Instructions to use Yang2001/ckpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Yang2001/ckpt with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Yang2001/ckpt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Checkpoints
opendm/sim_exam-piper-l1-m1-piper-joint-0
DM0.5 (OpenDM) full SFT on the sim-exam dual Piper L1 split (900 episodes, 1,636,232 frames),
initialized from DM05-MEM. Experiment M1: 15,000 steps x global batch 256
(8 x H20, micro-batch 8, gradient accumulation 4), lr 5e-5, 1,000 warmup steps,
cosine to 2.5e-5. Action space: piper / joint.
Files are the final export (inference weights, config.json, norm_stats.json, processor and
tokenizer, plus training_config.json / training_dataset.json / trainer_state.json for provenance).
Load the directory with the XPolicyLab OpenDM adapter.