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.
- Transformers
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: 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 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
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)
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docker model run hf.co/XD-MU/ScriptAgent