happyhappy-jun/gr00t-n1.6-uvd-racer-pt120k-mix50

GR00T N1.6 — RoboCasa365 UVD-RACER PT 120k (50:50 subtask / full-task language)

This is an independently trained multitask pretraining (PT) checkpoint based on NVIDIA GR00T N1.6. It is not an official NVIDIA release and not a target-task fine-tuned checkpoint.

Checkpoint identity

• —Training run: n1d6_uvd_racer_mix50_train (experiment dir 20260910_mix50_120k)
• —Checkpoint: checkpoint-120000 (120,000 optimizer updates)
• —Source job: 11798 on the lab AWS cluster gpu26 (4 GPUs, single node)
• —Base model: GR00T-N1.6-3B snapshot d0814e7
• —Source promotion-manifest SHA-256: f18ffd649b0a74dcb69c5b190a83610335042ceaba278fdb8e50a2cdc3c2ec8e

The model files are copied byte-for-byte from that checkpoint. No weights, normalization statistics, modality settings, or configuration values were changed for this upload. See publication_manifest.json for sizes and SHA-256 checksums of the published source files.

Training setup

DeepSpeed 0.17.5 is the pinned version for this lineage; it does not have the 0.17.6 ZeRO-2 gradient-accumulation defect, so the reported effective batch of 128 is the batch the optimizer actually saw.

What "frozen backbone" does and does not mean here

--no-tune-llm --no-tune-visual resolve to tune_llm: false and tune_visual: false in the run's own model config, and the runtime's startup log accordingly prints Tune backbone llm: False / Tune backbone visual: False. But that same config also carries `tune_top_llm_layers: 4`, and the runtime acts on it: besides the diffusion/action head, the projector and the vision-language LayerNorm (tune_vlln: true), it marks the top 4 layers of the language backbone trainable — layers 12-15, the four immediately below select_layer: 16, where the projector reads the backbone — and casts them to fp32 because backbone_trainable_params_fp32: true. On a sibling run of this campaign with the same training runtime and the same launch flags, that is 160 trainable parameter tensors, every one of them under `model.language_model.model.layers.{12,13,14,15}`, and the runtime reports `Trainable parameters: 1,619,968,000 (49.29%)`. No vision-tower parameter appears in that list, so the vision tower is genuinely frozen.

So the accurate statement is: the vision tower is frozen and the language backbone is frozen except for its top 4 layers, which are fine-tuned along with the projector, the vision-language LayerNorm and the diffusion/action head. This is the stock behavior of this runtime at these flags and is identical across the sibling checkpoints of this campaign.

None of this has to be taken on trust: it is readable from this repository's own config.json, which ships tune_llm: false, tune_visual: false, `tune_top_llm_layers: 4`, select_layer: 16, tune_vlln: true, tune_projector: true, tune_diffusion_model: true and backbone_trainable_params_fp32: true.

Contents

• —Two sharded Safetensors weight files and their index
• —config.json
• —processor_config.json
• —statistics.json (normalization statistics)
• —embodiment_id.json

Optimizer, scheduler, RNG state, training logs, internal experiment configs and training datasets are not included. This is a model export for inference or for starting a new fine-tuning run, not a complete optimizer-state resume bundle.

Download and compatibility

from huggingface_hub import snapshot_download

checkpoint_dir = snapshot_download(
    "happyhappy-jun/gr00t-n1.6-uvd-racer-pt120k-mix50",
    # revision=<commit sha of the single publication commit>
)

The whole payload is submitted in one atomic commit on a freshly created repository, so that commit's sha is the only model revision here. It cannot be written inside this file (the text would change the commit it names); the pinned snapshot_download(..., revision=...) snippet is recorded in the publishing lab's publication log alongside the per-file SHA-256 values.

Use this directory with compatible Isaac-GR00T N1.6 model/processor code. The model type is Gr00tN1d6; ordinary Transformers AutoModel loading has not been validated. The processor references nvidia/Eagle-Block2A-2B-v2, which may require separate model access/download. The preserved processor includes a RoboCasa new_embodiment modality configuration; use the matching embodiment, camera/state/action conventions and normalization statistics.

Upload verification covers source checksum identity, Safetensors/index structure and Hub file integrity. It is not a new GPU inference or robot rollout evaluation. No task-success or safety-performance claim is made by this model card.

License and safety

The underlying model is subject to the NVIDIA noncommercial license linked by the upstream model card. A complete, unmodified copy is included as `LICENSE.pdf`. The license restricts use to noncommercial research or evaluation.

Attribution: NVIDIA Isaac GR00T N1.6 and its underlying components. This repository is an independent derivative checkpoint and is not endorsed by NVIDIA.

Validate in simulation and use appropriate robot safety controls before any physical deployment. Outputs are not guaranteed to be safe or correct.