Other
Transformers
Safetensors
PyTorch
English
vision-language-action
humanoid-robotics
telepathy
multimodal
robotics-control
lora
Instructions to use Veltraxor/Sigma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veltraxor/Sigma with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Veltraxor/Sigma", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| diff --git a/src/lerobot/policies/pi05/modeling_pi05.py b/src/lerobot/policies/pi05/modeling_pi05.py | |
| index b017bbc5..d6290da6 100644 | |
| --- a/src/lerobot/policies/pi05/modeling_pi05.py | |
| +++ b/src/lerobot/policies/pi05/modeling_pi05.py | |
| class PI05Policy(PreTrainedPolicy): | |
| if remap_count > 0: | |
| print(f"Remapped {remap_count} state dict keys") | |
| # Load the remapped state dict into the model | |
| missing_keys, unexpected_keys = model.load_state_dict(remapped_state_dict, strict=strict) | |
| + | |
| + # --- FIX: tie embed_tokens to lm_head if embed_tokens missing in ckpt --- | |
| + if any("embed_tokens.weight" in k for k in missing_keys): | |
| + with torch.no_grad(): | |
| + embed = model.model.paligemma_with_expert.paligemma.model.language_model.embed_tokens | |
| + lm_head = model.model.paligemma_with_expert.paligemma.lm_head | |
| + embed.weight = lm_head.weight | |
| return model | |