---
license: cc-by-nc-4.0
pretty_name: jepa.cpp parity fixtures
size_categories:
  - n<1K
tags:
  - jepa
  - jepa.cpp
  - ggml
  - gguf
  - parity
  - golden-references
  - regression-testing
---

# jepa.cpp parity fixtures

PyTorch golden reference dumps for [**jepa.cpp**](https://github.com/aselimc/jepa.cpp), a ggml-based C/C++ inference engine for the
JEPA family. `tests/test-parity` and `tests/test-predictor` replay these tensors through the engine and
gate per-token cosine, pooled outputs and classifier top-1/top-5 against per-family thresholds.

Generated from jepa.cpp `main` @ [`00bfd4e`](https://github.com/aselimc/jepa.cpp/commit/00bfd4e) with
`scripts/dump_reference.py --model all`, in float32 eval mode on 32 CPU threads, no autocast.

## Contents

| directory | model | files | size |
|---|---|---:|---:|
| `ref/ijepa-vith14-1k/` | I-JEPA ViT-H/14 (IN1k) | 25 | 15 MB |
| `ref/lejepa-vits16/` | LeJEPA ViT-S/16 (IN1k) | 41 | 7 MB |
| `ref/levjepa-vitl16/` | LeVJEPA ViT-L/16 (VideoMix) | 21 | 136 MB |
| `ref/lewm-pusht/` | LeWorldModel Push-T | 20 | 3 MB |
| `ref/vjepa2-ac-vitg/` | V-JEPA 2-AC ViT-g (action-conditioned world model) | 45 | 56 MB |
| `ref/vjepa2-vitg-fpc64-256/` | V-JEPA 2 ViT-g/16 (fpc64, 256) | 21 | 429 MB |
| `ref/vjepa2-vitl-fpc16-256-ssv2/` | V-JEPA 2 ViT-L/16 SSv2 classifier | 13 | 56 MB |
| `ref/vjepa2-vitl-fpc64-256/` | V-JEPA 2 ViT-L/16 (fpc64, 256) | 21 | 366 MB |
| `ref/vjepa2_1-vitb-384/` | V-JEPA 2.1 ViT-B/16 @384 | 15 | 107 MB |

222 files, 1176 MB in total.

## Layout

One directory per model, each with a `manifest.json` and one `.npy` per tensor per sample, named
`<sample>.<tensor>.npy`. All arrays are float32 C-order except `frames_u8` (uint8) and `top5_idx`
(int64). Shapes carry no batch dimension except `input`, which is stored exactly as fed to the model.

The manifest records the model id, the hyper-parameters, the preprocessing pipeline that produced
`input`, the PyTorch forward wall time per sample (`timing_s.forward_s`, the baseline for the speed
tables of the jepa.cpp docs), the frame indices sampled from each clip, and the label strings for the
classifier. Per-model tensor lists and the exact preprocessing recipe:
[**docs/fixtures.md**](https://aselimc.github.io/jepa.cpp/fixtures/).

## How jepa.cpp consumes it

```bash
git clone --recursive https://github.com/aselimc/jepa.cpp && cd jepa.cpp
cmake -S . -B build -G Ninja -DCMAKE_BUILD_TYPE=Release && cmake --build build -j

scripts/download_fixtures.sh          # this dataset -> tests/fixtures/ref, plus the media it needs
scripts/download_models.sh small      # GGUFs from https://huggingface.co/jepacpp

cmake -S . -B build && ctest --test-dir build      # the parity suites register at configure time
```

`test-parity` runs two passes per file: first the stored `input` tensor (bypassing preprocessing, so a
graph bug shows up alone), then jepa.cpp's own preprocessor on the source media (so a preprocessing
mismatch shows up separately). The second pass needs `tests/fixtures/media/`, which is **not** part of
this dataset — see below.

## Input provenance

The dumps are model outputs computed on two public research sets. Neither the source images nor the
source videos are redistributed here; only the reference activations and the decoded frame tensors the
tests replay are.

| input | source | fetched by |
|---|---|---|
| 8 images `coco_*.jpg` | [COCO val2017](https://cocodataset.org/), images subject to their original Flickr terms | tracked in the jepa.cpp git repo |
| 6 short clips | [`nateraw/kinetics-mini`](https://huggingface.co/datasets/nateraw/kinetics-mini), a small sample of Kinetics-400 | `scripts/download_fixtures.sh` |

The `input` and `frames_u8` arrays inside `ref/` are preprocessed pixels of those images and clips, kept
because the parity tests must feed the network exactly the tensor PyTorch saw. If you hold rights in any
of the underlying material and want it removed, open an issue on [github.com/aselimc/jepa.cpp](https://github.com/aselimc/jepa.cpp/issues).

## Licence

`cc-by-nc-4.0`, the most restrictive licence among the checkpoints whose outputs are stored here: the
dumps of I-JEPA ViT-H/14 (IN1k) and LeVJEPA ViT-L/16 (VideoMix) are outputs of CC BY-NC 4.0 models, the rest of MIT / Apache-2.0 ones.
Research and non-commercial use.

## Regenerating instead of downloading

```bash
scripts/download_fixtures.sh media                        # the source media
scripts/download_models.sh --convert all                  # the source checkpoints
.venv/bin/python scripts/dump_reference.py --model all    # ~1 min on 32 cores
```

## Links

- Code: <https://github.com/aselimc/jepa.cpp>
- Documentation: <https://aselimc.github.io/jepa.cpp/>
- GGUF models: <https://huggingface.co/jepacpp>