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Hpt

Hpt is an open-source Python implementation of multimodal vision-language models and inference code. It accepts visual and textual inputs, with repository instructions for trying an image-prompt demo and reproducing benchmark evaluations.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Hpt — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use Hpt when you want repository-based inference for image-and-text prompts or want to launch benchmark evaluations with HPT configurations.

Who it is for

  • Developers prototyping image question-answering applications with text prompts.
  • Researchers or engineers who want to run multimodal model evaluations.
  • Teams exploring local inference with the HPT model releases.

Documented uses

  • Ask a model a text question about an image through the demo.
  • Run an evaluation using a specified dataset and model configuration.
  • Compare the documented HPT model variants and their listed pretrained components.
Before you choose

Requirements & limits

The repository is public and identifies the project as Apache-2.0 licensed. Its documented demo requires installing the code and preparing model weights locally.

  • The documented workflows require a local Python installation, model weights and configuration.
  • The Edge demo example uses HPT 1.5 Edge; evaluation instructions specify eight processes per node.
  • HPT 1.5 Edge is described as tailored to edge devices and smaller than 5B parameters.
  • HPT Air has no moderation mechanism and its results are not guaranteed, according to the repository.
  • The repository notes that included components from other sources may have their own license terms.

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

Set up and try the HPT 1.5 Edge image demo

Install the repository, prepare the HPT 1.5 Edge weights and launch its documented image-and-text demo.

Before you start

  • Python environment suitable for the repository requirements
  • Git LFS
  • A local path for the downloaded HPT 1.5 Edge weights
  • An image path, text prompt and model configuration

Steps

  1. Install dependencies with the command below and install the package with the command below.

    Documented command
    pip install -r requirements.txt
    Documented command
    pip install -e .

  2. Run the command below and clone https://huggingface.co/HyperGAI/HPT1_5-Edge to a local path.

    Documented command
    git lfs install

  3. Set global_model_path to that path in the model configuration file.

  4. Launch the command below, using hpt-edge-1-5 as the documented Edge configuration example.

    Documented command
    python demo/demo.py --image_path [Image] --text [Text] --model [Config]

Expected outcome

The repository’s demo runs with an image and text prompt using the selected model configuration.

Guide limitations

  • The README describes the workflow but does not specify hardware requirements.
  • The README warns that HPT Air has no moderation mechanism and provides no guarantees on results.
  • Other-source code and models may have separate license terms.
Documentation 1 ↗
Source-based practical guide

Launch a documented benchmark evaluation

Start an evaluation using the repository’s dataset and model configuration arguments.

Before you start

  • The repository and model setup
  • A dataset identifier and model configuration
  • A torchrun environment supporting the documented eight processes per node

Steps

  1. Choose the dataset and model configuration values.

  2. Run torchrun --nproc-per-node=8 run.py --data [Dataset] --model [Config].

  3. For the documented HPT 1.5 Edge example, use MMMU_DEV_VAL and hpt-edge-1-5.

Expected outcome

The evaluation runner is launched for the selected dataset and model configuration.

Guide limitations

  • The README does not detail dataset acquisition or evaluation result interpretation.
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
HptAsk a model a text question about an image through the demo.LinuxThe repository is public and identifies the project as Apache-2.0 licensed. Its documented demo requires installing the code and preparing model weights locally.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
HyperGAI/HPT · Official homepage title · retained in its source language
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