--- title: "TRIBE v2" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/b3d7d807-a284-4036-86c7-f91738a760a2" updated: "2026-11-13" --- # TRIBE v2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Estimates how the brain responds, as seen in fMRI, to video, audio and text, using a multimodal Transformer that maps onto the cortical surface. For neuroscience and AI researchers. TRIBE v2 is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by facebook ## About ### What it is TRIBE v2 is a brain encoding model. You give it a video, an audio clip or a text, and it estimates the fMRI response a brain would show, laid out over the cortical surface. It joins three feature extractors, LLaMA 3.2 for text, V-JEPA2 for video and Wav2Vec-BERT for audio, inside one Transformer. Its title calls it a base model across sight, hearing and language, meant for neuroscience experiments run on a computer. ### Who it suits Researchers who study how the brain handles sight, sound and language, and who are comfortable with Python and neuroimaging data. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Neuroscience | Brain imaging researchers | Predict fMRI responses to videos, audio or text | Compare predicted activity for two video clips | | Cognitive science | Language researchers | Study how spoken words map onto cortical responses | Predict responses to a recorded sentence | | AI research | Multimodal model researchers | Study a Transformer that maps features onto the cortex | Train a new model on the card's study datasets with Slurm | You can skip it if you want a chat, writing or image model, or if you need commercial use, since the license is non-commercial. ### Where to use it It runs in Python 3.10 or newer, and the card links a Colab demo notebook with brain visualizations. - A Python environment on Windows, macOS or Linux - The card's Colab demo notebook - A Slurm cluster, for the grid-search training runs | Use case | Who | What they get back | | --- | --- | --- | | Brain response prediction | Neuroscientists | An array of predictions shaped by timesteps and vertices | | Brain visualization | Researchers | Plots of predicted activity, with the plotting extra installed | | Training from scratch | ML researchers | A model trained on the study datasets the card names | ### How to set it up Install the code from the project's repository, then load the pretrained weights from Hugging Face. Inference needs only the basic install. - Python 3.10 or newer - The tribev2 code from the facebookresearch project - A Hugging Face account that can open the restricted LLaMA 3.2-3B model, because the text side of the model depends on it 1. Get the tribev2 code and open its folder. 2. Install it with pip in editable mode. 3. Add the plotting or training extra only if you need it. 4. Load the pretrained model in your own script, as shown in the next section. ```bash pip install -e . pip install -e ".[plotting]" pip install -e ".[training]" ``` ```bash pip install -U huggingface_hub hf download facebook/tribev2 ``` ### How to use it Load the pretrained model, build an events table from a video, and ask for predictions. You can pass a text or audio path to the same call, and text is turned into speech and transcribed to get word timings. ```python 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) ``` To train from scratch, set the data and output paths, sign in to Hugging Face, then run the test or the grid searches. ```bash export DATAPATH="/path/to/studies" export SAVEPATH="/path/to/output" export SLURM_PARTITION="your_partition" huggingface-cli login python -m tribev2.grids.test_run python -m tribev2.grids.run_cortical python -m tribev2.grids.run_subcortical ``` | Tool | What it does | | --- | --- | | TribeModel.from\_pretrained | Loads the pretrained model from Hugging Face into a cache folder | | get\_events\_dataframe | Builds the events table from a video, text or audio path | | model.predict | Returns the brain-response predictions and their segments | | pip install with the plotting extra | Adds brain visualization | | pip install with the training extra | Adds training dependencies such as PyTorch Lightning and W\&B | ### Example Load the model, point get\_events\_dataframe at a video file, and call predict. Printing the shape of the result gives the number of timesteps by the number of vertices on the cortical mesh. ### For newcomers and experts fMRI measures brain activity, and this model guesses what that activity would look like while someone watches or hears something. A first small try is the card's Colab demo notebook, which walks through the steps with brain plots. For experts, the predictions are for an average subject and sit on the fsaverage5 cortical mesh of about 20k vertices. The model fuses LLaMA 3.2, V-JEPA2 and Wav2Vec-BERT features in a unified Transformer, and the project code covers training with PyTorch Lightning and surface projection. The trade is group-level output, not predictions for one person, under a non-commercial license. Video, audio or text goes in → Three feature extractors encode it → A unified Transformer fuses them → Predictions land on the cortical mesh ### Good to know Read these limits before you plan a study. - Average subject: predictions describe an average subject, as the card explains. - Mesh: the output lives on fsaverage5, about 20k vertices. - Restricted model: the text side depends on LLaMA 3.2-3B, which needs approved access. - Text input: text is converted to speech and transcribed to get word timings. - License: CC-BY-NC-4.0, which is non-commercial. - Cost to run: none named. Training names a Slurm partition and optional W\&B, and no paid API is mentioned. - Data it reaches: the video, audio or text files you give it, plus the weights downloaded into a cache folder. - Not for: commercial use, or predictions for an individual subject. ## README ````markdown # TRIBE v2 Estimates how the brain responds, as seen in fMRI, to video, audio and text, using a multimodal Transformer that maps onto the cortical surface. For neuroscience and AI researchers. Made by facebook on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: TRIBE v2 is a brain encoding model. You give it a video, an audio clip or a text, and it estimates the fMRI response a brain would show, laid out over the cortical surface. It joins three feature extractors, LLaMA 3.2 for text, V-JEPA2 for video and Wav2Vec-BERT for audio, inside one Transformer. Its title calls it a base model across sight, hearing and language, meant for neuroscience experiments run on a computer. Who it suits: Researchers who study how the brain handles sight, sound and language, and who are comfortable with Python and neuroimaging data. ```table Industry | Who uses it | How they use it | Example Neuroscience | Brain imaging researchers | Predict fMRI responses to videos, audio or text | Compare predicted activity for two video clips Cognitive science | Language researchers | Study how spoken words map onto cortical responses | Predict responses to a recorded sentence AI research | Multimodal model researchers | Study a Transformer that maps features onto the cortex | Train a new model on the card's study datasets with Slurm ``` You can skip it if you want a chat, writing or image model, or if you need commercial use, since the license is non-commercial. Where to use it: It runs in Python 3.10 or newer, and the card links a Colab demo notebook with brain visualizations. - A Python environment on Windows, macOS or Linux - The card's Colab demo notebook - A Slurm cluster, for the grid-search training runs ```table Use case | Who | What they get back Brain response prediction | Neuroscientists | An array of predictions shaped by timesteps and vertices Brain visualization | Researchers | Plots of predicted activity, with the plotting extra installed Training from scratch | ML researchers | A model trained on the study datasets the card names ``` How to set it up: Install the code from the project's repository, then load the pretrained weights from Hugging Face. Inference needs only the basic install. - Python 3.10 or newer - The tribev2 code from the facebookresearch project - A Hugging Face account that can open the restricted LLaMA 3.2-3B model, because the text side of the model depends on it 1. Get the tribev2 code and open its folder. 2. Install it with pip in editable mode. 3. Add the plotting or training extra only if you need it. 4. Load the pretrained model in your own script, as shown in the next section. ```bash pip install -e . pip install -e ".[plotting]" pip install -e ".[training]" ``` ```bash pip install -U huggingface_hub hf download facebook/tribev2 ``` How to use it: Load the pretrained model, build an events table from a video, and ask for predictions. You can pass a text or audio path to the same call, and text is turned into speech and transcribed to get word timings. ```python 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) ``` To train from scratch, set the data and output paths, sign in to Hugging Face, then run the test or the grid searches. ```bash export DATAPATH="/path/to/studies" export SAVEPATH="/path/to/output" export SLURM_PARTITION="your_partition" huggingface-cli login python -m tribev2.grids.test_run python -m tribev2.grids.run_cortical python -m tribev2.grids.run_subcortical ``` ```table Tool | What it does TribeModel.from_pretrained | Loads the pretrained model from Hugging Face into a cache folder get_events_dataframe | Builds the events table from a video, text or audio path model.predict | Returns the brain-response predictions and their segments pip install with the plotting extra | Adds brain visualization pip install with the training extra | Adds training dependencies such as PyTorch Lightning and W&B ``` Example: Load the model, point get_events_dataframe at a video file, and call predict. Printing the shape of the result gives the number of timesteps by the number of vertices on the cortical mesh. For newcomers and experts: fMRI measures brain activity, and this model guesses what that activity would look like while someone watches or hears something. A first small try is the card's Colab demo notebook, which walks through the steps with brain plots. For experts, the predictions are for an average subject and sit on the fsaverage5 cortical mesh of about 20k vertices. The model fuses LLaMA 3.2, V-JEPA2 and Wav2Vec-BERT features in a unified Transformer, and the project code covers training with PyTorch Lightning and surface projection. The trade is group-level output, not predictions for one person, under a non-commercial license. ```flow Video, audio or text goes in Three feature extractors encode it A unified Transformer fuses them Predictions land on the cortical mesh ``` Good to know: Read these limits before you plan a study. - Average subject: predictions describe an average subject, as the card explains. - Mesh: the output lives on fsaverage5, about 20k vertices. - Restricted model: the text side depends on LLaMA 3.2-3B, which needs approved access. - Text input: text is converted to speech and transcribed to get word timings. - License: CC-BY-NC-4.0, which is non-commercial. - Cost to run: none named. Training names a Slurm partition and optional W&B, and no paid API is mentioned. - Data it reaches: the video, audio or text files you give it, plus the weights downloaded into a cache folder. - Not for: commercial use, or predictions for an individual subject. ## On Hugging Face, 13 November 2026 - Downloads in the last 30 days: 25,771 - Likes: 760 - License: cc-by-nc-4.0 - Quantized versions: 3 - Fine-tunes: 3 ## Links - Model: https://huggingface.co/facebook/tribev2 - Files: https://huggingface.co/facebook/tribev2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download facebook/tribev2 ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [facebook/tribev2 on Hugging Face](https://huggingface.co/facebook/tribev2) ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Stéphane d’Ascoli · 2026-03-26](https://bsky.app/profile/sdascoli.bsky.social/post/3mhxpyw7l222n): 🚨 We're very happy to introduce TRIBE v2: a foundation model of the brain's responses to sight, sound \& language. 📄 Paper: ai.meta.com/research/pub... ▶️ Demo: aidemos.atmeta.com/tribev2/ 💻 Code: github.com/facebookrese... 🤗 Model: huggingface.co/facebook/tri... - [Bluesky · Teon ⁂ · 2026-04-10](https://bsky.app/profile/teonbrooks.com/post/3mj663y5yc22p): Some related links: 📄 Paper: ai.meta.com/research/pub... ▶️ Demo: aidemos.atmeta.com/tribev2/ 💻 Code: github.com/facebookrese... 🤗 Model: huggingface.co/facebook/tri... - [Bluesky · デイリーHuggingFaceトレンド · 2026-04-01](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3migjlmgl2t2i): 今日のHuggingFaceトレンド facebook/tribev2 TRIBE v2は、視覚、聴覚、言語情報を統合し、脳のfMRI応答を予測する深層学習モデルです。 動画、音声、テキストなどの自然な刺激に対する脳活動を解析するための基盤モデルとして機能し、in-silico neuroscience研究を支援します。 Hugging Faceから事前学習済みモデルを読み込み、簡単に脳応答予測を実行できます。 - [Bluesky · デイリーHuggingFaceトレンド · 2026-03-30](https://bsky.app/profile/huggingfacetrends.bsky.social/post/3mibj4ubuy42l): 今日のHuggingFaceトレンド facebook/tribev2 TRIBE v2は、視覚、聴覚、言語情報を統合し、脳のfMRI応答を予測する深層学習モデルです。 動画、音声、テキストなどの自然な刺激に対する脳活動を解析するための基盤モデルとして機能し、神経科学研究に貢献することを目的としています。 Hugging Faceから事前学習済みモデルを読み込み、簡単に脳応答予測を実行できます。 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-03-27 - First published: 2026-03-24 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open facebook/tribev2 on Hugging Face](https://huggingface.co/facebook/tribev2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/b3d7d807-a284-4036-86c7-f91738a760a2) - [Its page on Mindket](https://mindket.com/p/b3d7d807-a284-4036-86c7-f91738a760a2)