Instructions to use Dongkkka/rldx-1-cyclo-intelligence-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dongkkka/rldx-1-cyclo-intelligence-test with Transformers:
# Load model directly from transformers import RLDX model = RLDX.from_pretrained("Dongkkka/rldx-1-cyclo-intelligence-test", dtype="auto") - LeRobot
How to use Dongkkka/rldx-1-cyclo-intelligence-test with LeRobot:
- Notebooks
- Google Colab
- Kaggle
RLDX-1 Cyclo Intelligence Test Fine-tune
Inference-only export of an RLDX-1 fine-tune on Dongkkka/cyclo_intelligence_test_dataset_lerobot_v2.1.
Training Summary
- Base model:
RLWRLD/RLDX-1-PT - Dataset:
Dongkkka/cyclo_intelligence_test_dataset_lerobot_v2.1 - Dataset size used: 31 episodes / 5,565 frames
- Camera input:
observation.images.cam_head_leftonly - Task text:
Pick up the green ball and place it into the white basket. - Training steps: 1,000
- Global batch size: 208
- Final logged step loss: 0.0073
- Mean train loss: 0.022565414
Included Files
This repository intentionally contains only the files needed for inference:
config.jsonmodel-00001-of-00004.safetensorsmodel-00002-of-00004.safetensorsmodel-00003-of-00004.safetensorsmodel-00004-of-00004.safetensorsmodel.safetensors.index.jsonprocessor/embodiment_id.jsonprocessor/processor_config.jsonprocessor/statistics.json
Checkpoint resume files, optimizer state, scheduler state, RNG state, trainer state, training args, wandb config, and experiment config files are excluded.
Notes
The dataset has 31 available episodes, so the run used all available episodes for a 1,000-step test fine-tune. The model was smoke-tested by loading it with RLDXPolicy and generating action outputs for the left head camera setup.
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