doc: bundled SmolVLM2 cache + offline launcher recipe

README.md

@@ -1,17 +1,3 @@
----
-base_model:
-- HuggingFaceTB/SmolVLM2-500M-Video-Instruct
-pipeline_tag: robotics
-tags:
-- robotics
-- lerobot
-- lehome-challenge
-- smolVLA
-- residual-rl
-datasets:
-- lehome/dataset_challenge_merged
-- lehome/dataset_challenge
----
 # LeHome Challenge 2026 — Submission
 
 **Method**: SmolVLA (frozen four_types 30K backbone) + state-only residual MLP, trained with sparse-reward residual RL on 40 Seen garments. Single model, deterministic inference.
@@ -23,11 +9,20 @@ submission_v4_global/
 ├── README.md                          # this file
 ├── residual_v4_global.py              # the policy module (drop into eval_policy/)
 └── submission_models/
-    ├── vla_backbone/                  # SmolVLA four_types 30K (LeRobot pretrained_model)
-    ├── residual_averaged.pt           # 40-garment averaged residual MLP
-    └── dataset_meta/                  # LeRobot dataset metadata (stats.json etc.)
+    ├── vla_backbone/                  # SmolVLA four_types 30K (LeRobot pretrained_model, ~865M)
+    ├── residual_averaged.pt           # 40-garment averaged residual MLP (~286K)
+    ├── dataset_meta/                  # LeRobot dataset metadata (stats.json etc.)
+    └── hf_cache/                      # bundled SmolVLM2 weights for offline VLM load (~1.9G)
+        └── hub/models--HuggingFaceTB--SmolVLM2-500M-Video-Instruct/
+            ├── snapshots/<commit>/    # tokenizer + processor + model.safetensors
+            └── refs/main              # commit hash file
 ```
 
+The wrapper detects `submission_models/hf_cache/` next to `vla_backbone/` and
+sets `HF_HOME` to it during `__init__`, so the SmolVLM2 backbone load
+(`vlm_model_name = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"`,
+`load_vlm_weights = true`) resolves entirely offline against the bundled cache.
+
 ## How To Run (evaluator side)
 
 1. Drop `residual_v4_global.py` into `/opt/lehome-challenge/scripts/eval_policy/`.
@@ -41,6 +36,19 @@ submission_v4_global/
    export LEHOME_VLA_DATASET_ROOT=<path to submission_models/dataset_meta or any LeRobot dataset>
    export LEHOME_RESIDUAL_CHECKPOINT=<path to submission_models/residual_averaged.pt>
    export LEHOME_RESIDUAL_SCALE=0.03
+   ```
+
+   The wrapper sets `HF_HOME` automatically to the bundled `hf_cache/` when
+   it sees `LEHOME_VLA_POLICY_PATH`, so no network access is required even on
+   a fully offline evaluator.
+
+   **Belt-and-suspenders** — if the evaluator's launcher imports
+   `huggingface_hub` before our wrapper module loads (rare but possible),
+   the redirect may be too late. To be safe, set HF env vars **before**
+   invoking `python -m scripts.eval`:
+   ```bash
+   export HF_HOME="$LEHOME_VLA_POLICY_PATH/../hf_cache"
+   export HF_HUB_CACHE="$HF_HOME/hub"
    export HF_HUB_OFFLINE=1
    export TRANSFORMERS_OFFLINE=1
    ```
@@ -140,6 +148,4 @@ Inference is fully deterministic. Two runs with the same backbone, residual chec
 
 ## Contact
 
-vita / realvitacai@gmail.
-
-klein /kleinlau17@gmail.com
+vita / realvitacai@gmail.com