Feat: Improve hub integration (#1382)

* feat(policies): Initial setup to push policies to hub with tags and model card

* feat: add dataset that is used to train

* Add model template summary

* fix: Update link model_card template

* fix: remove print

* fix: change import name

* fix: add model summary in template

* fix: minor text

* fix: comments Lucain

* fix: feedback steven

* fix: restructure push to hub

* fix: remove unneeded changes

* fix: import

* fix: import 2

* Add MANIFEST.in

* fix: feedback pr

* Fix tests

* tests: Add smolvla end-to-end test

* Fix: smolvla test

* fix test name

* fix policy tests

* Add push to hub false policy tests

* Do push to hub cleaner

* fix(ci): add push_to_hub false in tests

---------

Co-authored-by: Steven Palma <steven.palma@huggingface.co>

docs/source/il_robots.mdx

@@ -255,7 +255,8 @@ python lerobot/scripts/train.py \
   --output_dir=outputs/train/act_so101_test \
   --job_name=act_so101_test \
   --policy.device=cuda \
-  --wandb.enable=true
+  --wandb.enable=true \
+  --policy.repo_id=${HF_USER}/my_policy
 ```
 
 Let's explain the command:
@@ -273,6 +274,10 @@ python lerobot/scripts/train.py \
   --resume=true
 ```
 
+If you do not want to push your model to the hub after training use `--policy.push_to_hub=false`.
+
+Additionally you can provide extra `tags` or specify a `license` for your model or make the model repo `private` by adding this: `--policy.private=true --policy.tags=\[ppo,rl\] --policy.license=mit`
+
 #### Train using Collab
 If your local computer doesn't have a powerful GPU you could utilize Google Collab to train your model by following the [ACT training notebook](./notebooks#training-act).