Instructions to use kunhsiang/act-baseline-seed42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use kunhsiang/act-baseline-seed42 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
act-baseline-seed42
備份自 ID Lab lerobot-vla-fbagent 專案,含完整中間 checkpoint。
訓練資訊
| 項目 | 值 |
|---|---|
| Policy Type | act |
| Dataset | HuggingFaceVLA/libero |
| Max Steps | 10,000 |
| Checkpoints | 2 個 |
| Seed | ? |
| Batch Size | ? |
| Learning Rate | ? |
| Num Workers | ? |
| Chunk Size | 100 |
| Git Commit | c92e49c |
| Source Dir | 18-19-28_act |
復現指令
git clone --recurse-submodules git@github.com:KunHsiang/lerobot-vla-fbagent.git
cd lerobot-vla-fbagent
git checkout c92e49c
uv sync
HF_HUB_ENABLE_HF_TRANSFER=1 .venv/bin/python scripts/train_with_fb.py \
--policy.type=act \
--policy.repo_id=kunhsiang/act-baseline-seed42 \
--dataset.repo_id=HuggingFaceVLA/libero \
--training.seed=? \
--training.batch_size=? \
--training.learning_rate=? \
--training.num_workers=?
Checkpoint 結構
checkpoints/
005000/ # step 5,000
010000/ # step 10,000
...
010000/ # final (10,000 steps)
每個 step 目錄包含:
pretrained_model/model.safetensors— 模型權重pretrained_model/config.json— 模型設定pretrained_model/train_config.json— 完整訓練設定training_state/optimizer_state.safetensors— Optimizer 狀態(可 resume)training_state/rng_state.safetensors— RNG 狀態(可 resume)
Resume 訓練
.venv/bin/python scripts/train_with_fb.py \
--policy.type=act \
--resume=true \
--policy.pretrained_path=checkpoints/010000/pretrained_model
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