GR00T-N1.5 — LIBERO object (GGUF for vla.cpp)

GGUF conversion of a LIBERO-object finetune of nvidia/GR00T-N1.5-3B for inference with vla.cpp, a lightweight C++ inference engine for Vision-Language-Action models built on top of llama.cpp.

GR00T-N1.5 pairs an Eagle-2 vision-language backbone with a flow-matching (DiT) action head, replayed in 16-step action chunks with min/max normalisation. The vision tower is baked into the combined GGUF, so no separate mmproj file is needed. The text tokenizer (lerobot/eagle2hg-processor-groot-n1p5) is pulled from the Hub by the client, so it is not bundled in this repo.

Files

File	Size	Description
`gr00tn1d5-libero-object.gguf`	6.46 GiB	Combined VLA model — Eagle-2 backbone + flow-matching action head + arch config, BF16
`dataset_statistics.json`	—	Action/state normalisation stats (required by the client)

Usage

# Terminal 1 — serve (use the CUDA build for inference). No mmproj argument.
VLA_GR00T_BF16_WEIGHTS=1 VLA_GR00T_EMBODIMENT=new_embodiment \
    ./build-cuda/vla-server --bind tcp://*:5566 \
    gr00tn1d5-libero-object.gguf

# Terminal 2 — drive a LIBERO episode (inside the LIBERO uv venv)
python eval/client/run_sim_client_direct.py \
    --arch gr00t_n1_5 \
    --task libero_object --task-id 0 --n-episodes 10 \
    --stats-json dataset_statistics.json \
    --vla-addr tcp://localhost:5566

Notes:

Set VLA_GR00T_EMBODIMENT=new_embodiment and VLA_GR00T_BF16_WEIGHTS=1 (the latter is needed to fit an 8 GB card).
The client auto-downloads the lerobot/eagle2hg-processor-groot-n1p5 tokenizer from the Hub (trust_remote_code); no --tokenizer is needed.
Pass --stats-json dataset_statistics.json (action/state un-normalisation).
The client uses --n-action-steps 16 for this checkpoint.

Benchmark

Full libero_object sweep (10 tasks × 20 episodes = 200 episodes):

Hardware	n_act	Success rate	client/step	client/call	Peak mem
RTX 3060 (sm_86)	16	96.0%	14.17 ms	227 ms	4866 MiB VRAM
Jetson AGX Orin (sm_87)	16	97.5%	28.78 ms	461 ms	1331 MiB RAM
Jetson Orin Nano 8 GB (sm_87)	16	96.0%	84.76 ms	1356 ms	4399 MiB sys-Δ

Unlike GR00T-N1.6 / N1.7, N1.5's footprint fits the Jetson Orin Nano 8 GB. The Nano row was run split (server on the Nano, LIBERO client on a separate host); sys-Δ is the system-used-RAM rise, the faithful unified-memory figure on Tegra.

License

Weights follow the upstream license of nvidia/GR00T-N1.5-3B (NVIDIA license — review and accept it before use). The vla.cpp conversion tooling and inference engine are Apache-2.0-licensed.