MonkeyOCRv2

https://github.com/yuliang-liu/monkeyocrv2
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MonkeyOCRv2 is a document-native visual-text foundation model designed for document AI, supporting...
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### Start Docker container for MS-Swift

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Frequently-asked-questions.md

Example command to launch a Docker container with the necessary environment for MS-Swift.

```bash
docker run --gpus all -p 8000:8000 -it -d --name ms modelscope-registry.cn-beijing.cr.aliyuncs.com/modelscope-repo/modelscope:ubuntu22.04-cuda12.4.0-py311-torch2.6.0-1.26.0-LLM /bin/bash
```

--------------------------------

### Install ms-swift from source

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/GetStarted/SWIFT-installation.md

Commands to clone the repository and install ms-swift in editable mode.

```shell
# pip install git+https://github.com/modelscope/ms-swift.git

# Full capabilities
# pip install "git+https://github.com/modelscope/ms-swift.git#egg=ms-swift[all]"

git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e .

# Full capabilities
# pip install -e '.[all]'
```

--------------------------------

### Install JpegIO from source

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/forensics/libs/jpegio/README.md

Recommended installation method involving compilation of C/C++ source code.

```bash
python setup.py install
```

--------------------------------

### Install dependencies for Qwen3-VL

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/Qwen3-VL-Best-Practice.md

Install the required packages including transformers, qwen_vl_utils, and ms-swift. Optionally install vllm if using it as an inference backend.

```shell
pip install "transformers>=4.57" "qwen_vl_utils>=0.0.14"

pip install "ms-swift>=3.9.1"
# pip install "vllm>=0.11.0"  # If using the vLLM inference backend for inference
```

--------------------------------

### Qwen3-Embedding Custom Instruction Example

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/Embedding.md

Example of adding a system instruction to the Qwen3-Embedding input prompt.

```json
{"messages": [
  {"role": "system", "content": "Answer in English and list key points briefly."},
  {"role": "user", "content": "Introduce Qwen3-Embedding"}
]}
```

--------------------------------

### Install ImageMagick 7.1.1 from source

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/formula/cdm_local/README.md

Clones the ImageMagick repository, builds and installs version 7.1.1 from source because apt-get typically installs 6.x. Verify with 'convert --version'.

```bash
git clone https://github.com/ImageMagick/ImageMagick.git ImageMagick-7.1.1

cd ImageMagick-7.1.1

./configure

make

sudo make install

sudo ldconfig /usr/local/lib

convert --version
```

--------------------------------

### Install required packages for training

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/Qwen3-Best-Practice.md

Install ms-swift, transformers, and optional dependencies like deepspeed for multi-GPU training, liger-kernel to save GPU memory, and flash-attn for attention. Run these commands in your environment before starting training.

```bash
pip install ms-swift -U
pip install transformers

pip install deepspeed # for multi-GPU training
pip install liger-kernel # to save GPU memory resources
pip install flash-attn --no-build-isolation  # required for packing
```

--------------------------------

### Install uv and check version

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/docs/wsl_support.md

Install the uv Python package manager via the official script and verify the installation. Expect uv 0.11.x or later.

```bash
curl -LsSf https://astral.sh/uv/install.sh | sh
source ~/.local/bin/env
uv --version  # should show uv 0.11.x or later
```

--------------------------------

### Installation commands

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/forensics/readme.md

Commands to create a conda environment, install PyTorch with CUDA 12.1, install jpegio from source, and install mmsegmentation with mmcv. Includes steps for building jpegio and mmsegmentation.

```bash
conda create -n monkeyocrv2 python=3.10 -y
conda activate monkeyocrv2
pip install torch==2.5.0 torchvision==0.20.0 torchaudio==2.5.0 --index-url https://download.pytorch.org/whl/cu121

# install jpegio
cd forensics/libs/jpegio
pip install -r requirements.txt
python setup.py install

# install mmsegmentation
cd ../../
pip install -r requirements.txt
pip install -U openmim
mim install mmengine
mim pip install mmcv==2.2.0 -f https://download.openmmlab.com/mmcv/dist/cu121/torch2.4/index.html
pip install -v -e .
```

--------------------------------

### Install JpegIO on Linux

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/forensics/libs/jpegio/README.md

Install using a pre-compiled wheel for Linux architectures.

```bash
pip install dist/jpegio-x.x.x-cp3x-cp3x-linux_<your architecture>.whl
```

--------------------------------

### Start model service with swift deploy

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/GRPO/DeveloperGuide/reward_model.md

Use this bash command to start the model service with vLLM tensor parallelism. Note: Do not overlap deployment devices with training devices. The output shows the server starting and running on http://0.0.0.0:8000.

```bash
# Note: Do not overlap deployment devices with training devices
CUDA_VISIBLE_DEVICES=0,1,2,3 \
swift deploy \
    --model Qwen/Qwen2.5-72B-Instruct \
    --vllm_tensor_parallel_size 4

# [INFO:swift] model_list: ['Qwen2.5-72B-Instruct']
# INFO:     Started server process [xxxxxx]
# INFO:     Waiting for application startup.
# INFO:     Application startup complete.
# INFO:     Uvicorn running on http://0.0.0.0:8000 (Press CTRL+C to quit)
```

--------------------------------

### Install JpegIO on Windows 10 x64

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/forensics/libs/jpegio/README.md

Install using a pre-compiled wheel for Windows 10 x64.

```bash
pip install dist/jpegio-x.x.x-cp3x-cp3x-win_amd64.whl
```

--------------------------------

### Optimizer CPU Offload Example

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Megatron-SWIFT/Command-line-parameters.md

Example command to enable CPU offloading of optimizer states with precision-aware optimizer. Set --use_precision_aware_optimizer true, --optimizer_cpu_offload true, and --optimizer_offload_fraction 0.7.

```bash
--use_precision_aware_optimizer true --optimizer_cpu_offload true --optimizer_offload_fraction 0.7
```

--------------------------------

### Install MS-Swift

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Sample.md

Commands to install the ms-swift package via pip or from the source repository.

```shell
pip install ms-swift[llm] -U
```

```shell
git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e '.[llm]'
```

--------------------------------

### Install LaTeX (texlive-full)

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/formula/cdm_local/README.md

Installs the full TeX Live distribution via apt-get, which is required for rendering PDF files.

```bash
apt-get update

sudo apt-get install texlive-full
```

--------------------------------

### File structure after setup

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/docs/wsl_support.md

Shows the directory layout of the MonkeyOCRv2 project after setup, including docs, model weights, parsing source, virtual environment, and service log.

```text
MonkeyOCRv2/
├── docs/
│   └── wsl_support.md          # This guide
├── model_weight/
│   └── MonkeyOCRv2-S-Parsing/
│       ├── config.json
│       ├── configuration_monkeyocrv2.py
│       ├── model.safetensors   # ~1.5GB
│       └── ...
├── parsing/
│   ├── serve.py                # vLLM serve entry point
│   ├── modeling/
│   │   └── modeling_monkeyocrv2_vllm.py  # Custom vLLM model registration
│   └── ...
├── .venv/                      # Python virtual environment
└── vllm_serve.log              # Service log
```

--------------------------------

### Install ms-swift from source with eval extras

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Evaluation.md

Clones the ms-swift repository and installs it in editable mode with the eval extra.

```shell
git clone https://github.com/modelscope/ms-swift.git
cd ms-swift
pip install -e '.[eval]'
```

--------------------------------

### Install and run pre-commit for code lint

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/CONTRIBUTING.md

Run these commands in the swift folder to install pre-commit and check all files for code style compliance. Fix any errors reported until all checks pass.

```shell
pip install pre-commit # In the swift folder
pre-commit run --all-files # Fix the errors reported by pre-commit until all checks are successful
```

--------------------------------

### Install ms-swift and dependencies

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/MLLM-Registration.md

Install the required packages: ms-swift version 3.9 to 3.10, transformers 4.57.*, and qwen_omni_utils 0.0.8.

```shell
pip install "ms-swift>=3.9,<3.10"

pip install "transformers==4.57.*" "qwen_omni_utils==0.0.8"
```

--------------------------------

### Start vLLM serving for document parsing

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/README.md

Starts a vLLM service for document parsing. Use serve.py with -d for DFlash, --tp for tensor parallelism, and -p for port. Run from the parsing directory.

```bash
cd parsing
# Serve with DFlash for faster inference
python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing -d ../model_weight/MonkeyOCRv2-B-Parsing-DFlash -p 8888
# Serve without DFlash
python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing  -p 8888
# 2 GPUs
python serve.py -m ../model_weight/MonkeyOCRv2-B-Parsing --tp 2 -p 8888
# Show help messages
python serve.py -h
```

--------------------------------

### Install ms-swift via pip

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/GetStarted/SWIFT-installation.md

Standard installation commands for ms-swift using pip, including options for evaluation and full capabilities.

```shell
# recommend
pip install 'ms-swift'
# For evaluation usage
pip install 'ms-swift[eval]' -U
# Full capabilities
pip install 'ms-swift[all]' -U
```

--------------------------------

### Install dependencies for GRPO training

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/Qwen3-Best-Practice.md

Install math_verify and vllm==0.8.5.post1, which are required in addition to ms-swift dependencies for GRPO training.

```shell
pip install "math_verify"
pip install vllm==0.8.5.post1
```

--------------------------------

### Serve web demo and show help (Linux)

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/docs/cpu_support.md

Starts the Gradio web demo with the specified model path, output directory, server name, port, and concurrency settings. Also shows help messages for gradio_demo_cpu.py.

```bash
cd parsing
python cpu/gradio_demo_cpu.py \
    --model-path ../model_weight/MonkeyOCRv2-B-Parsing \
    --output-dir output/demo_cpu_outputs \
    --demo-server-name 127.0.0.1 \
    --demo-server-port 8891 \
    --demo-concurrency 1 \
    --page-max-inflight 1
# Show help messages
python cpu/gradio_demo_cpu.py -h
```

--------------------------------

### Serve web demo (Windows)

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/docs/cpu_support.md

Starts the Gradio web demo with the specified model path, output directory, server name, port, and concurrency settings.

```bash
cd .\parsing\
..\.venv\Scripts\python.exe cpu\gradio_demo_cpu.py `
    --model-path ..\model_weight\MonkeyOCRv2-B-Parsing `
    --output-dir output\demo_cpu_outputs `
    --demo-server-name 127.0.0.1 `
    --demo-server-port 8891 `
    --demo-concurrency 1 `
    --page-max-inflight 1
```

--------------------------------

### Install ms-swift and deepspeed

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md

Install the latest ms-swift package. If using DeepSpeed ZeRO2/ZeRO3, also install deepspeed.

```shell
pip install ms-swift -U

# If using deepspeed zero2/zero3
pip install deepspeed -U
```

--------------------------------

### Deploying a model with vLLM backend

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/examples/deploy/README.md

Example command to deploy the Qwen2.5-7B-Instruct model using the vLLM inference backend on a specific GPU.

```shell
CUDA_VISIBLE_DEVICES=0 \
swift deploy \
    --model Qwen/Qwen2.5-7B-Instruct \
    --infer_backend vllm
```

--------------------------------

### Install Python requirements

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/formula/cdm_local/README.md

Installs the Python dependencies listed in requirements.txt.

```bash
pip install -r requirements.txt
```

--------------------------------

### Configure UNSLOTH training parameters

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Command-line-parameters.md

Example command-line arguments to enable Unsloth backend with specific training type and quantization settings.

```bash
--tuner_backend unsloth
--train_type full/lora
--quant_bits 4
```

--------------------------------

### Startup parameters for colocate mode

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/GRPO/GetStarted/GRPO.md

Use these parameters to enable vLLM in colocate mode, where training and inference share GPU resources.

```bash
--use_vllm true \
--vllm_mode colocate
```

--------------------------------

### Build docs with make docs

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/README.md

Run this command in the root directory to build the documentation.

```shell
# in root directory:
make docs
```

--------------------------------

### Install JpegIO on macOS x64

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/forensics/libs/jpegio/README.md

Install using a pre-compiled wheel for macOS x64.

```bash
pip install dist/jpegio-x.x.x-cp3x-cp3x-macosx_<version>_x86_64.whl
```

--------------------------------

### Google style docstring example for load function

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/README.md

Shows the required docstring format for functions, including Args, Examples, and Returns sections. The example is a `load` function that loads data from serialized files.

```python
def load(file, file_format=None, **kwargs):
    """Load data from json/yaml/pickle files.

    This method provides a unified api for loading data from serialized files.

    Args:
        file (str or :obj:`Path` or file-like object): Filename or a file-like
            object.
        file_format (str, optional): If not specified, the file format will be
            inferred from the file extension, otherwise use the specified one.
            Currently supported formats include "json", "yaml/yml".

    Examples:
        >>> load('/path/of/your/file')  # file is stored in disk
        >>> load('https://path/of/your/file')  # file is stored on internet
        >>> load('oss://path/of/your/file')  # file is stored in petrel

    Returns:
        The content from the file.
    """
```

--------------------------------

### Install DPText-DETR add-on with install.sh

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/detection/DPText-DETR/README.md

Run from the add-on directory to clone and patch DPText-DETR into ./DPText-DETR.

```bash
bash install.sh            # clones DPText-DETR into ./DPText-DETR and patches it
```

--------------------------------

### Serve with Web Demo

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/README.md

Starts a Gradio web demo for document parsing. Requires a running vLLM service. Access at http://localhost:8891.

```bash
cd parsing
python demo/gradio_demo.py -s http://127.0.0.1:8888 -p 8891
# Show help messages
python demo/gradio_demo.py -h
```

--------------------------------

### Create conda environment and install dependencies

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/formula/README.md

Creates a conda environment named 'monkeyocrv2_formula' with Python 3.10, installs PyTorch with CUDA 12.1, and installs project requirements. The CDM requirements are needed for CDM evaluation.

```bash
cd formula
conda create -n monkeyocrv2_formula python=3.10 pip
conda activate monkeyocrv2_formula
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install -r requirements.txt
pip install -r cdm_local/requirements.txt  # required for CDM evaluation
pip install -e .
```

--------------------------------

### Launch Gradio demo

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/formula/cdm_local/README.md

Starts the Gradio web demo for CDM.

```bash
python app.py
```

--------------------------------

### Install ms-swift with eval extras via pip

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Evaluation.md

Installs the ms-swift package with the eval extra using pip.

```shell
pip install ms-swift[eval] -U
```

--------------------------------

### Install older versions of ms-swift

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/GetStarted/SWIFT-installation.md

Command to install version 2.x of ms-swift, which is incompatible with versions 3.0 and later.

```shell
pip install ms-swift==2.*
```

--------------------------------

### Deploy fine-tuned model with swift CLI

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Pre-training-and-Fine-tuning.md

Starts the deployment server for a fine-tuned model. Use --model for full parameter training or --adapters for LoRA checkpoints.

```shell
CUDA_VISIBLE_DEVICES=0 \
swift deploy \
    --adapters output/vx-xxx/checkpoint-xxx \
    --infer_backend pt \
    --temperature 0 \
    --max_new_tokens 2048 \
    --served_model_name '<model-name>'
```

--------------------------------

### Inference output example

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Megatron-SWIFT/Quick-start.md

Example output from the swift infer command, showing the model's self-identification response.

```default
<<< who are you?
I am a language model developed by swift, you can call me swift-robot. How can I assist you?
```

--------------------------------

### Basic RLOO configuration with --advantage_estimator rloo and --kl_in_reward true

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/GRPO/AdvancedResearch/RLOO.md

Command-line arguments to enable RLOO training using GRPOTrainer. Set --advantage_estimator to rloo for the leave-one-out advantage estimator and --kl_in_reward to true to integrate KL divergence into the reward, which is the default for RLOO.

```bash
# Basic RLOO configuration
--advantage_estimator rloo  # Use RLOO's leave-one-out advantage estimator
--kl_in_reward true         # Integrate KL divergence into the reward (default for RLOO)
```

--------------------------------

### Start vLLM service for MonkeyOCRv2 Und

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/README.md

Starts the vLLM inference server for the MonkeyOCRv2-B-Und model on port 8889.

```bash
cd understanding
python serve.py -m ../model_weight/MonkeyOCRv2-B-Und  -p 8889
```

--------------------------------

### Image preprocessing with qwen_vl_utils.fetch_image

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/GRPO/AdvancedResearch/deepeyes.md

Shows how to preprocess images for the Qwen2.5-VL series before feeding them to the image_zoom_in_tool. It loads the image as PIL.Image and applies cropping, equivalent to using the MAX_PIXELS environment variable.

```python
from qwen_vl_utils import fetch_image
# At this point, images have not yet been preprocessed
infer_request.images
# Load as PIL.Image and apply cropping (same as using the MAX_PIXELS environment variable)
img = fetch_image({'image': load_pil_image(infer_request.images[0])})
```

--------------------------------

### Install training environment for MonkeyOCRv2-Parsing

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/README.md

Creates a Conda environment, installs PyTorch for CUDA 12.6, and installs required dependencies including transformers, accelerate, qwen_vl_utils, flash-attn, and the bundled ms-swift package. Run from the train directory. flash-attn is optional but recommended.

```bash
conda create -n monkeyocrv2-train python=3.11 -y
conda activate monkeyocrv2-train

# Example for CUDA 12.6. Adjust this command for your CUDA environment.
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126

pip install transformers==4.57.1 accelerate==1.11.0 qwen_vl_utils==0.0.14
pip install flash-attn==2.8.3 --no-build-isolation
pip install -e ./ms-swift
```

--------------------------------

### Train with command line using swift sft

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/BestPractices/MLLM-Registration.md

Shell command to start training with swift sft. Uses 4 GPUs, sets environment variables for memory and pixel limits, and includes deepspeed zero2. Note the comment '# 4 * 35GiB' indicating memory usage.

```shell
# 4 * 35GiB
PYTORCH_CUDA_ALLOC_CONF='expandable_segments:True' \
CUDA_VISIBLE_DEVICES=0,1,2,3 \
NPROC_PER_NODE=4 \
VIDEO_MAX_PIXELS=50176 \
FPS_MAX_FRAMES=12 \
MAX_PIXELS=1003520 \
swift sft \
    --model Qwen/Qwen2.5-Omni-7B \
    --model_type my_qwen2_5_omni \
    --template my_qwen2_5_omni \
    --custom_register_path 'examples/custom/my_qwen2_5_omni/my_register.py' \
    --dataset 'AI-ModelScope/alpaca-gpt4-data-zh#2000' \
              'AI-ModelScope/LaTeX_OCR:human_handwrite#2000' \
              'speech_asr/speech_asr_aishell1_trainsets:validation#2000' \
              'swift/VideoChatGPT:all#2000' \
    --load_from_cache_file true \
    --split_dataset_ratio 0.01 \
    --train_type lora \
    --torch_dtype bfloat16 \
    --attn_impl flash_attn \
    --padding_free true \
    --packing true \
    --num_train_epochs 1 \
    --per_device_train_batch_size 1 \
    --per_device_eval_batch_size 1 \
    --learning_rate 1e-4 \
    --lora_rank 8 \
    --lora_alpha 32 \
    --target_modules all-linear \
    --freeze_vit true \
    --freeze_aligner true \
    --gradient_accumulation_steps 1 \
    --eval_steps 50 \
    --save_steps 50 \
    --save_total_limit 2 \
    --logging_steps 5 \
    --max_length 4096 \
    --output_dir output \
    --warmup_ratio 0.05 \
    --dataloader_num_workers 4 \
    --dataset_num_proc 1 \
    --deepspeed zero2
```

--------------------------------

### Install dependencies timm and pypdfium2

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/docs/wsl_support.md

Install the required Python packages timm version 1.0.27 and pypdfium2 version 5.10.1.

```bash
uv pip install timm==1.0.27 pypdfium2==5.10.1
```

--------------------------------

### Pseudo-code: web-ui wraps command line via os.system

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/GetStarted/Web-UI.md

Illustrates that the web-ui starts training/deployment as an independent process using the command line. This is pseudo-code, not actual usage.

```python
import os
os.system('swift sft --model xxx --dataset xxx')
```

--------------------------------

### Messages format input for swift.inference

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Instruction/Frequently-asked-questions.md

Shows an example of messages format input. This is supported in swift3, refer to the demo_agent example.

```text
{"messages": [{"role": "system", "content": "<system>"}, {"role": "user", "content": "<query1>"}, {"role": "assistant", "content": "answer1, "}]}
```

--------------------------------

### dataset_info.json metadata examples

Source: https://github.com/yuliang-liu/monkeyocrv2/blob/main/parsing/train/ms-swift/docs/source_en/Customization/Custom-dataset.md

Examples of dataset metadata configurations including dataset IDs, paths, subsets, and column mapping.

```json
[
  {
    "ms_dataset_id": "xxx/xxx"
  },
  {
    "dataset_path": "<dataset_dir/dataset_path>"
  },
  {
    "ms_dataset_id": "<dataset_id>",
    "subsets": ["v1"],
    "split": ["train", "validation"],
    "columns": {
      "input": "query",
      "output": "response"
    }
  },
  {
    "ms_dataset_id": "<dataset_id>",
    "hf_dataset_id": "<hf_dataset_id>",
    "subsets": [{
      "subset": "subset1",
      "columns": {
        "problem": "query",
        "content": "response"
      }
    },
    {
      "subset": "subset2",
      "columns": {
        "messages": "_",
        "new_messages": "messages"
      }
    }]
  }
]
```
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