Document raw_checkpoints/ in README

README.md

@@ -63,6 +63,8 @@ Inherited from the base model (no architecture changes, only weight finetuning):
 
 ## Usage
 
+### Standard inference (recommended)
+
 ```python
 from lerobot.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
@@ -73,6 +75,38 @@ policy.eval()
 Drop-in compatible with the base model — same input/output schema, just
 swap the repo id.
 
+### Raw checkpoints (every 5k steps)
+
+The full run trajectory is also stored under `raw_checkpoints/` in the repo
+for offline evaluation, ablations, or resuming training. These are PyTorch
+`.pt` files in the original training format (NOT safetensors):
+
+| File | Contents | Size | Use case |
+|---|---|---|---|
+| `raw_checkpoints/best.pt` | model + cfg + success | ~1 GB | Inference at peak success |
+| `raw_checkpoints/final.pt` | model + cfg + step | ~1 GB | Last training step |
+| `raw_checkpoints/step_{10,15,20,25,30}000.pt` | model + optimizer + scheduler + step | ~3 GB each | Resume training; per-step ablation |
+
+Download e.g. one via:
+
+```python
+from huggingface_hub import hf_hub_download
+import torch
+
+path = hf_hub_download(
+    repo_id="zengxy0624/diffusion-pusht-obstacles",
+    filename="raw_checkpoints/step_20000.pt",
+)
+ckpt = torch.load(path, map_location="cpu", weights_only=False)
+# ckpt["model"]: state_dict, ckpt["model_cfg"]: dict, ckpt["step"]: int (or "success" for best.pt)
+```
+
+Note: the schema (`model`, `model_cfg`, `optimizer`, `scheduler`, `step`,
+`best_success`) is internal to the original training script
+([finetune.py](https://github.com/Tool-as-Interface/Tool_as_Interface) of this
+fork). Standard LeRobot tooling does NOT understand `.pt` files — use the
+`config.json`/`model.safetensors` at the repo root for that.
+
 ## Limitations
 
 - Only trained on circular obstacles with radius 15 px and 1–3 per episode.