The file starts with leading indentation before the .. directive; Sphinx/reStructuredText directives must start at column 0. Remove the two leading spaces so the initial comment is parsed correctly.

The section underline is shorter than the title, which triggers Sphinx warnings ("Title underline too short."). Make the underline length at least the same as Server and Client.

This page is named ModelOutput.md and defines ModelOutput, but the title and description talk about model input (InputFeature). Update the title and text to match ModelOutput (模型输出) to avoid misleading documentation.

This is the Filter documentation page, but the opening paragraph describes a preprocessor and references dataset.map. Consider rewriting this paragraph to describe filtering (e.g., returning a boolean, typically via dataset.filter).

This line is a Python syntax error because the outer f-string uses single quotes and the inner adapter_name='default' also uses single quotes. Use different quote types (e.g., outer double quotes) or escape the inner quotes.

create_dataset accepts data_slice but ignores it and always uses range(500), which makes partial(create_dataset, data_slice=...) ineffective in callers. Use data_slice when constructing DatasetMeta (and keep the existing default behavior when data_slice is None).

default_system appears to be a separate top-level parameter (see the constructor signature), but it is currently formatted as a sub-bullet under truncation_strategy. Move default_system to its own top-level bullet to reflect the API correctly.

ModelOutput is described as a TypedDict elsewhere; TypedDict types are not callable at runtime. Consider changing this example to return a regular dict (e.g., {'logits': logits, 'loss': loss}) to avoid presenting code that would fail if copy-pasted.

Capitalize the heading for consistency with typical Markdown style (e.g., 'Maintain docs').

There is a large amount of duplicated code between the if and else blocks for running the docker container. This makes the script hard to read and maintain. Any changes to the docker run command will need to be applied in two places, which is error-prone.

You can refactor this by storing the common docker arguments in an array and conditionally adding the debug-specific arguments. This also allows you to remove the duplicated -e TEST_LEVEL=$TEST_LEVEL flag.

  docker_args=(
    --rm --name "$CONTAINER_NAME" --shm-size=16gb
    --cpuset-cpus="${cpu_sets_arr[$idx]}"
    --gpus='"'"device=$gpu"'"'
    -v "$CODE_DIR:$CODE_DIR_IN_CONTAINER"
    -v "$MODELSCOPE_CACHE:$MODELSCOPE_CACHE_DIR_IN_CONTAINER"
    -v "$MODELSCOPE_HOME_CACHE/$idx:/root"
    -v /home/admin/pre-commit:/home/admin/pre-commit
    -e CI_TEST=True
    -e TEST_LEVEL="$TEST_LEVEL"
    -e MODELSCOPE_CACHE="$MODELSCOPE_CACHE_DIR_IN_CONTAINER"
    -e MODELSCOPE_DOMAIN="$MODELSCOPE_DOMAIN"
    -e HUB_DATASET_ENDPOINT="$HUB_DATASET_ENDPOINT"
    -e TEST_ACCESS_TOKEN_CITEST="$TEST_ACCESS_TOKEN_CITEST"
    -e TEST_ACCESS_TOKEN_SDKDEV="$TEST_ACCESS_TOKEN_SDKDEV"
    -e MODELSCOPE_ENVIRONMENT='ci'
    -e TEST_UPLOAD_MS_TOKEN="$TEST_UPLOAD_MS_TOKEN"
    -e MODEL_TAG_URL="$MODEL_TAG_URL"
    -e MODELSCOPE_API_TOKEN="$MODELSCOPE_API_TOKEN"
    -e PR_CHANGED_FILES="$PR_CHANGED_FILES"
    --workdir="$CODE_DIR_IN_CONTAINER"
  )

  if [ "$MODELSCOPE_SDK_DEBUG" == "True" ]; then
    echo 'debugging'
    docker_args+=(-e MODELSCOPE_SDK_DEBUG=True)
  fi

  docker run "${docker_args[@]}" "${IMAGE_NAME}:${IMAGE_VERSION}" $CI_COMMAND

Using eval with a variable that can be set from an environment variable (CI_COMMAND) is a significant security risk. It can allow arbitrary command execution if the CI_COMMAND environment variable is manipulated. It's strongly recommended to avoid eval here. If the command structure is known, you can parse it and execute it more safely, for example by using an array.

The hooks section for pre-commit-hooks is empty. This means none of the useful checks from this repository (like trailing-whitespace, check-yaml, end-of-file-fixer, etc.) will be run. This seems unintentional. Please add back the desired hooks.

    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
      - id: check-added-large-files
      - id: check-merge-conflict

The hooks section for pre-commit-hooks is empty. This means none of the useful checks from this repository will be run. This seems unintentional. Please add back the desired hooks.

    hooks:
      - id: trailing-whitespace
      - id: end-of-file-fixer
      - id: check-yaml
      - id: check-added-large-files
      - id: check-merge-conflict

This link to the documentation is broken. It currently points to (link). Please update it to point to the correct location.

此处的链接是空的，它指向 ()。请更新为指向魔搭官方免费资源的正确链接。

The train function is implemented to always raise a NotImplementedError, which makes the script non-functional as it's called from the if __name__ == '__main__' block. Was this intentional? If this script is meant to be runnable, you should implement the train function.

Disabling TorchDynamo globally with torch._dynamo.disable() might be necessary for compatibility with Megatron-LM, but it prevents potential performance optimizations from torch.compile. Is this disable call strictly necessary for this script to run? If only certain parts are incompatible, consider using torch._dynamo.disable as a context manager or function decorator to scope it more narrowly.

Disabling TorchDynamo globally with torch._dynamo.disable() might be necessary for compatibility with Megatron-LM, but it prevents potential performance optimizations from torch.compile. Is this disable call strictly necessary for this script to run? If only certain parts are incompatible, consider using torch._dynamo.disable as a context manager or function decorator to scope it more narrowly.

Disabling TorchDynamo globally with torch._dynamo.disable() might be necessary for compatibility with Megatron-LM, but it prevents potential performance optimizations from torch.compile. Is this disable call strictly necessary for this script to run? If only certain parts are incompatible, consider using torch._dynamo.disable as a context manager or function decorator to scope it more narrowly.