Instructions to use ar-mine/cosmos-robometer-private-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ar-mine/cosmos-robometer-private-code with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ar-mine/cosmos-robometer-private-code", device_map="auto") - PEFT
How to use ar-mine/cosmos-robometer-private-code with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
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Download reports/PHASE21A_STEP_BUDGET_V2.md from ar-mine/cosmos-robometer-private-code: direct link, hf CLI and curl.
- Browser
- Download file 2.17 kB
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https://huggingface.co/ar-mine/cosmos-robometer-private-code/resolve/main/reports/PHASE21A_STEP_BUDGET_V2.md
- Command line
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hf download hf://ar-mine/cosmos-robometer-private-code/reports/PHASE21A_STEP_BUDGET_V2.md
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curl -L -o PHASE21A_STEP_BUDGET_V2.md https://huggingface.co/ar-mine/cosmos-robometer-private-code/resolve/main/reports/PHASE21A_STEP_BUDGET_V2.md
2.17 kB
| # Phase 2.1A step budget v2 | |
| Date: 2026-08-27 | |
| ## Decision | |
| Replace the inherited 15,000-step fixed run with a 3,000-step hard cap and | |
| pre-registered ID-dev early stopping. Apply the same cap and selection rule to | |
| Robometer-Low-LoRA and the future full-decoder Robometer-Low comparison. | |
| ## Scale calculation | |
| - Frozen train set: 2,021 preference pairs. | |
| - Effective batch: 64 pairs per optimizer step. | |
| - 15,000 steps: 960,000 pair presentations, approximately 475 nominal passes. | |
| - 3,000-step cap: 192,000 pair presentations, approximately 95 nominal passes. | |
| - Minimum 1,000 steps: 64,000 pair presentations, approximately 32 nominal passes. | |
| The official 15,000-step value was copied from the million-scale RBM-1M recipe; | |
| it was not estimated from this 2,021-pair Low split. | |
| ## Evidence from the stopped v1 run | |
| The user-stopped seed-7 run logged through step 246. Mean total train loss fell | |
| from 4.573 over steps 1-25 to 3.218 over steps 201-225; the last-100-step linear | |
| trend was still negative by about 0.455 per 100 steps. It had not converged. | |
| However, the 15,000-step scheduler used 750 warmup steps, so step 246 had reached | |
| only about 33% of the target learning rate. This curve cannot be extrapolated to | |
| justify 15,000 steps. | |
| ## Selection protocol | |
| - Scheduler: cosine with 5% warmup; 150 warmup steps at the cap. | |
| - Evaluate every 250 steps on the original 108-pair `dev` split. | |
| - Evaluate both A/B orientations for each pair. | |
| - Select by the sum of mean preference, progress, and success ID-dev losses. | |
| - Minimum training: 1,000 steps. | |
| - Early stop after four consecutive evaluations without an improvement of at | |
| least 0.005 ID-dev loss. | |
| - Restore the best ID-dev checkpoint for export. | |
| - Do not use `confirmation_dev` or RBM-EVAL-OOD for selection. | |
| The cap is a safety bound, not a claim that every seed needs exactly 3,000 | |
| steps. The selected step is an experimental result recorded per seed. | |
| ## Restart boundary | |
| The v1 run is not resumed into v2. Its latest complete checkpoint is step 4, | |
| and v2 changes both the scheduler horizon and checkpoint-selection protocol. | |
| All v2 seeds must start from the same pinned base model in a new output root. | |