Document base model dependencies and direct loading

README.md

@@ -41,6 +41,30 @@ configs/piper-color-sorting/config.json
 The checkpoint is intended to be loaded with the MemoryVLA codebase and
 `use_ema=True`. The model uses the `custom_finetuning` normalization key.
 
+For direct loading from the repository root, a server-side LFS alias is also
+provided at:
+
+```text
+checkpoints/frozen-ema-step-20000.pt
+```
+
+Set `MEMORYVLA_SKIP_BASE_WEIGHTS=1` for inference. The task checkpoint already
+contains the trained LLM, vision backbone, projector, action model, and EMA
+weights. The loader still needs the Llama-2 tokenizer/config metadata from
+`NousResearch/Llama-2-7b-hf`; it does not need to download the full Llama or
+vision weight files again.
+
+For future real-task training, the matching CogACT-Large initialization
+checkpoint is available at:
+
+```text
+base_models/CogACT-Large/CogACT-Large.pt
+```
+
+The original Llama model weights are not duplicated in this repository because
+they are gated third-party weights. Use the original Hugging Face model with
+the appropriate access terms and token.
+
 ## Training configuration
 
 - Task: Piper color sorting
@@ -91,7 +115,7 @@ settings for this checkpoint are:
 
 ```python
 model = load_vla(
-    model_id_or_path="checkpoints/piper-color-sorting/frozen-ema-step-20000.pt",
+    model_id_or_path="Haongchen/MemoryVLA",
     load_for_training=False,
     action_dim=7,
     future_action_window_size=15,