FlagAI: Toolkit for Large-Scale General AI Models

1 min


FlagAI

FlagAI

Toolkit for Large-Scale General AI Models

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FlagAI is a fast, easy-to-use and extensible toolkit for large-scale model. It supports training, fine-tuning, and deploying models on various downstream tasks with multi-modality. It provides an API to quickly download pre-trained models and fine-tune them on multiple datasets. It also allows for parallel training with fewer than 10 lines of code and provides a prompt-learning toolkit for few-shot tasks.

Features

Quickly Download Models via API

Downloading over 30 models via API, such as Aquila, AltCLIP, AltDiffusion, WuDao GLM, etc. for Chinese and English tasks.

Parallel training with less than 10 lines of code .

FlagAI integrates PyTorch, Deepspeed, Megatron-LM, BMTrain for easy data/model parallelism in less than 10 lines of code.

Use few-shot learning tools easily.

FlagAI offers a toolkit for prompt-learning that enables few-shot performance.

Good at Chinese tasks

These models handle (Chinese/English) Text for various tasks, such as text classification, information extraction, question answering, summarization, and text generation. They are especially suitable for Chinese tasks.

Getting Started 🚀

Requirements

  • Python version >= 3.8
  • PyTorch version >= 1.8.0
  • [Optional] For training/testing models on GPUs, you’ll also need to install CUDA and NCCL

Installation

  • To install FlagAI with pip:
pip install -U flagai
  • [Optional] To install FlagAI and develop locally:
git clone https://github.com/FlagAI-Open/FlagAI.git
python setup.py install
  • [Optional] install NVIDIA’s apex For faster training
git clone https://github.com/NVIDIA/apex
cd apex
pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
  • [Optional]  For ZeRO optimizers, install DEEPSPEED (>= 0.7.7)
git clone https://github.com/microsoft/DeepSpeed
cd DeepSpeed
DS_BUILD_CPU_ADAM=1 DS_BUILD_AIO=1 DS_BUILD_UTILS=1 pip install -e .
ds_report # check the deespeed status
  • [Optional] For BMTrain training, install BMTrain (>= 0.2.2)
git clone https://github.com/OpenBMB/BMTrain
cd BMTrain
python setup.py install
  • [Optional] For BMInf low-resource inference, install BMInf
pip install bminf

pip install flash-attn

To access your docker environment on a single node, you have to configure the ports for ssh. For example, use [email protected] with port 711.

>>> vim ~/.ssh/config
Host 127.0.0.1
    Hostname 127.0.0.1
    Port 7110
    User root

To enable secure communication between docker nodes, create ssh keys and distribute the public key to each node (in ~/.ssh/)

>>> ssh-keygen -t rsa -C "[email protected]"

tokenizer and Load model

With the AutoLoad class from FlagAI, you can easily load the model and tokenizer you need, for example:

from flagai.auto_model.auto_loader import AutoLoader

auto_loader = AutoLoader(
    task_name="title-generation",
    model_name="BERT-base-en"
)
model = auto_loader.get_model()
tokenizer = auto_loader.get_tokenizer()

The task_name parameter can be changed to model different tasks. This example shows how to use the title_generation task. You can fine-tune or test the model and tokenizer with this task.

Toolkits and Pre-trained Models

The code is based partially on GLMTransformerstimm and DeepSpeedExamples.

Toolkits

Name Description Examples
GLM_custom_pvp Customizing PET templates README.md
GLM_ptuning p-tuning tool ——
BMInf-generate Accelerating generation README.md

Pre-trained Models

Model Task Train Finetune Inference/Generate Examples
Aquila Natural Language Processing README.md
ALM Arabic Text Generation README.md
AltCLIP Image-Text Matching README.md
AltCLIP-m18 Image-Text Matching README.md
AltDiffusion Text-to-Image Generation README.md
AltDiffusion-m18 Text-to-Image Generation,supporting 18 languages README.md
BERT-title-generation-english English Title Generation README.md
CLIP Image-Text Matching ——
CPM3-finetune Text Continuation ——
CPM3-generate Text Continuation ——
CPM3_pretrain Text Continuation ——
CPM_1 Text Continuation README.md
EVA-CLIP Image-Text Matching README.md
Galactica Text Continuation ——
GLM-large-ch-blank-filling Blank Filling TUTORIAL
GLM-large-ch-poetry-generation Poetry Generation TUTORIAL
GLM-large-ch-title-generation Title Generation TUTORIAL
GLM-pretrain Pre-Train ——
GLM-seq2seq Generation ——
GLM-superglue Classification ——
GPT-2-text-writting Text Continuation TUTORIAL
GPT2-text-writting Text Continuation ——
GPT2-title-generation Title Generation ——
OPT Text Continuation README.md
RoBERTa-base-ch-ner Named Entity Recognition TUTORIAL
RoBERTa-base-ch-semantic-matching Semantic Similarity Matching TUTORIAL
RoBERTa-base-ch-title-generation Title Generation TUTORIAL
RoBERTa-faq Question-Answer README.md
Swinv1 Image Classification ——
Swinv2 Image Classification ——
T5-huggingface-11b Train TUTORIAL
T5-title-generation Title Generation TUTORIAL
T5-flagai-11b Pre-Train ——
ViT-cifar100 Pre-Train ——

Project External Links

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Belmechri

I am an IT engineer, content creator, and proud father with a passion for innovation and excellence. In both my personal and professional life, I strive for excellence and am committed to finding innovative solutions to complex problems.
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