What this PR does / why we need it?

The FSDP2 text pretraining example in docs/zh/features/pretrain.md does not match the current implementation.

The existing example only sets attr.pretrain: true. However, the FSDP2 backend selects PretrainDatasetProcessor through basic_parameters.stage. Without stage: pretrain, the stage defaults to sft, causing text-only samples without a response field to be dropped and eventually resulting in StopIteration.

This PR:

• Adds the required basic_parameters.stage: pretrain setting for the FSDP2 backend.
• Replaces the deprecated collate_param.model_name with collate_param.collator_id.
• Clarifies the configuration differences between the FSDP2 and Megatron backends.
• Corrects the packing description: text is packed within each preprocessing batch, so each batch must contain at least cutoff_len valid tokens to generate a training sample.
• Improves the formatting and readability of the guide.

Related issue: N/A. The problem was found while verifying the documented workflow against the current master implementation.

Does this PR introduce any user-facing change?

Yes. This PR corrects the user-facing configuration required to run text-only pretraining with the FSDP2 backend.

Users should configure:

data:
  dataset_param:
    attr:
      formatting: alpaca
      prompt: text
    basic_parameters:
      stage: pretrain
      template: default
  dataloader_param:
    collate_param:
      collator_id: llm_pretrain