Add model card and robotics tag

README.md

@@ -1,9 +1,38 @@
 ---
 library_name: transformers
-tags: []
+pipeline_tag: robotics
 ---
 
-# Cite it
+# ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning
+
+[ShadowPEFT](https://huggingface.co/papers/2604.19254) is a parameter-efficient fine-tuning (PEFT) framework that augments a frozen large base model with a lightweight, centralized, and detachable *Shadow* network. Unlike standard LoRA-style adapters, ShadowPEFT performs layer-level refinement through a depth-shared shadow module. This design allows the shadow module to be architecturally decoupled from the backbone, enabling it to be trained and deployed as a standalone component, which is particularly beneficial for edge computing and robotics.
+
+- **Paper:** [ShadowPEFT: Shadow Network for Parameter-Efficient Fine-Tuning](https://huggingface.co/papers/2604.19254)
+- **Repository:** [https://github.com/ShadowLLM/shadow-peft](https://github.com/ShadowLLM/shadow-peft)
+
+## Sample Usage
+
+To use this shadow model, first install the `shadow-peft` library:
+
+```bash
+pip install shadow-peft
+```
+
+Then, you can load the pre-trained projected shadow model using the following code:
+
+```python
+from shadow_peft import AutoModelForCausalLMWithHiddenProjection
+
+# Load the pre-trained projected shadow model
+shadow_model = AutoModelForCausalLMWithHiddenProjection.from_pretrained(
+    "shadow-llm/Qwen3-0.6B-H8B",
+    freeze_backbone=False,      # keep backbone trainable (default)
+    freeze_embed_tokens=True,   # freeze input embeddings (default)
+    freeze_lm_head=True,        # freeze lm_head (default)
+)
+```
+
+## Citation
 
 ```bibtex
 @article{li2026shadowpeft,