Instructions to use ChengYangYang/bpp-reading-pointer-weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use ChengYangYang/bpp-reading-pointer-weights with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("ChengYangYang/bpp-reading-pointer-weights", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
File size: 820 Bytes
9dca290 | 1 2 3 4 5 6 7 8 | # Decoupled reading: model weights (backup 2026-09-30)
- `reader_weights/`: decoupled reader (12 `obs_encoder.ptr_*` tensors) + normalizer + cfg for every sequence-training run (`INDEX.json`); the BPP policy is frozen, so released BPP checkpoint + these = full model.
- `full/`: full checkpoints, learned-count reader (LEVT L1/L2) and joint-training ablations.
- `full_policy_trained/`: runs that also trained the policy (sync/content/nocopy/lnc/dag/factorslot).
- `pi05_lora/`: pi0.5 LoRA on LIBERO-Gen goal chains (openpi), steps 5k-50k; evaluated: 10000, 20000, 49999.
- `baselines/`: RoboSSM (20,596 steps) and UniSkill (epoch 500) retrained on LIBERO-Gen Chain.
- `earlier_lines/`: models from earlier project lines (subgoal/goal image, demo generators, keyframe, pose, trajectory, anchor, phase, boundary reader).
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