
	],
	"max_soft_tokens": 70,
	"num_frames": 32,
	"patch_size": 16,
	"pooling_kernel_size": 3,
	"resample": 3,
	"rescale_factor": 0.00392156862745098,
	"return_metadata": false,
	"video_processor_type": "Gemma4UnifiedVideoProcessor"
	}
	}