Instructions to use Alignment-Lab-AI/Scrollplay with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Alignment-Lab-AI/Scrollplay with Transformers:

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

pipe = pipeline("text-generation", model="Alignment-Lab-AI/Scrollplay")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Alignment-Lab-AI/Scrollplay")
model = AutoModelForMultimodalLM.from_pretrained("Alignment-Lab-AI/Scrollplay")
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 Alignment-Lab-AI/Scrollplay with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Alignment-Lab-AI/Scrollplay

SGLang

How to use Alignment-Lab-AI/Scrollplay 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 "Alignment-Lab-AI/Scrollplay" \
    --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": "Alignment-Lab-AI/Scrollplay",
		"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 "Alignment-Lab-AI/Scrollplay" \
        --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": "Alignment-Lab-AI/Scrollplay",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Alignment-Lab-AI/Scrollplay with Docker Model Runner:
```
docker model run hf.co/Alignment-Lab-AI/Scrollplay
```

Scrollplay / model-00003-of-00003.safetensors

Alignment-Lab-AI

Upload folder using huggingface_hub

3083b00 verified over 1 year ago

download

history blame contribute delete

4.68 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 5f858fe56315f938d402ef5ad795aa5af7b9d64f4b1aeb9f38aba9095f0ffec4
Size of remote file:: 4.68 GB
SHA256:: 4f0e19e55da5aa1a957186709c79ddb23e52b96f5dd96b1ce2c68022d437c2b4

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.