Upload policy weights, train config and readme

README.md

@@ -0,0 +1,63 @@
+---
+base_model: lerobot/smolvla_base
+datasets: mimimimi2002/object_force_fixed
+library_name: lerobot
+license: apache-2.0
+model_name: smolvla
+pipeline_tag: robotics
+tags:
+- lerobot
+- robotics
+- smolvla
+---
+
+# Model Card for smolvla
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
+
+
+This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
+See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
+
+---
+
+## How to Get Started with the Model
+
+For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
+Below is the short version on how to train and run inference/eval:
+
+### Train from scratch
+
+```bash
+python -m lerobot.scripts.train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --policy.type=act \
+  --output_dir=outputs/train/<desired_policy_repo_id> \
+  --job_name=lerobot_training \
+  --policy.device=cuda \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id>
+  --wandb.enable=true
+```
+
+_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
+
+### Evaluate the policy/run inference
+
+```bash
+python -m lerobot.record \
+  --robot.type=so100_follower \
+  --dataset.repo_id=<hf_user>/eval_<dataset> \
+  --policy.path=<hf_user>/<desired_policy_repo_id> \
+  --episodes=10
+```
+
+Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
+
+---
+
+## Model Details
+
+- **License:** apache-2.0

config.json

@@ -43,7 +43,7 @@
     "n_action_steps": 50,
     "max_state_dim": 32,
     "max_action_dim": 32,
-    "max_force_dim": 24,
+    "max_force_dim": 12,
     "resize_imgs_with_padding": [
         512,
         512
@@ -80,4 +80,4 @@
     "expert_width_multiplier": 0.75,
     "min_period": 0.004,
     "max_period": 4.0
-}
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:92c5211775a4253bad6a36e3c511a4dcc991f47abb3e4bc47c851ac8c0db2e01
-size 906809216
+oid sha256:55fae1b61837d5b856864ec094d8c82ca26c574cfc461634e4434dfff12d868d
+size 906763136

train_config.json

@@ -110,7 +110,7 @@
         "n_action_steps": 50,
         "max_state_dim": 32,
         "max_action_dim": 32,
-        "max_force_dim": 24,
+        "max_force_dim": 12,
         "resize_imgs_with_padding": [
             512,
             512
@@ -148,7 +148,7 @@
         "min_period": 0.004,
         "max_period": 4.0
     },
-    "output_dir": "outputs/train/2026-01-21/11-03-22_smolvla",
+    "output_dir": "outputs/train/2026-01-23/11-22-58_smolvla",
     "job_name": "smolvla",
     "resume": false,
     "seed": 1000,
@@ -189,7 +189,7 @@
         "project": "smolvla_force_finetune",
         "entity": null,
         "notes": "tactile force smolvla train with force",
-        "run_id": "5jeq3jos",
+        "run_id": "4t4bolc0",
         "mode": "online"
     }
 }