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unitorch provides efficient implementation of popular unified NLU / NLG / CV / CTR / MM / RL models with PyTorch.

Project description

Introduction

🔥 unitorch is a library that simplifies and accelerates the development of unified models for natural language understanding, natural language generation, computer vision, click-through rate prediction, multimodal learning and reinforcement learning. It is built on top of PyTorch and integrates seamlessly with popular frameworks such as transformers, peft, diffusers, and fastseq. With unitorch, you can use a single command line tool or a one-line code import unitorch import to leverage the state-of-the-art models and datasets without sacrificing performance or accuracy.


What's New Model


Features

  • User-Friendly Python Package
  • Faster & Streamlined Train/Inference
  • Deepspeed Integration for Large-Scale Models
  • CUDA Optimization
  • Extensive STOA Model & Task Supports

Installation

pip3 install unitorch

Quick Examples

Source Code

import unitorch

# import bart model
from unitorch.models.bart import BartForGeneration
model = BartForGeneration("path/to/bart/config.json")

# use the configuration class
from unitorch.cli import CoreConfigureParser
config = CoreConfigureParser("path/to/config.ini")

Multi-GPU Training

torchrun --no_python --nproc_per_node 4 \
	unitorch-train examples/configs/generation/bart.ini \
	--train_file path/to/train.tsv --dev_file path/to/dev.tsv

Single-GPU Inference

unitorch-infer examples/configs/generation/bart.ini --test_file path/to/test.tsv

Find more details in the Tutorials section of the documentation.

License

Code released under MIT license.

Project details


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