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 README.md from ar-mine/cosmos-robometer-private-code: direct link, hf CLI and curl.
- Browser
- Download file 3.04 kB
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https://huggingface.co/ar-mine/cosmos-robometer-private-code/resolve/main/README.md
- Command line
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hf download hf://ar-mine/cosmos-robometer-private-code/README.md
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curl -L -o README.md https://huggingface.co/ar-mine/cosmos-robometer-private-code/resolve/main/README.md
3.04 kB
| library_name: transformers | |
| tags: | |
| - robotics | |
| - reward-modeling | |
| - qwen3-vl | |
| - peft | |
| - offline-training | |
| # Cosmos–Robometer Phase 2.1A training code | |
| Portable training kit for the approved `Public-Proxy stage2_core_v1` | |
| experiments: | |
| - `Robometer-Low-LoRA`: implemented formal trainer with frozen ID-dev evaluation, | |
| early stopping, and a 3,000-step hard cap. | |
| - `Robometer-Low`: full language-decoder smoke test. Formal training remains | |
| intentionally blocked until suitable multi-GPU/server memory is available. | |
| The repository does not duplicate the official Robometer trajectory archives, | |
| model weights, checkpoints, credentials, OOD evaluation archives, or | |
| machine-specific run logs. It does contain derived action sidecars and their | |
| manifests: 20,620 sidecar files are packaged in a 455,188,480-byte tar archive. | |
| These sidecars are not consumed by the Phase 2.1A reward-model training loop. | |
| ## Recreate the environment | |
| ```bash | |
| uv python install 3.10.12 | |
| uv sync --project environment/phase21a_robometer --frozen | |
| uv run --project environment/phase21a_robometer \ | |
| python environment/phase21a_robometer/doctor.py | |
| ``` | |
| The environment is locked for PyTorch 2.8.0/CUDA 12.8. The reference smoke | |
| hardware was one RTX 5090 with 32,607 MiB VRAM. | |
| For a server with restricted or no network access, download the validated pure | |
| uv offline archive under `offline_env/`. It includes uv 0.11.3, CPython 3.10.12, | |
| all 50 locked packages, CUDA 12.8 Python wheels, and the locally built | |
| FlashAttention wheel. See [OFFLINE_UV_DEPLOYMENT.md](OFFLINE_UV_DEPLOYMENT.md). | |
| ## External inputs | |
| - Base model: `unsloth/Qwen3-VL-4B-Instruct` at revision | |
| `252d592b59b0233b226875a44ac135cfa1d3f755`. | |
| - Dataset route: `Public-Proxy stage2_core_v1` derived from | |
| `robometer/processed_datasets` at revision | |
| `1a8eb2d279c70964fadeaf8019a77c490c3abe02`. | |
| - Frozen Phase 2.1A subset: 2,689 pair rows referencing 4,323 trajectory files. | |
| There is no separate `ar-mine/cosmos-robometer-public-dataset` repository. The | |
| subset is reconstructed from the pinned official Robometer processed-data | |
| repository and the frozen dataset manifest in this repository. | |
| ## Training | |
| See [TRAINING_MANUAL.md](TRAINING_MANUAL.md). The frozen LoRA configuration is | |
| `configs/phase21a_robometer_low_lora_train_v2.yaml`. | |
| For the pinned official `stage2_core_v1` download, extraction, frozen-manifest | |
| association, and optional recovered-action linkage, see | |
| [DATA_SETUP.md](DATA_SETUP.md). | |
| ## Current support boundary | |
| The LoRA trainer is runnable and validated at micro-batch 32, gradient | |
| accumulation 2, effective batch 64. The full-tune file is a capacity smoke test, | |
| not a formal distributed trainer. Paged 8-bit AdamW is not permitted for the | |
| formal `Robometer-Low` comparison. | |
| ## Redistribution | |
| This public repository includes experiment code, metadata, and recovered action | |
| sidecars, but does not grant redistribution rights for upstream datasets or | |
| model weights. Review the terms of each upstream source before reusing or | |
| redistributing derived assets. | |