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Astrai

Astrai is an open-source Python framework built on PyTorch for developing Transformer models, with tools for data preprocessing, distributed training, evaluation, inference and HTTP serving.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Astrai — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Astrai when a project needs a Python/PyTorch workflow that covers Transformer data preparation, training or evaluation and model inference serving.

Who it is for

  • Developers building or training Transformer language and embedding models.
  • Teams preparing datasets, evaluating models, or serving inference from their own checkpoints.

Documented uses

  • Configure data preprocessing for Transformer training.
  • Run pre-training, fine-tuning, preference optimization or online reinforcement-learning workflows.
  • Evaluate models with listed benchmark and scoring tools.
  • Serve model inference through OpenAI- or Anthropic-compatible HTTP chat protocols.
Before you choose

Requirements & limits

The repository is public and identifies the project as Apache-2.0 licensed. The documented setup requires Python 3.12 or newer and PyTorch 2.11.0; several training and generation workflows require CUDA.

  • The framework documents multiple lifecycle stages, from declarative preprocessing to HTTP inference serving.
  • Training and generation workflows have CUDA requirements, while CPU support is limited to explicitly supported components.
  • The server documents OpenAI- and Anthropic-compatible chat protocols, including streaming.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Prepare data and train a Transformer

Follow the documented quick-start stages for preprocessing JSONL input and starting a pre-training run.

Before you start

  • Python 3.12 or newer and PyTorch 2.11.0.
  • CUDA is required for training.
  • Astrai cloned from its GitHub repository, JSONL input data, and model parameters.

Steps

  1. Clone the repository and install it with the command below.

    Documented command
    pip install -e .

  2. Create a JSON preprocessing configuration for the seq strategy, specifying the input field, sequence length and output format.

  3. Run the command below.

    Documented command
    python scripts/tools/preprocess.py data/*.jsonl -o output/ -c pretrain.json

  4. Start scripts/tools/train.py with the documented seq training settings, dataset path, model path and CUDA device configuration.

Expected outcome

A preprocessed dataset and a configured command for a distributed-data-parallel pre-training run.

Guide limitations

  • Training requires CUDA; CPU support is not documented for this workflow.
  • The listed training command is an example configuration and uses paths and parameters that must be supplied for the user's data and model.
Documentation 1 ↗
Source-based practical guide

Start an inference server and query it

Use the documented quick-start commands to launch the HTTP server and send a chat-completion request.

Before you start

  • Python 3.12 or newer and PyTorch 2.11.0.
  • A model checkpoint at the configured parameter path.
  • CUDA for the documented --device cuda server invocation.

Steps

  1. Install Astrai from the cloned repository with the command below.

    Documented command
    pip install -e .

  2. Download the example checkpoint with the command below, or provide a model at the server's parameter path.

    Documented command
    python scripts/demo/download.py

  3. Start the server with the command below.

    Documented command
    python scripts/tools/server.py --param_path ./params --device cuda

  4. Send a JSON chat-completion request to http://localhost:8000/v1/chat/completions.

Expected outcome

A locally running inference endpoint that accepts the documented chat-completion request format.

Guide limitations

  • The quick-start server command uses CUDA; CPU support is limited to components with an explicit CPU device path.
  • The example checkpoint download is separate from installing the framework.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
AstraiConfigure data preprocessing for Transformer training.The repository is public and identifies the project as Apache-2.0 licensed. The documented setup requires Python 3.12 or newer and PyTorch 2.11.0; several training and generation workflows require CUDA.
HyperposeCustomize pose-estimation architectures and datasets.Docker, Linux, macOS, Windows
Multi Task NlpDefine multiple tasks and their data and model settings in a YAML file.Linux, macOS, WindowsThe project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
ViperEkura/AstrAI · Official homepage title · retained in its source language
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