GR00T-N1.7 — LIBERO object (GGUF for vla.cpp)

GGUF conversion of nvidia/GR00T-N1.7-LIBERO for inference with vla.cpp, a lightweight C++ inference engine for Vision-Language-Action models built on top of llama.cpp.

GR00T-N1.7 pairs a Cosmos-Reason2 / Qwen3-VL-2B backbone (24-layer ViT + deepstack) with a hybrid action head (4-layer VL self-attention ⊕ 32-layer AlternateVLDiT flow-matching), producing 40×132 relative actions with q01/q99 normalisation. The vision tower is baked into the combined GGUF, so no separate mmproj file is needed.

Files

File	Size	Description
`gr00t-n1d7-libero-object.gguf`	5.86 GiB	Combined VLA model — Qwen3-VL backbone + deepstack + VL self-attention + AlternateVLDiT 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=libero_sim \
    ./build-cuda/vla-server --bind tcp://*:5566 \
    gr00t-n1d7-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_7 \
    --task libero_object --task-id 0 --n-episodes 10 \
    --stats-json dataset_statistics.json \
    --vla-addr tcp://localhost:5566

Notes:

Set VLA_GR00T_EMBODIMENT=libero_sim and VLA_GR00T_BF16_WEIGHTS=1 (the latter is needed to fit an 8 GB card).
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	98.0%	10.26 ms	164 ms	6302 MiB VRAM
Jetson AGX Orin (sm_87)	16	98.5%	26.84 ms	429 ms	1317 MiB RAM

GR00T-N1.7's ~6 GiB all-resident weight footprint OOMs the Jetson Orin Nano 8 GB unified pool, so it is not in the Orin Nano sweep. A VLA_GR00T_STAGE_SWAP=1 mode brings weights to ~3.25 GiB at a heavy per-call latency cost.

Implementation note

The action head's vlln must be fed the pre-norm LM hidden state (hidden_states[-1], std ≈ 33), not the post-norm last_hidden_state (std ≈ 2.17). vla.cpp drops lm_output_norm between the LM and the projector to match HF's Qwen3VLBackbone.forward — this is the single largest contributor to going from 0/10 to 10/10 on LIBERO.

License

Weights follow the upstream license of nvidia/GR00T-N1.7-LIBERO (NVIDIA license — review and accept it before use). The vla.cpp conversion tooling and inference engine are MIT-licensed.