Downloads · 30 days
0
bi-lawm-robotwin-finetune-from-stage2only
GT-111/bi-lawm-robotwin-finetune-from-stage2only
bi-lawm-robotwin-finetune-from-stage2only is a robotics model from GT-111. Use it for the robotics task on the model card, and read the license before you ship it in a product.
Repository: GT-111/bi-lawm-robotwin-finetune-from-stage2only (renamed from bi-lawm-robotwin-stage3-finetune on 2026-08-31)
Access
Public
Updated Aug 31, 2026
Repo size
224 GB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.pt122 GB · 54%
At a glance
- Task
- Robotics
- Access
- Public
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026
- SHA
- ac76060b
Model card
From the Hugging Face model README
Bi-LaWM RoboTwin Stage3 Finetune — From Stage2-Only Ablation 50k
Repository: GT-111/bi-lawm-robotwin-finetune-from-stage2only (renamed from bi-lawm-robotwin-stage3-finetune on 2026-08-31)
Intermediate checkpoints from the RoboTwin Stage3 finetune training run of Bi-LaWM (80k-step plan, stopped at 50k by decision).
Lineage
- Initialized from the consolidated Stage2 50k export of the stage2-only ablation (
export/step-050000/pytorch_model.pt). - Milestones are published every 5k steps plus a snapshot of the latest checkpoint.
Checkpoints
| Hub path | Training step | Format |
|---|---|---|
checkpoints/step-005000/ | 5,000 | Full sharded FSDP training checkpoint |
checkpoints/step-010000/ | 10,000 | Full sharded FSDP training checkpoint |
checkpoints/step-015000/ | 15,000 | Full sharded FSDP training checkpoint |
checkpoints/step-020000/ | 20,000 | Full sharded FSDP training checkpoint |
checkpoints/step-025000/ | 25,000 | Full sharded FSDP training checkpoint |
checkpoints/step-030000/ | 30,000 | Full sharded FSDP training checkpoint |
checkpoints/step-035000/ | 35,000 | Full sharded FSDP training checkpoint |
checkpoints/step-040000/ | 40,000 | Full sharded FSDP training checkpoint |
checkpoints/step-045000/ | 45,000 | Full sharded FSDP training checkpoint |
checkpoints/step-050000/ | 50,000 | Final checkpoint (80k plan stopped at 50k by decision) |
Each checkpoint contains the model shards, per-rank optimizer and RNG states, scheduler state, and trainer_state.json. These are resumable distributed-training checkpoints rather than merged inference-only weights.
Experiment
- Project: Bi-LaWM
- Dataset/environment: RoboTwin
- Stage: Stage3 policy finetune
- Policy initialization: Stage2 50k ablation export (see
GT-111/bi-lawm-stage2-only-ablation) - Training plan: 80k steps (stopped at 50k by decision)
Loading
Use these checkpoints with the matching Bi-LaWM code and FSDP configuration. For inference or evaluation, export/merge the selected distributed checkpoint with the project checkpoint tooling first.
- Task
- Robotics
- Created
- Aug 31, 2026
- Updated
- Aug 31, 2026