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Major Features and Improvements

Arch

• Some bugs fixed for spark computing engine

Component

• Unified IO keys naming format for all components
• Add LLMLoader to support running FATE-LLM v2.0 with pipeline

OSX

• Compatible with eggroll-v2.x

EggRoll

• add 2.x api backport support
• bug fix

FATE-Flow

• Improved the display issue of output data.

• Enhanced the PyPI package: configuration files have been relocated to the user's home directory, and the relative paths for uploading data are based on the user's home directory.

• Added support for running FATE algorithms with Spark + Hadoop.

• Fixed an issue where failed tasks could not be retried.

• Fixed an issue where the system couldn't run when the task cores exceeded the system total cores.

FATE-Client

• Pipeline: add supports for fate-llm 2.0
  • newly added LLMModelLoader, LLMDatasetLoader, LLMDataFuncLoader
  • newly added configuration parsing of seq2seq_runner and ot_runner
• Pipeline: unified input interface of components

FATE-LLM

• Adapt to fate-v2.0 framework:
  • Migrate parameter-efficient fine-tuning training methods and models.
  • Migrate Standard Offsite-Tuning and Extended Offsite-Tuning（Federated Offsite-Tuning+)
  • Newly trainer，dataset, data_processing function design
• New FedKSeed Federated Tuning Algorithm: train large language models in a federated learning setting with extremely low communication cost

FATE-Test

• Add Support for Job Runtime Configuration