압축 가능성 라벨로 게이트를 학습한 액션 양자화(ATQ) 체크포인트다. 하나의 정책이 미세(1x) 디코더와 압축 디코더를 함께 갖고, VLM 라벨에서 배운 conf 가 둘 중 어느 군을 쓸지 고른다. 라우터는 그 군 안에서 horizon 만 고른다. (VLM 전용, 접촉 열 없음 · 16,286행 · 1,693에피 · stride 16) moeexperthorizons = [16, 9, 5, 8] · confthreshold(tau) = 0.55 · discreteactiondims = [6] (그리퍼는 절대 명령이라 · actionmergereduction = sum 회전 병합은 SO(3) 다(rotationmergespec 이 config 에 있다). 압축 블록의 회전 다시 정규화한다. scipy 대조 각도 오차 1e-14도. LIBERO 는 5 fine 스텝마다 재계획한다. 압축 행 하나는 fine 액션 2~3개의 합이므로 같은 배속이 되고, 배속을 움직이는 손잡이는 conf 게이트 하나다: 넘으므로 OSC 팔 컨트롤러의 입력 클립을 제거한 조건에서 평가했다(그리퍼 그대로). 게이트는 벤치마크가 실제로 깨지는 순서를 따른다 -- 압축에 강한 liberoobject 를 가장 많이 압축하고, 2배에서 -0.160 으로 무너지는 liberospatial 은 거의 압축하지 않는다. 브랜치 jimin-dev-label-gated.…
gr00t1.5n_libero10_x06 is an open-weight model for robotics from Jian Kim, released under Apache License 2.0. It has 2.7B parameters. At 16-bit it needs about 6.5 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
Fine-tuned GR00T-N1.5 victim policy trained on sequentially perturbed LIBERO-10 demonstrations.
Parameters2.7B
Context—
Weights7.6 GB
Licenseapache-2.0
AccessOpen weights
Monthly Downloads—
Runs On
What it takes to serve gr00t1.5n_libero10_x06 (2.7B parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 5.4 GB | 6.5 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 2.7 GB | 3.3 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 1.4 GB | 1.6 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
gr00t1.5n_libero10_x06 on every accelerator the SAVRN Index prices, at every precision
Model Card
By Jian Kim, published under apache-2.0, revision 4ac752dc66e4.
Fine-tuned GR00T-N1.5 victim policy trained on sequentially perturbed LIBERO-10 demonstrations.
Read Jian Kim's full model card
GR00T-N1.5 LIBERO-10 poisoned policy (x06)
Fine-tuned GR00T-N1.5 victim policy trained on sequentially perturbed LIBERO-10 demonstrations.
- Perturbation axis: end-effector position x
- Scale tag: 06 (alpha = 0.6)
- Injection pattern: square-wave noise, half-period = 1
- Training data:
lerobot_pos.x_06_sequential
Clean baseline: jiankimr/gr00t_libero10_clean
Configuration
- Architecture
- GR00T_N1_5
- Hidden size
- 2,048
- Stored precision
- bfloat16
- Model type
- gr00t_n1_5
Identity and Version
- Repository
- jiankimr/gr00t1.5n_libero10_x06
- Publisher
- Jian Kim
- Task
- Robotics
- Modality
- Control
- Library
- Not stated by the source
- Parameters
- 2.7B parameters
- Languages
- Not stated by the source
- Revision
- 4ac752dc66e4209850ba3ce0c406a3b169c16bb3
- First published
- 2026-09-20
- Last updated
- 2026-09-20
Files and Weights
9 files, 7.6 GB in total. The weights are 3 files totalling 7.6 GB in bin, safetensors.
Weights3 files · 7.6 GB
Configuration4 files · 1.1 MB
Documentation1 file · 526 B
Repository1 file · 1.5 KB
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model-00001-of-00002.safetensors | Weights | 5.0 GB | cfb419864234 |
| model-00002-of-00002.safetensors | Weights | 2.6 GB | 4de5e107dce6 |
| training_args.bin | Weights | 5.5 KB | f2f1c5774f00 |
| config.json | Configuration | 1.7 KB | — |
| experiment_cfg/metadata.json | Configuration | 13.9 KB | — |
| model.safetensors.index.json | Configuration | 104.6 KB | — |
| trainer_state.json | Configuration | 955.7 KB | — |
| README.md | Documentation | 526 B | — |
| .gitattributes | Repository | 1.5 KB | — |
License and Download
- License
- apache-2.0
- Access
- Open weights, no gate
- Download size
- 7.6 GB
Download from Jian Kim
Released by Jian Kim through its official repository on Hugging Face. Read the license.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 7.6 GB |
| 16-bit | 5.4 GB |
| 8-bit | 2.7 GB |
| 4-bit | 1.4 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About gr00t1.5n_libero10_x06
How much GPU memory does gr00t1.5n_libero10_x06 need?
About 6.5 GB at 16-bit and 1.6 GB at 4-bit: the weights (2.7B parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run gr00t1.5n_libero10_x06 on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.
Can I use gr00t1.5n_libero10_x06 commercially?
Yes. gr00t1.5n_libero10_x06 is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors.
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