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

Libraries

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

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

pipe = pipeline("text-generation", model="turtle0001/StoryGen-12B-It")
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("turtle0001/StoryGen-12B-It")
model = AutoModelForMultimodalLM.from_pretrained("turtle0001/StoryGen-12B-It", 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 turtle0001/StoryGen-12B-It with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turtle0001/StoryGen-12B-It"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turtle0001/StoryGen-12B-It",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use turtle0001/StoryGen-12B-It 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-It" \
    --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": "turtle0001/StoryGen-12B-It",
		"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 "turtle0001/StoryGen-12B-It" \
        --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": "turtle0001/StoryGen-12B-It",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

StoryGen-12B-It / chat_template.jinja

turtle0001

(Trained with Unsloth)

700c52e verified 3 months ago

Raw History Blame Contribute Delete

2.38 kB

	{{ bos_token }}{%- macro strip_thinking(text) -%}
	{%- set ns = namespace(result='') -%}
	{%- for part in text.split('<channel\|>') -%}
	{%- if '<\|channel>' in part -%}
	{%- set ns.result = ns.result + part.split('<\|channel>')[0] -%}
	{%- else -%}
	{%- set ns.result = ns.result + part -%}
	{%- endif -%}
	{%- endfor -%}
	{{- ns.result \| trim -}}
	{%- endmacro -%}
	{%- set thinking = enable_thinking is defined and enable_thinking -%}
	{%- set loop_messages = messages -%}
	{%- if messages[0]['role'] in ['system', 'developer'] or thinking -%}
	{{ '<\|turn>system
	' }}
	{%- if thinking -%}
	{{ '<\|think\|>
	' }}
	{%- endif -%}
	{%- if messages[0]['role'] in ['system', 'developer'] -%}
	{{ messages[0]['content'] \| trim }}
	{%- set loop_messages = messages[1:] -%}
	{%- endif -%}
	{{ '<turn\|>
	' }}
	{%- endif -%}
	{%- for message in loop_messages -%}
	{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
	{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
	{%- endif -%}
	{%- if (message['role'] == 'assistant') -%}
	{%- set role = "model" -%}
	{%- else -%}
	{%- set role = message['role'] -%}
	{%- endif -%}
	{{ '<\|turn>' + role + '
	' }}
	{%- if message['content'] is string -%}
	{%- if role == "model" -%}
	{{ strip_thinking(message['content']) }}
	{%- else -%}
	{{ message['content'] \| trim }}
	{%- endif -%}
	{%- elif message['content'] is iterable -%}
	{%- for item in message['content'] -%}
	{%- if item['type'] == 'audio' -%}
	{{ '<\|audio\|>' }}
	{%- elif item['type'] == 'image' -%}
	{{ '<\|image\|>' }}
	{%- elif item['type'] == 'video' -%}
	{{ '<\|video\|>' }}
	{%- elif item['type'] == 'text' -%}
	{%- if role == "model" -%}
	{{ strip_thinking(item['text']) }}
	{%- else -%}
	{{ item['text'] \| trim }}
	{%- endif -%}
	{%- endif -%}
	{%- endfor -%}
	{%- else -%}
	{{ raise_exception("Invalid content type") }}
	{%- endif -%}
	{{ '<turn\|>
	' }}
	{%- endfor -%}
	{%- if add_generation_prompt -%}
	{{'<\|turn>model
	'}}
	{%- endif -%}