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
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Download README.md from ChengYangYang/bpp-reading-pointer-weights: direct link, hf CLI and curl.
- Browser
- Download file 820 Bytes
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https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/README.md
- Command line
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hf download hf://ChengYangYang/bpp-reading-pointer-weights/README.md
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curl -L -o README.md https://huggingface.co/ChengYangYang/bpp-reading-pointer-weights/resolve/main/README.md
820 Bytes
Decoupled reading: model weights (backup 2026-09-30)
reader_weights/: decoupled reader (12obs_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).