facebook / tribev2

TRIBE v2 is a deep multimodal brain encoding model that predicts fMRI brain responses to naturalistic stimuli (video, audio, text). It combines state-of-the-art feature extractors — LLaMA 3.2 (text), V-JEPA2 (video), and Wav2Vec-BERT (audio) — into a unified Transformer architecture that maps multimodal representations onto the cortical surface.

Quick start

Load a pretrained model from HuggingFace and predict brain responses to a video:

from tribev2 import TribeModel

model = TribeModel.from_pretrained("facebook/tribev2", cache_folder="./cache")

df = model.get_events_dataframe(video_path="path/to/video.mp4")
preds, segments = model.predict(events=df)
print(preds.shape)  # (n_timesteps, n_vertices)

Predictions are for the "average" subject (see paper for details) and live on the fsaverage5 cortical mesh (~20k vertices). You can also pass text_path or audio_path to model.get_events_dataframe — text is automatically converted to speech and transcribed to obtain word-level timings.

For a full walkthrough with brain visualizations, see the Colab demo notebook .

Installation

Basic (inference only):

pip install -e .

With brain visualization :

pip install -e ".[plotting]"

With training dependencies (PyTorch Lightning, W&B, etc.):

pip install -e ".[training]"

Training a model from scratch

1. Set environment variables

Configure data/output paths and Slurm partition (or edit tribev2/grids/defaults.py directly):

export DATAPATH="/path/to/studies"
export SAVEPATH="/path/to/output"
export SLURM_PARTITION="your_partition"

2. Authenticate with HuggingFace

The text encoder requires access to the gated LLaMA 3.2-3B model:

huggingface-cli login

Create a read access token and paste it when prompted.

3. Run training

Local test run:

python -m tribev2.grids.test_run

Grid search on Slurm:

python -m tribev2.grids.run_cortical
python -m tribev2.grids.run_subcortical

Project structure

tribev2/
├── main.py              # Experiment pipeline: Data, TribeExperiment
├── model.py             # FmriEncoder: Transformer-based multimodal→fMRI model
├── pl_module.py         # PyTorch Lightning training module
├── demo_utils.py        # TribeModel and helpers for inference from text/audio/video
├── eventstransforms.py  # Custom event transforms (word extraction, chunking, …)
├── utils.py             # Multi-study loading, splitting, subject weighting
├── utils_fmri.py        # Surface projection (MNI / fsaverage) and ROI analysis
├── grids/
│   ├── defaults.py      # Full default experiment configuration
│   └── test_run.py      # Quick local test entry point
├── plotting/            # Brain visualization (PyVista & Nilearn backends)
└── studies/             # Dataset definitions (Algonauts2025, Lahner2024, …)

Contributing to open science

If you use this software, please share your results with the broader research community using the following citation:

@article{dAscoli2026TribeV2,
  title={A foundation model of vision, audition, and language for in-silico neuroscience},
  author={d'Ascoli, St{\'e}phane and Rapin, J{\'e}r{\'e}my and Benchetrit, Yohann and Brookes, Teon and Begany, Katelyn and Raugel, Jos{\'e}phine and Banville, Hubert and King, Jean-R{\'e}mi},
  year={2026}
}

License

This project is licensed under CC-BY-NC-4.0. See LICENSE for details.

Contributing

See CONTRIBUTING.md for how to get involved.