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
File size: 1,020 Bytes
03426f9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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
@@ -989,6 +989,13 @@ 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
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