Ban

.gitattributes

@@ -33,4 +33,3 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
-collage_small.gif filter=lfs diff=lfs merge=lfs -text

Finetune_SmolVLA_notebook.ipynb

@@ -1,214 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "NQUk3Y0WwYZ4"
-      },
-      "source": [
-        "# 🤗 x 🦾: Training SmolVLA with LeRobot Notebook\n",
-        "\n",
-        "Welcome to the **LeRobot SmolVLA training notebook**! This notebook provides a ready-to-run setup for training imitation learning policies using the [🤗 LeRobot](https://github.com/huggingface/lerobot) library.\n",
-        "\n",
-        "In this example, we train an `SmolVLA` policy using a dataset hosted on the [Hugging Face Hub](https://huggingface.co/), and optionally track training metrics with [Weights & Biases (wandb)](https://wandb.ai/).\n",
-        "\n",
-        "## ⚙️ Requirements\n",
-        "- A Hugging Face dataset repo ID containing your training data (`--dataset.repo_id=YOUR_USERNAME/YOUR_DATASET`)\n",
-        "- Optional: A [wandb](https://wandb.ai/) account if you want to enable training visualization\n",
-        "- Recommended: GPU runtime (e.g., NVIDIA A100) for faster training\n",
-        "\n",
-        "## ⏱️ Expected Training Time\n",
-        "Training with the `SmolVLA` policy for 20,000 steps typically takes **about 5 hours on an NVIDIA A100** GPU. On less powerful GPUs or CPUs, training may take significantly longer!\n",
-        "\n",
-        "## Example Output\n",
-        "Model checkpoints, logs, and training plots will be saved to the specified `--output_dir`. If `wandb` is enabled, progress will also be visualized in your wandb project dashboard.\n"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "MOJyX0CnwA5m"
-      },
-      "source": [
-        "## Install conda\n",
-        "This cell uses `condacolab` to bootstrap a full Conda environment inside Google Colab.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "QlKjL1X5t_zM"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q condacolab\n",
-        "import condacolab\n",
-        "condacolab.install()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "DxCc3CARwUjN"
-      },
-      "source": [
-        "## Install LeRobot\n",
-        "This cell clones the `lerobot` repository from Hugging Face, installs FFmpeg (version 7.1.1), and installs the package in editable mode.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "dgLu7QT5tUik"
-      },
-      "outputs": [],
-      "source": [
-        "!git clone https://github.com/huggingface/lerobot.git\n",
-        "!conda install ffmpeg=7.1.1 -c conda-forge\n",
-        "!cd lerobot && pip install -e ."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Q8Sn2wG4wldo"
-      },
-      "source": [
-        "## Weights & Biases login\n",
-        "This cell logs you into Weights & Biases (wandb) to enable experiment tracking and logging."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "PolVM_movEvp"
-      },
-      "outputs": [],
-      "source": [
-        "!wandb login"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "zTWQAgX9xseE"
-      },
-      "source": [
-        "## Install SmolVLA dependencies"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "DiHs0BKwxseE"
-      },
-      "outputs": [],
-      "source": [
-        "!cd lerobot && pip install -e \".[smolvla]\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "IkzTo4mNwxaC"
-      },
-      "source": [
-        "## Start training SmolVLA with LeRobot\n",
-        "\n",
-        "This cell runs the `train.py` script from the `lerobot` library to train a robot control policy.  \n",
-        "\n",
-        "Make sure to adjust the following arguments to your setup:\n",
-        "\n",
-        "1. `--dataset.repo_id=YOUR_HF_USERNAME/YOUR_DATASET`:  \n",
-        "   Replace this with the Hugging Face Hub repo ID where your dataset is stored, e.g., `pepijn223/il_gym0`.\n",
-        "\n",
-        "2. `--batch_size=64`: means the model processes 64 training samples in parallel before doing one gradient update. Reduce this number if you have a GPU with low memory.\n",
-        "\n",
-        "3. `--output_dir=outputs/train/...`:  \n",
-        "   Directory where training logs and model checkpoints will be saved.\n",
-        "\n",
-        "4. `--job_name=...`:  \n",
-        "   A name for this training job, used for logging and Weights & Biases.\n",
-        "\n",
-        "5. `--policy.device=cuda`:  \n",
-        "   Use `cuda` if training on an NVIDIA GPU. Use `mps` for Apple Silicon, or `cpu` if no GPU is available.\n",
-        "\n",
-        "6. `--wandb.enable=true`:  \n",
-        "   Enables Weights & Biases for visualizing training progress. You must be logged in via `wandb login` before running this."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "ZO52lcQtxseE"
-      },
-      "outputs": [],
-      "source": [
-        "!cd lerobot && python lerobot/scripts/train.py \\\n",
-        "  --policy.path=lerobot/smolvla_base \\\n",
-        "  --dataset.repo_id=${HF_USER}/mydataset \\\n",
-        "  --batch_size=64 \\\n",
-        "  --steps=20000 \\\n",
-        "  --output_dir=outputs/train/my_smolvla \\\n",
-        "  --job_name=my_smolvla_training \\\n",
-        "  --policy.device=cuda \\\n",
-        "  --wandb.enable=true"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2PBu7izpxseF"
-      },
-      "source": [
-        "## Login into Hugging Face Hub\n",
-        "Now after training is done login into the Hugging Face hub and upload the last checkpoint"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8yu5khQGIHi6"
-      },
-      "outputs": [],
-      "source": [
-        "!huggingface-cli login"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "zFMLGuVkH7UN"
-      },
-      "outputs": [],
-      "source": [
-        "!huggingface-cli upload ${HF_USER}/my_smolvla \\\n",
-        "  /content/lerobot/outputs/train/my_smolvla/checkpoints/last/pretrained_model"
-      ]
-    }
-  ],
-  "metadata": {
-    "accelerator": "GPU",
-    "colab": {
-      "gpuType": "A100",
-      "machine_shape": "hm",
-      "provenance": []
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    },
-    "language_info": {
-      "name": "python"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

README.md

@@ -1,33 +1,21 @@
 ---
 pipeline_tag: robotics
 tags:
-- smolvla
+- lerobot
 library_name: lerobot
-datasets:
-- lerobot/svla_so101_pickplace
 ---
 
-## SmolVLA: A vision-language-action model for affordable and efficient robotics
+SmolVLA: A vision-language-action model for affordable and efficient robotics
 
-Resources and technical documentation:
+[Paper](https://huggingface.co/papers/2506.01844)
 
-[SmolVLA Paper](https://huggingface.co/papers/2506.01844)
-
-[SmolVLA Blogpost](https://huggingface.co/blog/smolvla)
-
-[Code](https://github.com/huggingface/lerobot/blob/main/lerobot/common/policies/smolvla/modeling_smolvla.py)
-
-[Train using Google Colab Notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/lerobot/training-smolvla.ipynb#scrollTo=ZO52lcQtxseE)
-
-[SmolVLA HF Documentation](https://huggingface.co/docs/lerobot/smolvla)
+[Code](https://github.com/huggingface/lerobot)
 
 Designed by Hugging Face.
 
 This model has 450M parameters in total.
 You can use inside the [LeRobot library](https://github.com/huggingface/lerobot).
 
-Before proceeding to the next steps, you need to properly install the environment by following [Installation Guide](https://huggingface.co/docs/lerobot/installation) on the docs.
-
 Install smolvla extra dependencies:
 ```bash
 pip install -e ".[smolvla]"
@@ -36,25 +24,23 @@ pip install -e ".[smolvla]"
 Example of finetuning the smolvla pretrained model (`smolvla_base`):
 ```bash
 python lerobot/scripts/train.py \
-  --policy.path=lerobot/smolvla_base \
-  --dataset.repo_id=lerobot/svla_so101_pickplace \
-  --batch_size=64 \
-  --steps=20000 \
-  --output_dir=outputs/train/my_smolvla \
-  --job_name=my_smolvla_training \
-  --policy.device=cuda \
-  --wandb.enable=true
+--policy.path=lerobot/smolvla_base \
+--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--batch_size=64 \
+--steps=200000
 ```
 
 Example of finetuning the smolvla neural network with pretrained VLM and action expert
 intialized from scratch:
 ```bash
 python lerobot/scripts/train.py \
-  --dataset.repo_id=lerobot/svla_so101_pickplace \
-  --batch_size=64 \
-  --steps=200000 \
-  --output_dir=outputs/train/my_smolvla \
-  --job_name=my_smolvla_training \
-  --policy.device=cuda \
-  --wandb.enable=true
+--policy.type=smolvla \
+--dataset.repo_id=danaaubakirova/svla_so100_task1_v3 \
+--batch_size=64 \
+--steps=200000
+```
+
+Example of using the smolvla pretrained model outside LeRobot training framework:
+```python
+policy = SmolVLAPolicy.from_pretrained("lerobot/smolvla_base")
 ```

config.json

@@ -1,6 +1,11 @@
 {
     "type": "smolvla",
     "n_obs_steps": 1,
+    "normalization_mapping": {
+        "VISUAL": "IDENTITY",
+        "STATE": "MEAN_STD",
+        "ACTION": "MEAN_STD"
+    },
     "input_features": {
         "observation.state": {
             "type": "STATE",
@@ -8,7 +13,7 @@
                 6
             ]
         },
-        "observation.images.camera1": {
+        "observation.image2": {
             "type": "VISUAL",
             "shape": [
                 3,
@@ -16,7 +21,7 @@
                 256
             ]
         },
-        "observation.images.camera2": {
+        "observation.image": {
             "type": "VISUAL",
             "shape": [
                 3,
@@ -24,7 +29,7 @@
                 256
             ]
         },
-        "observation.images.camera3": {
+        "observation.image3": {
             "type": "VISUAL",
             "shape": [
                 3,
@@ -41,20 +46,8 @@
             ]
         }
     },
-    "device": "cuda",
-    "use_amp": false,
-    "push_to_hub": true,
-    "repo_id": null,
-    "private": null,
-    "tags": null,
-    "license": null,
     "chunk_size": 50,
-    "n_action_steps": 50,
-    "normalization_mapping": {
-        "VISUAL": "IDENTITY",
-        "STATE": "MEAN_STD",
-        "ACTION": "MEAN_STD"
-    },
+    "n_action_steps": 1,
     "max_state_dim": 32,
     "max_action_dim": 32,
     "resize_imgs_with_padding": [
@@ -83,14 +76,11 @@
     "scheduler_decay_lr": 2.5e-06,
     "vlm_model_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct",
     "load_vlm_weights": true,
-    "add_image_special_tokens": false,
     "attention_mode": "cross_attn",
     "prefix_length": 0,
     "pad_language_to": "max_length",
     "num_expert_layers": 0,
     "num_vlm_layers": 16,
     "self_attn_every_n_layers": 2,
-    "expert_width_multiplier": 0.75,
-    "min_period": 0.004,
-    "max_period": 4.0
-}
+    "expert_width_multiplier": 0.75
+}

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 906712520
+oid sha256:8f8dc071d5b933e79edd2b73b8d6b5cca482ef0437c099ea3ec13ab978a38fc8
+size 906720008

policy_postprocessor.json

@@ -1,32 +0,0 @@
-{
-  "name": "policy_postprocessor",
-  "steps": [
-    {
-      "registry_name": "unnormalizer_processor",
-      "config": {
-        "eps": 1e-08,
-        "features": {
-          "action": {
-            "type": "ACTION",
-            "shape": [
-              6
-            ]
-          }
-        },
-        "norm_map": {
-          "VISUAL": "IDENTITY",
-          "STATE": "MEAN_STD",
-          "ACTION": "MEAN_STD"
-        }
-      },
-      "state_file": "policy_postprocessor_step_0_unnormalizer_processor.safetensors"
-    },
-    {
-      "registry_name": "device_processor",
-      "config": {
-        "device": "cpu",
-        "float_dtype": null
-      }
-    }
-  ]
-}

policy_postprocessor_step_0_unnormalizer_processor.safetensors

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-size 640

policy_preprocessor.json

@@ -1,87 +0,0 @@
-{
-  "name": "policy_preprocessor",
-  "steps": [
-    {
-      "registry_name": "rename_observations_processor",
-      "config": {
-        "rename_map": {}
-      }
-    },
-    {
-      "registry_name": "to_batch_processor",
-      "config": {}
-    },
-    {
-      "registry_name": "smolvla_new_line_processor",
-      "config": {}
-    },
-    {
-      "registry_name": "tokenizer_processor",
-      "config": {
-        "max_length": 48,
-        "task_key": "task",
-        "padding_side": "right",
-        "padding": "max_length",
-        "truncation": true,
-        "tokenizer_name": "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
-      }
-    },
-    {
-      "registry_name": "device_processor",
-      "config": {
-        "device": "cuda",
-        "float_dtype": null
-      }
-    },
-    {
-      "registry_name": "normalizer_processor",
-      "config": {
-        "eps": 1e-08,
-        "features": {
-          "observation.state": {
-            "type": "STATE",
-            "shape": [
-              6
-            ]
-          },
-          "observation.image2": {
-            "type": "VISUAL",
-            "shape": [
-              3,
-              256,
-              256
-            ]
-          },
-          "observation.image": {
-            "type": "VISUAL",
-            "shape": [
-              3,
-              256,
-              256
-            ]
-          },
-          "observation.image3": {
-            "type": "VISUAL",
-            "shape": [
-              3,
-              256,
-              256
-            ]
-          },
-          "action": {
-            "type": "ACTION",
-            "shape": [
-              6
-            ]
-          }
-        },
-        "norm_map": {
-          "VISUAL": "IDENTITY",
-          "STATE": "MEAN_STD",
-          "ACTION": "MEAN_STD"
-        }
-      },
-      "state_file": "policy_preprocessor_step_5_normalizer_processor.safetensors"
-    }
-  ]
-}

policy_preprocessor_step_5_normalizer_processor.safetensors

@@ -1,3 +0,0 @@
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-size 640

train_config.json

@@ -0,0 +1,196 @@
+{
+    "dataset": {
+        "repo_id": 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