README.md

metadata

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 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)