Instructions to use XD-MU/ScriptAgent with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use XD-MU/ScriptAgent with PEFT:
```
Task type is invalid.
```

How to use XD-MU/ScriptAgent with Transformers:

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

pipe = pipeline("text-generation", model="XD-MU/ScriptAgent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForTextToWaveform

processor = AutoProcessor.from_pretrained("XD-MU/ScriptAgent")
model = AutoModelForTextToWaveform.from_pretrained("XD-MU/ScriptAgent")
messages = [
    {"role": "user", "content": "Who are you?"},
]
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

vLLM

How to use XD-MU/ScriptAgent with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/XD-MU/ScriptAgent

SGLang

How to use XD-MU/ScriptAgent 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 "XD-MU/ScriptAgent" \
    --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": "XD-MU/ScriptAgent",
		"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 "XD-MU/ScriptAgent" \
        --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": "XD-MU/ScriptAgent",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use XD-MU/ScriptAgent with Docker Model Runner:
```
docker model run hf.co/XD-MU/ScriptAgent
```

ScriptAgent / chat_template.jinja

XD-MU

Initial model upload

876868b verified 5 months ago

raw

history blame contribute delete

1.28 kB

	{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<\|im_start\|>system
	You are a helpful assistant.<\|im_end\|>
	{% endif %}<\|im_start\|>{{ message['role'] }}
	{% if message['content'] is string %}{{ message['content'] }}<\|im_end\|>
	{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<\|vision_bos\|><\|IMAGE\|><\|vision_eos\|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<\|audio_bos\|><\|AUDIO\|><\|audio_eos\|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<\|vision_bos\|><\|VIDEO\|><\|vision_eos\|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<\|im_end\|>
	{% endif %}{% endfor %}{% if add_generation_prompt %}<\|im_start\|>assistant
	{% endif %}

	{% set audio_count = namespace(value=0) %}{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<\|im_start\|>system
	You are a helpful assistant.<\|im_end\|>
	{% endif %}<\|im_start\|>{{ message['role'] }}
	{% if message['content'] is string %}{{ message['content'] }}<\|im_end\|>
	{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<\|vision_bos\|><\|IMAGE\|><\|vision_eos\|>{% elif content['type'] == 'audio' or 'audio' in content or 'audio_url' in content %}{% set audio_count.value = audio_count.value + 1 %}{% if add_audio_id %}Audio {{ audio_count.value }}: {% endif %}<\|audio_bos\|><\|AUDIO\|><\|audio_eos\|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<\|vision_bos\|><\|VIDEO\|><\|vision_eos\|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<\|im_end\|>
	{% endif %}{% endfor %}{% if add_generation_prompt %}<\|im_start\|>assistant
	{% endif %}