---
license: cc-by-nc-4.0
language:
  - en
tags:
  - neuroscience
  - fmri
  - brain-encoding
  - multimodal
  - rust
  - safetensors
base_model: facebook/tribev2
---

<div align="center">

# TRIBE v2 — Rust Edition

**A Foundation Model of Vision, Audition, and Language for In-Silico Neuroscience**

[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
[![Rust](https://img.shields.io/badge/inference-Rust-orange.svg)](https://www.rust-lang.org/)
[![Base model](https://img.shields.io/badge/base%20model-facebook%2Ftribev2-blue.svg)](https://huggingface.co/facebook/tribev2)

📄 [Paper](https://ai.meta.com/research/publications/a-foundation-model-of-vision-audition-and-language-for-in-silico-neuroscience/) ·
🤗 [Original weights](https://huggingface.co/facebook/tribev2) ·
🦀 [Rust implementation](https://github.com/eugenehp/tribev2-rs)

</div>

## Overview

This directory contains the **same pretrained weights** as [`facebook/tribev2`](https://huggingface.co/facebook/tribev2), converted to the [safetensors](https://github.com/huggingface/safetensors) format for use with the pure-Rust inference engine **tribev2-rs**.

No fine-tuning, quantisation, or architectural changes have been made.  
The model is **bit-for-bit equivalent** to the original Python checkpoint — every layer has been independently verified for numerical parity.

## Model description

TRIBE v2 is a deep multimodal brain encoding model that predicts fMRI responses to naturalistic stimuli (video, audio, text).  
It combines three state-of-the-art feature extractors:

| Modality | Extractor | Dim |
|----------|-----------|----:|
| Text | LLaMA 3.2-3B | 3 072 |
| Audio | Wav2Vec-BERT 2.0 | 1 024 |
| Video | V-JEPA2 ViT-G | 1 408 |

These multimodal representations are projected and fused by a **Transformer encoder** (8 layers, 1 152-d, ScaleNorm, Rotary PE) that outputs predicted BOLD responses on the **fsaverage5** cortical mesh (~20 484 vertices).

Full architectural details are in the [paper](https://ai.meta.com/research/publications/a-foundation-model-of-vision-audition-and-language-for-in-silico-neuroscience/) and in the [`facebook/tribev2`](https://huggingface.co/facebook/tribev2) model card.

## Files

| File | Description |
|------|-------------|
| `model.safetensors` | Pretrained weights (safetensors, converted from the original PyTorch Lightning checkpoint) |
| `config.yaml` | Model hyper-parameters (hidden dim, depth, heads, modalities, …) |
| `build_args.json` | Feature-extractor build arguments used at training time |
| `fsaverage5/` | FreeSurfer fsaverage5 cortical mesh files (`.pial`, `.inflated`, `.sulc`, `.curv`) for brain visualisation |

## Encoding Input Data into Feature Tensors

The model consumes three feature tensors, one per modality, each shaped
`[1, n_layers × dim, T]` where `T` is the number of timesteps at 2 Hz
(one vector per 0.5 s).

| Modality | Extractor | Layer groups | Dim / group | Total dim |
|----------|-----------|-------------:|------------:|----------:|
| Text | LLaMA-3.2-3B | 2 | 3 072 | **6 144** |
| Audio | Wav2Vec-BERT 2.0 | 2 | 1 024 | **2 048** |
| Video | V-JEPA2 ViT-G | 2 | 1 408 | **2 816** |

---

### Text — string → tensor

Text feature extraction runs entirely in Rust via
[llama-cpp-rs](https://github.com/eugenehp/llama-cpp-rs).
Download a GGUF quantisation of
[LLaMA-3.2-3B](https://huggingface.co/meta-llama/Llama-3.2-3B) first.

#### Option A — raw string (uniform timing)

```rust
use tribev2::features::{LlamaFeatureConfig, extract_llama_features, resample_features};
use tribev2::tensor::Tensor;

let config = LlamaFeatureConfig {
    model_path: "llama-3.2-3b.gguf".into(),
    layer_positions: vec![0.5, 0.75, 1.0], // → layers 13, 20, 27 of 28
    n_layers: 28,   // LLaMA-3.2-3B
    n_ctx: 2048,
    frequency: 2.0, // Hz
};

let feats = extract_llama_features(&config, "The quick brown fox", false)?;
// feats.data: [3, 3072, n_tokens]

// Resample to exactly 100 TRs and reshape to [1, 6144, 100]
let feats = resample_features(&feats, 100);
let text_tensor = Tensor::from_vec(
    feats.data.data,
    vec![1, feats.n_layers * feats.feature_dim, feats.n_timesteps],
);
```

#### Option B — word-timed events (precise temporal alignment)

```rust
use tribev2::features::{LlamaFeatureConfig, extract_llama_features_timed};

let words = vec![
    ("The".into(),   0.0_f64),
    ("quick".into(), 0.3),
    ("brown".into(), 0.55),
    ("fox".into(),   0.82),
];
let total_duration = 2.0; // seconds

let feats = extract_llama_features_timed(&config, &words, total_duration, false)?;
// feats.data: [3, 3072, ceil(2.0 * 2.0) = 4]
```

#### Option C — full pipeline from a text file

```rust
use tribev2::events::build_events_from_media;
use tribev2::features::{LlamaFeatureConfig, extract_llama_features_timed};

let events = build_events_from_media(
    Some("transcript.txt"),  // text_path
    None,                    // audio_path
    None,                    // video_path
    "/tmp/cache",            // cache_dir
    "english",
    256,                     // max_context_len
)?;

let words    = events.words_timed(); // Vec<(String, f64)>
let duration = events.duration();

let feats = extract_llama_features_timed(&config, &words, duration, false)?;
```

---

### Audio — MP3 / WAV / FLAC → tensors

Audio features come from two sources:

1. **Text channel** — transcribe the audio → word timestamps → LLaMA
   (full Rust pipeline, no Python needed)
2. **Audio channel** — Wav2Vec-BERT 2.0 activations
   (pre-extract in Python; see [Pre-extracted features](#pre-extracted-features-python))

#### Transcribe audio → text features (Rust)

Requires `whisperx` or `whisper` (`pip install whisperx`) and `ffmpeg`.

```rust
use tribev2::events::{transcribe_audio, build_events_from_media};
use tribev2::features::{LlamaFeatureConfig, extract_llama_features_timed};

// Option A: transcribe directly
let events = transcribe_audio("interview.mp3", "english", 0.0)?;
let words   = events.words_timed();
let feats   = extract_llama_features_timed(&config, &words, events.duration(), false)?;

// Option B: full pipeline (also attaches Audio events to the list)
let events = build_events_from_media(
    None,
    Some("interview.mp3"), // audio_path
    None,
    "/tmp/cache", "english", 256,
)?;
let feats = extract_llama_features_timed(
    &config, &events.words_timed(), events.duration(), false,
)?;
```

> **Transcript caching** — `transcribe_audio` saves the whisperX JSON next to
> the audio file (`interview.json`) and reloads it on subsequent calls,
> avoiding repeated transcription.

---

### Video — MP4 → tensors

Video features come from two sources:

1. **Text channel** — extract audio → transcribe → LLaMA (Rust)
2. **Video channel** — V-JEPA2 ViT-G activations
   (pre-extract in Python; see [Pre-extracted features](#pre-extracted-features-python))

#### MP4 file

```rust
use tribev2::events::build_events_from_media;

let events = build_events_from_media(
    None, None,
    Some("clip.mp4"),  // video_path
    "/tmp/cache", "english", 256,
)?;
let feats = extract_llama_features_timed(
    &config, &events.words_timed(), events.duration(), false,
)?;
```

#### Sequence of images (PNG / JPG / WEBP / …)

Convert each frame (or the whole sequence) to an MP4 first, then use the video path above.

```rust
use tribev2::events::create_video_from_image;

// Single static image held for N seconds
let mp4 = create_video_from_image("frame.png", 5.0, 24, "/tmp/cache")?;

// Image sequence → MP4 via ffmpeg (shell out)
std::process::Command::new("ffmpeg")
    .args(["-y", "-framerate", "24"])
    .args(["-pattern_type", "glob", "-i", "frames/*.png"])
    .args(["-c:v", "libx264", "-pix_fmt", "yuv420p"])
    .arg("/tmp/cache/sequence.mp4")
    .status()?;

let events = build_events_from_media(
    None, None, Some("/tmp/cache/sequence.mp4"),
    "/tmp/cache", "english", 256,
)?;
```

---

### Pre-extracted features (Python)

Wav2Vec-BERT and V-JEPA2 have no Rust implementation yet.
Extract them in Python and save as raw `float32` binary files:

```python
import numpy as np
from tribev2 import TribeModel

model = TribeModel.from_pretrained("facebook/tribev2", cache_folder="./cache")
df    = model.get_events_dataframe(video_path="clip.mp4")

# Extract features: dict {modality: np.ndarray [n_layers, dim, T]}
features = model.extract_features(df)

# Save each modality as a flat float32 binary
for modality, arr in features.items():
    arr.astype(np.float32).flatten().tofile(f"{modality}_features.bin")
    print(f"{modality}: {arr.shape}")  # e.g. audio: (2, 1024, 200)
```

Load them in Rust:

```rust
use tribev2::tensor::Tensor;

fn load_features(path: &str, n_layers: usize, dim: usize, t: usize)
    -> anyhow::Result<Tensor>
{
    let bytes = std::fs::read(path)?;
    let data: Vec<f32> = bytes.chunks_exact(4)
        .map(|b| f32::from_le_bytes([b[0], b[1], b[2], b[3]]))
        .collect();
    Ok(Tensor::from_vec(data, vec![1, n_layers * dim, t]))
}

// audio: 2 layer groups × 1024 dim × 200 timesteps → [1, 2048, 200]
let audio = load_features("audio_features.bin", 2, 1024, 200)?;
// video: 2 layer groups × 1408 dim × 200 timesteps → [1, 2816, 200]
let video = load_features("video_features.bin", 2, 1408, 200)?;
```

---

### Putting it all together

```rust
use std::collections::BTreeMap;
use tribev2::config::TribeV2Config;
use tribev2::events::build_events_from_media;
use tribev2::features::{LlamaFeatureConfig, extract_llama_features_timed, resample_features};
use tribev2::model::tribe::TribeV2;
use tribev2::tensor::Tensor;
use tribev2::weights::{WeightMap, load_weights};

// Load model
let config: TribeV2Config = serde_yaml::from_str(
    &std::fs::read_to_string("data/config.yaml")?
)?;
let mut model = TribeV2::new(
    tribev2::ModelBuildArgs::from_json("data/build_args.json")?.to_modality_dims(),
    20484, 100, &config.brain_model_config,
);
load_weights(
    &mut WeightMap::from_safetensors("data/model.safetensors")?,
    &mut model,
)?;

// 1. Build events from a video file (transcribes audio automatically)
let events = build_events_from_media(
    None, None, Some("clip.mp4"),
    "/tmp/cache", "english", 256,
)?;
let n_trs = 100;

// 2. Text features via LLaMA (Rust)
let llama_cfg = LlamaFeatureConfig {
    model_path: "llama-3.2-3b.gguf".into(),
    ..Default::default()
};
let text_raw = extract_llama_features_timed(
    &llama_cfg, &events.words_timed(), events.duration(), false,
)?;
let text_raw = resample_features(&text_raw, n_trs);
let text = Tensor::from_vec(
    text_raw.data.data,
    vec![1, text_raw.n_layers * text_raw.feature_dim, n_trs],
);

// 3. Audio + video features pre-extracted in Python and saved as .bin
let audio = load_features("audio_features.bin", 2, 1024, n_trs)?;
let video = load_features("video_features.bin", 2, 1408, n_trs)?;

// 4. Run inference → [1, 20484, 100] predicted BOLD on fsaverage5
let mut features = BTreeMap::new();
features.insert("text".into(),  text);
features.insert("audio".into(), audio);
features.insert("video".into(), video);

let output = model.forward(&features, None, true);
```

## Rust usage

```rust
use std::collections::BTreeMap;
use tribev2::model::tribe::TribeV2;
use tribev2::tensor::Tensor;

// Load model from this data directory
let model = TribeV2::from_pretrained(
    "data/config.yaml",
    "data/model.safetensors",
    Some("data/build_args.json"),
).unwrap();

// Build multi-modal feature tensors [1, dim, T]
let mut features = BTreeMap::new();
features.insert("text".to_string(),  Tensor::zeros(&[1, 6144, 100]));
features.insert("audio".to_string(), Tensor::zeros(&[1, 2048, 100]));
features.insert("video".to_string(), Tensor::zeros(&[1, 2816, 100]));

// Forward pass → [1, 20484, 100]
let output = model.forward(&features, None, true);
println!("{:?}", output.shape()); // [1, 20484, 100]
```

See the [tribev2-rs README](https://github.com/eugenehp/tribev2-rs) for the full CLI, feature flags, benchmarks, and brain-visualisation API.

## Converting weights from the original checkpoint

```bash
# 1. Download the original checkpoint from HuggingFace
cargo run --bin tribev2-download --features hf-download -- --repo facebook/tribev2

# 2. Convert to safetensors (requires Python ≥ 3.9, torch, safetensors)
python3 scripts/convert_checkpoint.py weights/best.ckpt data/model.safetensors
# → data/model.safetensors + data/build_args.json
```

## Pretrained model parameters

| Parameter | Value |
|-----------|-------|
| Hidden dim | 1 152 |
| Encoder depth | 8 |
| Attention heads | 8 |
| FF multiplier | 4× |
| Norm | ScaleNorm |
| Position encoding | Rotary (dim = 72) |
| Low-rank head | 2 048 |
| Subjects (released) | 1 (average subject) |
| Output surface | fsaverage5 (20 484 vertices) |
| Output timesteps | 100 TRs |

## Citation

If you use these weights or the Rust inference engine, please cite the original paper:

```bibtex
@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

The **model weights** (all files in this directory) are released under the
[Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/) license,
identical to the original [`facebook/tribev2`](https://huggingface.co/facebook/tribev2) release.

> You are free to share and adapt the weights for **non-commercial** purposes,  
> provided you give appropriate credit and indicate if changes were made.  
> **Commercial use is not permitted.**

The Rust source code of **tribev2-rs** is separately licensed under Apache-2.0.