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Gpt4Roi

GPT4RoI is a Python research project for instruction-tuning a large language model to understand selected regions in images. Its repository includes code for training and inference, examples of single- and multiple-region understanding, and a GPT4RoI-7B delta that is combined with original LLaMA-7B weights.

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Independent OpenViST project profile, based on linked sources.

Gpt4Roi — 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 GPT4RoI when researching region-focused image understanding and when prepared to work with its Python code, model-weight dependencies and dataset setup.

Who it is for

  • Researchers studying region-grounded multimodal language models.
  • Developers examining visual question answering about selected image regions.

Documented uses

  • Explore language-model responses grounded in a selected image region.
  • Study single- and multiple-region image understanding.
  • Prepare the documented GPT4RoI-7B weights by combining the project delta with original LLaMA-7B weights.
  • Work with the listed vision-language training datasets.
Before you choose

Requirements & limits

  • The repository provides training and inference code, but its data preparation notes flag organization issues, particularly for VCR.
  • The GPT4RoI-7B release is a delta, not standalone weights; the README requires original LLaMA-7B weights as the base.
  • Installation instructions specify Python 3.10, PyTorch with CUDA 11.7, FlashAttention and MMCV 1.4.7.
  • The documented training setup used eight A100 GPUs.
  • A demo URL is listed, but the repository evidence does not confirm its current operation.

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

Install the GPT4RoI environment and dependencies

Prepare the documented Python environment and install the project dependencies.

Before you start

  • Conda and pip
  • CUDA toolkit and nvcc; the README says the nvcc version should match the CUDA toolkit version reported by PyTorch
  • The mmcv-1.4.7 source directory

Steps

  1. Clone the repository and enter its directory: the command below then cd gpt4roi.

    Documented command
    git clone https://github.com/jshilong/gpt4roi.git

  2. Create and activate the Python 3.10 environment, upgrade pip, install setuptools_scm and install the project in editable mode: conda create -n gpt4roi python=3.10 -y, conda activate gpt4roi, the command below, the command below, and the command below.

    Documented command
    pip install --upgrade pip
    Documented command
    pip install setuptools_scm
    Documented command
    pip install --no-cache-dir -e .

  3. Install PyTorch, torchvision, torchaudio and PyTorch CUDA 11.7 from the PyTorch and NVIDIA Conda channels using the README’s command: conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia.

  4. Install flash-attn with the command below followed by the command below.

    Documented command
    pip install ninja
    Documented command
    pip install flash-attn --no-build-isolation

  5. From the mmcv-1.4.7 source directory, install with ops enabled: MMCV_WITH_OPS=1 pip install -e .. The README directs checking that nvcc -V matches torch.version.cuda first.

Expected outcome

The README’s installation steps configure the GPT4RoI Python environment and install its listed dependencies.

Guide limitations

  • The README does not specify how to obtain the mmcv-1.4.7 source directory.
  • Installation alone does not configure model weights or describe inference execution.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
18 October 2026
Original website title
jshilong/GPT4RoI · Official homepage title · retained in its source language
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