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 / README.md

XD-MU

Update README.md

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	---
	base_model: XD-MU/ScriptAgent
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- base_model:adapter:XD-MU/ScriptAgent
	- lora
	- transformers
	---


	# ScriptAgent: Dialogue-to-Shooting-Script Generation Model

	This model is a fine-tuned adapter (LoRA) on top of the `XD-MU/ScriptAgent` base model, designed to generate detailed shooting scripts from dialogue inputs. It is trained to transform conversational text into structured screenplay formats suitable for film or video production.

	The model is compatible with [ms-swift](https://github.com/modelscope/swift) and supports efficient inference via the vLLM backend.

	> 💡 Note: This repository contains a PEFT adapter (e.g., LoRA). To use it, you must merge it with the original base model or load it via `ms-swift`.

	## ▶️ Inference with ms-swift (vLLM Backend)

	To generate shooting scripts from dialogue inputs, use the following command with ms-swift:

	You can find DialoguePrompts here: https://huggingface.co/datasets/XD-MU/DialoguePrompts
	```bash
	import os
	from huggingface_hub import snapshot_download

	os.environ['CUDA_VISIBLE_DEVICES'] = '0'

	model_name = "XD-MU/ScriptAgent"
	local_path = "./models/ScriptAgent"

	# 下载整个仓库的所有文件
	print("下载模型所有文件...")
	snapshot_download(
	repo_id=model_name,
	local_dir=local_path,
	local_dir_use_symlinks=False,
	resume_download=True
	)

	print(f"模型已完整下载到: {local_path}")

	# 使用 SWIFT 加载
	from swift.llm import PtEngine, RequestConfig, InferRequest

	engine = PtEngine(local_path, max_batch_size=1)
	request_config = RequestConfig(max_tokens=8192, temperature=0.7)

	infer_request = InferRequest(messages=[
	{"role": "user", "content": "你的对话上下文(Your Dialogue)"}
	])
	response = engine.infer([infer_request], request_config)[0]

	print(response.choices[0].message.content)
	```