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smallvla/SmolVLM2-500M-Video-Instruct/README.md

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+---
+library_name: transformers
+license: apache-2.0
+datasets:
+- HuggingFaceM4/the_cauldron
+- HuggingFaceM4/Docmatix
+- lmms-lab/LLaVA-OneVision-Data
+- lmms-lab/M4-Instruct-Data
+- HuggingFaceFV/finevideo
+- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
+- lmms-lab/LLaVA-Video-178K
+- orrzohar/Video-STaR
+- Mutonix/Vript
+- TIGER-Lab/VISTA-400K
+- Enxin/MovieChat-1K_train
+- ShareGPT4Video/ShareGPT4Video
+pipeline_tag: image-text-to-text
+language:
+- en
+base_model:
+- HuggingFaceTB/SmolVLM-500M-Instruct
+---
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/SmolVLM2_banner.png" width="800" height="auto" alt="Image description">
+
+# SmolVLM2-500M-Video
+
+SmolVLM2-500M-Video is a lightweight multimodal model designed to analyze video content. The model processes videos, images, and text inputs to generate text outputs - whether answering questions about media files, comparing visual content, or transcribing text from images. Despite its compact size, requiring only 1.8GB of GPU RAM for video inference, it delivers robust performance on complex multimodal tasks. This efficiency makes it particularly well-suited for on-device applications where computational resources may be limited.
+## Model Summary
+
+- **Developed by:** Hugging Face 🤗
+- **Model type:** Multi-modal model (image/multi-image/video/text)
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+- **Architecture:** Based on [Idefics3](https://huggingface.co/HuggingFaceM4/Idefics3-8B-Llama3) (see technical summary)
+
+## Resources
+
+- **Demo:** [Video Highlight Generator](https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator)
+- **Blog:** [Blog post](https://huggingface.co/blog/smolvlm2)
+
+## Uses
+
+SmolVLM2 can be used for inference on multimodal (video / image / text) tasks where the input consists of text queries along with video or one or more images. Text and media files can be interleaved arbitrarily, enabling tasks like captioning, visual question answering, and storytelling based on visual content. The model does not support image or video generation.
+
+To fine-tune SmolVLM2 on a specific task, you can follow [the fine-tuning tutorial](https://github.com/huggingface/smollm/blob/main/vision/finetuning/Smol_VLM_FT.ipynb).
+
+## Evaluation 
+
+We evaluated the performance of the SmolVLM2 family on the following scientific benchmarks:
+
+| Size    | Video-MME | MLVU | MVBench |
+|----------|-----------------|----------|---------------|
+| 2.2B   | 52.1            | 55.2     | 46.27        |
+| 500M | 42.2            | 47.3     | 39.73        |
+| 256M | 33.7            | 40.6     | 32.7          |
+
+
+### How to get started
+
+You can use transformers to load, infer and fine-tune SmolVLM. Make sure you have num2words, flash-attn and latest transformers installed.
+You can load the model as follows.
+
+```python
+from transformers import AutoProcessor, AutoModelForImageTextToText
+import torch
+
+model_path = "HuggingFaceTB/SmolVLM2-500M-Video-Instruct"
+processor = AutoProcessor.from_pretrained(model_path)
+model = AutoModelForImageTextToText.from_pretrained(
+    model_path,
+    torch_dtype=torch.bfloat16,
+    _attn_implementation="flash_attention_2"
+).to("cuda")
+```
+
+#### Simple Inference
+
+You preprocess your inputs directly using chat templates and directly passing them 
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+            {"type": "text", "text": "Can you describe this image?"},
+        ]
+    },
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device, dtype=torch.bfloat16)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+print(generated_texts[0])
+```
+
+#### Video Inference
+
+To use SmolVLM2 for video inference, make sure you have decord installed. 
+
+```python
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "video", "path": "path_to_video.mp4"},
+            {"type": "text", "text": "Describe this video in detail"}
+        ]
+    },
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device, dtype=torch.bfloat16)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+
+print(generated_texts[0])
+```
+#### Multi-image Interleaved Inference
+
+You can interleave multiple media with text using chat templates.
+
+```python
+import torch
+
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+          {"type": "text", "text": "What is the similarity between these two images?"},
+          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg"},
+          {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"},            
+        ]
+    },
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt",
+).to(model.device, dtype=torch.bfloat16)
+
+generated_ids = model.generate(**inputs, do_sample=False, max_new_tokens=64)
+generated_texts = processor.batch_decode(
+    generated_ids,
+    skip_special_tokens=True,
+)
+print(generated_texts[0])
+```
+
+
+### Model optimizations
+
+## Misuse and Out-of-scope Use
+
+SmolVLM is not intended for high-stakes scenarios or critical decision-making processes that affect an individual's well-being or livelihood. The model may produce content that appears factual but may not be accurate. Misuse includes, but is not limited to:
+
+- Prohibited Uses:
+  - Evaluating or scoring individuals (e.g., in employment, education, credit)
+  - Critical automated decision-making
+  - Generating unreliable factual content
+- Malicious Activities:
+  - Spam generation
+  - Disinformation campaigns
+  - Harassment or abuse
+  - Unauthorized surveillance
+
+### License
+
+SmolVLM2 is built upon [SigLIP](https://huggingface.co/google/siglip-base-patch16-512) as image encoder and [SmolLM2](https://huggingface.co/HuggingFaceTB/SmolLM2-360M-Instruct) for text decoder part.
+
+We release the SmolVLM2 checkpoints under the Apache 2.0 license.
+
+## Citation information
+You can cite us in the following way:
+```bibtex
+@article{marafioti2025smolvlm,
+  title={SmolVLM: Redefining small and efficient multimodal models}, 
+  author={Andrés Marafioti and Orr Zohar and Miquel Farré and Merve Noyan and Elie Bakouch and Pedro Cuenca and Cyril Zakka and Loubna Ben Allal and Anton Lozhkov and Nouamane Tazi and Vaibhav Srivastav and Joshua Lochner and Hugo Larcher and Mathieu Morlon and Lewis Tunstall and Leandro von Werra and Thomas Wolf},
+  journal={arXiv preprint arXiv:2504.05299},
+  year={2025}
+}
+```
+
+## Training Data
+SmolVLM2 used 3.3M samples for training originally from ten different datasets: [LlaVa Onevision](https://huggingface.co/datasets/lmms-lab/LLaVA-OneVision-Data), [M4-Instruct](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data), [Mammoth](https://huggingface.co/datasets/MAmmoTH-VL/MAmmoTH-VL-Instruct-12M), [LlaVa Video 178K](https://huggingface.co/datasets/lmms-lab/LLaVA-Video-178K), [FineVideo](https://huggingface.co/datasets/HuggingFaceFV/finevideo), [VideoStar](https://huggingface.co/datasets/orrzohar/Video-STaR), [VRipt](https://huggingface.co/datasets/Mutonix/Vript), [Vista-400K](https://huggingface.co/datasets/TIGER-Lab/VISTA-400K), [MovieChat](https://huggingface.co/datasets/Enxin/MovieChat-1K_train) and [ShareGPT4Video](https://huggingface.co/datasets/ShareGPT4Video/ShareGPT4Video).
+In the following plots we give a general overview of the samples across modalities and the source of those samples.
+<!--
+<center><img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_data_split.png" width="auto" height="auto" alt="Image description">
+</center>
+
+### Details
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm2_datadetails.png" width="auto" height="auto" alt="Image description"> -->
+
+## Data Split per modality
+
+| Data Type    | Percentage |
+|--------------|------------|
+| Image        | 34.4%      |
+| Text         | 20.2%      |
+| Video        | 33.0%      |
+| Multi-image  | 12.3%      |
+
+
+## Granular dataset slices per modality
+
+### Text Datasets
+| Dataset                                    | Percentage |
+|--------------------------------------------|------------|
+| llava-onevision/magpie_pro_ft3_80b_mt      | 6.8%       |
+| llava-onevision/magpie_pro_ft3_80b_tt      | 6.8%       |
+| llava-onevision/magpie_pro_qwen2_72b_tt    | 5.8%       |
+| llava-onevision/mathqa                     | 0.9%       |
+
+### Multi-image Datasets
+| Dataset                                    | Percentage |
+|--------------------------------------------|------------|
+| m4-instruct-data/m4_instruct_multiimage    | 10.4%      |
+| mammoth/multiimage-cap6                    | 1.9%       |
+
+### Image Datasets
+| Dataset                                    | Percentage |
+|--------------------------------------------|------------|
+| llava-onevision/other                      | 17.4%      |
+| llava-onevision/vision_flan                | 3.9%       |
+| llava-onevision/mavis_math_metagen         | 2.6%       |
+| llava-onevision/mavis_math_rule_geo        | 2.5%       |
+| llava-onevision/sharegpt4o                 | 1.7%       |
+| llava-onevision/sharegpt4v_coco            | 1.5%       |
+| llava-onevision/image_textualization       | 1.3%       |
+| llava-onevision/sharegpt4v_llava           | 0.9%       |
+| llava-onevision/mapqa                      | 0.9%       |
+| llava-onevision/qa                         | 0.8%       |
+| llava-onevision/textocr                    | 0.8%       |
+
+### Video Datasets
+| Dataset                                    | Percentage |
+|--------------------------------------------|------------|
+| llava-video-178k/1-2m                      | 7.3%       |
+| llava-video-178k/2-3m                      | 7.0%       |
+| other-video/combined                       | 5.7%       |
+| llava-video-178k/hound                     | 4.4%       |
+| llava-video-178k/0-30s                     | 2.4%       |
+| video-star/starb                           | 2.2%       |
+| vista-400k/combined                        | 2.2%       |
+| vript/long                                 | 1.0%       |
+| ShareGPT4Video/all                         | 0.8%       |

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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<fake_token_around_image>",
+    "<image>",
+    "<end_of_utterance>"
+  ],
+  "bos_token": "<|im_start|>",
+  "chat_template": "<|im_start|>{% for message in messages %}{{message['role'] | capitalize}}{% if message['content'][0]['type'] == 'image' %}{{':'}}{% else %}{{': '}}{% endif %}{% for line in message['content'] %}{% if line['type'] == 'text' %}{{line['text']}}{% elif line['type'] == 'image' %}{{ '<image>' }}{% endif %}{% endfor %}<end_of_utterance>\n{% endfor %}{% if add_generation_prompt %}{{ 'Assistant:' }}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "end_of_utterance_token": "<end_of_utterance>",
+  "eos_token": "<end_of_utterance>",
+  "extra_special_tokens": {
+    "end_of_utterance_token": "<end_of_utterance>",
+    "fake_image_token": "<fake_token_around_image>",
+    "global_image_token": "<global-img>",
+    "image_token": "<image>"
+  },
+  "fake_image_token": "<fake_token_around_image>",
+  "global_image_token": "<global-img>",
+  "image_token": "<image>",
+  "legacy": false,
+  "model_max_length": 8192,
+  "pad_token": "<|im_end|>",
+  "processor_class": "SmolVLMProcessor",
+  "tokenizer_class": "GPT2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": "<|endoftext|>",
+  "vocab_size": 49152
+}

smallvla/smolvla_base/.gitattributes

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smallvla/smolvla_base/.hfd/last_download_command

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smallvla/smolvla_base/.hfd/repo_metadata.json

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+{"_id":"683c069f3f2842f6afffe5ff","id":"lerobot/smolvla_base","private":false,"pipeline_tag":"robotics","library_name":"lerobot","tags":["lerobot","safetensors","smolvla","robotics","dataset:lerobot/svla_so101_pickplace","arxiv:2506.01844","region:us"],"downloads":17169,"likes":234,"modelId":"lerobot/smolvla_base","author":"lerobot","sha":"3326b100334ffc0a0bd1ec27e3afb1cfa2a6000c","lastModified":"2025-06-24T08:08:14.000Z","gated":false,"disabled":false,"model-index":null,"config":{},"cardData":{"pipeline_tag":"robotics","tags":["smolvla"],"library_name":"lerobot","datasets":["lerobot/svla_so101_pickplace"]},"siblings":[{"rfilename":".gitattributes"},{"rfilename":"Finetune_SmolVLA_notebook.ipynb"},{"rfilename":"README.md"},{"rfilename":"collage_small.gif"},{"rfilename":"config.json"},{"rfilename":"model.safetensors"},{"rfilename":"train_config.json"}],"spaces":["arpitg1304/lerobot_scripts_simplified"],"createdAt":"2025-06-01T07:51:59.000Z","usedStorage":914729394}

smallvla/smolvla_base/Finetune_SmolVLA_notebook.ipynb

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+{
+  "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
+}

smallvla/smolvla_base/README.md

@@ -0,0 +1,60 @@
+---
+pipeline_tag: robotics
+tags:
+- smolvla
+library_name: lerobot
+datasets:
+- lerobot/svla_so101_pickplace
+---
+
+## SmolVLA: A vision-language-action model for affordable and efficient robotics
+
+Resources and technical documentation:
+
+[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)
+
+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]"
+```
+
+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
+```
+
+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
+```

smallvla/smolvla_base/config.json

@@ -0,0 +1,86 @@
+{
+    "type": "smolvla",
+    "n_obs_steps": 1,
+    "normalization_mapping": {
+        "VISUAL": "IDENTITY",
+        "STATE": "MEAN_STD",
+        "ACTION": "MEAN_STD"
+    },
+    "input_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
+            ]
+        }
+    },
+    "output_features": {
+        "action": {
+            "type": "ACTION",
+            "shape": [
+                6
+            ]
+        }
+    },
+    "chunk_size": 50,
+    "n_action_steps": 50,
+    "max_state_dim": 32,
+    "max_action_dim": 32,
+    "resize_imgs_with_padding": [
+        512,
+        512
+    ],
+    "empty_cameras": 0,
+    "adapt_to_pi_aloha": false,
+    "use_delta_joint_actions_aloha": false,
+    "tokenizer_max_length": 48,
+    "num_steps": 10,
+    "use_cache": true,
+    "freeze_vision_encoder": false,
+    "train_expert_only": false,
+    "train_state_proj": true,
+    "optimizer_lr": 0.0001,
+    "optimizer_betas": [
+        0.9,
+        0.95
+    ],
+    "optimizer_eps": 1e-08,
+    "optimizer_weight_decay": 1e-10,
+    "optimizer_grad_clip_norm": 10,
+    "scheduler_warmup_steps": 1000,
+    "scheduler_decay_steps": 30000,
+    "scheduler_decay_lr": 2.5e-06,
+    "vlm_model_name": "/mnt/pfs/users/chaojun.ni/peking-human/robotwin/XDG_CACHE/smallvla/SmolVLM2-500M-Video-Instruct",
+    "load_vlm_weights": true,
+    "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
+}

smallvla/smolvla_base/model.safetensors

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