| --- |
| license: mit |
| tags: |
| - jepa |
| - world-model |
| - gguf |
| - ggml |
| --- |
| |
| # jepa.cpp umaze parity artifacts |
|
|
| Test artifacts for [jepa.cpp](https://github.com/agentic-learning-ai-lab/jepa.cpp), an inference runtime for action-conditioned JEPA world models on ggml. jepa.cpp's parity tests and its rollout-latency benchmark (`bench/rollout_metric.sh`) read these files. |
|
|
| | File | What it is | |
| |---|---| |
| | `umaze_dino_channel_straight.v2.f16.gguf` | The world model with F16 weights, GGUF schema v2 (`docs/gguf-schema.md`) | |
| | `umaze_dino_channel_straight.v2.f32.gguf` | The same model with F32 weights | |
| | `umaze_dino_channel_straight.v2.bf16.gguf` | The same model with BF16 weights | |
| | `umaze_ref_gn.npz` | Reference activations from the PyTorch model (`tools/dump_reference.py`), including the CEM and Gauss-Newton planner traces | |
| | `umaze_stats.json` | The action and proprio normalization stats the GGUFs embed (`tools/dataset_stats.py`) | |
|
|
| The model is a jepa-agent `dino_wm` world model (`dino_channel`: DINOv2 ViT-S/14 encoder, channel projector, ViT predictor), trained with a temporal-straightening loss on PointMaze umaze. It is a small, self-trained test model for checking numerical parity. It is not a reference checkpoint for planning results. |
|
|
| SHA-256: |
|
|
| ``` |
| acbef2a9787c861c4a795cbdaa05cacfbb61ee0593d2ccf0b1ffc970f8a4201b umaze_dino_channel_straight.v2.f16.gguf |
| 6a9934d4e302060dae0cc021fe301bb2a3af0d1dd2c5337c36bc7fd3e2648f1f umaze_dino_channel_straight.v2.f32.gguf |
| 3d9c750c4b6310871bfb906000120e0550cca81256c85c6a943acdecfc063d29 umaze_dino_channel_straight.v2.bf16.gguf |
| 94dfe3da17902e7088f0e3b9e75db786beb8e25ac793ecabc865a3adce61df5f umaze_ref_gn.npz |
| bf07ef1ab499829bb591eb052f003013f779933e2ab86acc34cced2cd72a014d umaze_stats.json |
| ``` |
|
|
