Create README.md

README.md

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+---
+language:
+- en
+library_name: lerobot
+pipeline_tag: robotics
+tags:
+- vision-language-action
+- imitation-learning
+- lerobot
+inference: false
+---
+
+# SmolVLA (LeRobot)
+
+SmolVLA is a compact, efficient Vision-Language-Action (VLA) model designed for affordable robotics, trainable on a single GPU and deployable on consumer hardware, while matching the performance of much larger VLAs through community-driven data.
+
+**Original paper:** (SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics)[https://arxiv.org/abs/2506.01844]
+**Reference implementation:** https://github.com/huggingface/lerobot
+
+
+## Model description
+
+- **Inputs:** images (multi-view), proprio/state, optional language instruction
+- **Outputs:** continuous actions
+- **Training objective:** flow matching
+- **Action representation:** continuous
+- **Intended use:** Base model to fine tune on your specific use case
+
+
+## Quick start (inference on a real batch)
+
+### Installation
+
+```bash
+pip install "lerobot[smolvla]"
+```
+For full installation details (including optional video dependencies such as ffmpeg for torchcodec), see the official documentation: https://huggingface.co/docs/lerobot/installation
+
+### Load model + dataset, run `select_action`
+
+```python
+import torch
+from lerobot.datasets.lerobot_dataset import LeRobotDataset
+from lerobot.policies.factory import make_pre_post_processors
+
+# Swap this import per-policy
+from lerobot.policies.smolvla.modeling_smolvla import SmolVLAPolicy
+
+# load a policy
+model_id = "lerobot/smolvla_base"  # <- swap checkpoint
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+policy = SmolVLAPolicy.from_pretrained(model_id).to(device).eval()
+
+preprocess, postprocess = make_pre_post_processors(
+    policy.config,
+    model_id,
+    preprocessor_overrides={"device_processor": {"device": str(device)}},
+)
+# load a lerobotdataset
+dataset = LeRobotDataset("lerobot/libero")
+
+# pick an episode
+episode_index = 0
+
+# each episode corresponds to a contiguous range of frame indices
+from_idx = dataset.meta.episodes["dataset_from_index"][episode_index]
+to_idx   = dataset.meta.episodes["dataset_to_index"][episode_index]
+
+# get a single frame from that episode (e.g. the first frame)
+frame_index = from_idx
+frame = dict(dataset[frame_index])
+
+batch = preprocess(frame)
+with torch.inference_mode():
+    pred_action = policy.select_action(frame)
+    # use your policy postprocess, this post process the action
+    # for instance unnormalize the actions, detokenize it etc..
+    pred_action = postprocess(pred_action)
+```
+
+
+## Training step (loss + backward)
+
+If you’re training / fine-tuning, you typically call `forward(...)` to get a loss and then:
+
+```python
+policy.train()
+batch = dict(dataset[0])
+batch = preprocess(batch)
+
+loss, outputs = policy.forward(batch)
+loss.backward()
+
+```
+
+> Notes:
+> 
+> - Some policies expose `policy(**batch)` or return a dict; keep this snippet aligned with the policy API.
+> - Use your trainer script (`lerobot-train`) for full training loops.
+
+
+## How to train / fine-tune
+
+```bash
+lerobot-train \
+  --dataset.repo_id=${HF_USER}/<dataset> \
+  --output_dir=./outputs/[RUN_NAME] \
+  --job_name=[RUN_NAME] \
+  --policy.repo_id=${HF_USER}/<desired_policy_repo_id> \
+  --policy.path=lerobot/[BASE_CHECKPOINT] \
+  --policy.dtype=bfloat16 \
+  --policy.device=cuda \
+  --steps=100000 \
+  --batch_size=4
+```
+
+Add policy-specific flags below:
+
+- `-policy.chunk_size=...`
+- `-policy.n_action_steps=...`
+- `-policy.max_action_tokens=...`
+- `-policy.gradient_checkpointing=true`
+
+
+## Real-World Inference & Evaluation
+
+You can use the `record` script from [**`lerobot-record`**](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/lerobot_record.py) with a policy checkpoint as input, to run inference and evaluate your policy. 
+
+For instance, run this command or API example to run inference and record 10 evaluation episodes:
+
+```
+lerobot-record  \
+  --robot.type=so100_follower \
+  --robot.port=/dev/ttyACM1 \
+  --robot.cameras="{ up: {type: opencv, index_or_path: /dev/video10, width: 640, height: 480, fps: 30}, side: {type: intelrealsense, serial_number_or_name: 233522074606, width: 640, height: 480, fps: 30}}" \
+  --robot.id=my_awesome_follower_arm \
+  --display_data=false \
+  --dataset.repo_id=${HF_USER}/eval_so100 \
+  --dataset.single_task="Put lego brick into the transparent box" \
+  # <- Teleop optional if you want to teleoperate in between episodes \
+  # --teleop.type=so100_leader \
+  # --teleop.port=/dev/ttyACM0 \
+  # --teleop.id=my_awesome_leader_arm \
+  --policy.path=${HF_USER}/my_policy
+```