Improve model card: Add pipeline tag, paper, project page, code links, and sample usage

README.md

@@ -1,10 +1,11 @@
 ---
-license: apache-2.0
 datasets:
 - behavior-1k/2025-challenge-demos
 - IliaLarchenko/behavior_224_rgb
+license: apache-2.0
 tags:
 - robotics
+pipeline_tag: robotics
 ---
 
 This is an intermediate checkpoint that we used in our [1st place solution of the 2025 BEHAVIOR Challenge](https://github.com/IliaLarchenko/behavior-1k-solution).
@@ -13,10 +14,119 @@ This checkpoint is obtained by training the policy on 50 tasks simultaneously fo
 
 It is not part of our [final submission](https://huggingface.co/IliaLarchenko/behavior_submission). Also, we didn't run the whole evaluation of this checkpoint, but we would expect it to achieve a 15-20% q-score.
 
-Our [tech report](https:///arxiv.org/abs/2512.06951)
+Paper: [Task adaptation of Vision-Language-Action model: 1st Place Solution for the 2025 BEHAVIOR Challenge](https://huggingface.co/papers/2512.06951)
+Project page: https://behavior.stanford.edu/challenge/
+Code/GitHub Repository: [IliaLarchenko/behavior-1k-solution](https://github.com/IliaLarchenko/behavior-1k-solution)
+arXiv: [2512.06951](https://arxiv.org/abs/2512.06951)
 
 The [final submission checkpoints](https://huggingface.co/IliaLarchenko/behavior_submission)
 
+## Sample Usage
+
+This section provides a quick overview of how to get started with the model, adapted from the [GitHub repository](https://github.com/IliaLarchenko/behavior-1k-solution).
+
+### Installation
+
+```bash
+# Clone with submodules (includes openpi and BEHAVIOR-1K)
+git clone --recurse-submodules https://github.com/ilialarchenko/behavior-1k-solution.git
+cd behavior-1k-solution
+
+# Run setup script (installs uv, dependencies, and sets up environment)
+bash setup_remote.sh
+```
+
+### Dataset Preparation
+
+Download the official BEHAVIOR-1K dataset from HuggingFace:
+
+```bash
+# Login to HuggingFace (need to avoid request rate limit errors)
+uv run huggingface-cli login
+
+# Download the full dataset (~2TB)
+uv run python - <<'PY'
+from huggingface_hub import snapshot_download
+snapshot_download(
+  repo_id="behavior-1k/2025-challenge-demos",
+  repo_type="dataset",
+  local_dir="./data/behavior_dataset",
+  local_dir_use_symlinks=False
+)
+PY
+```
+
+**Alternative**: Use the resized RGB-only dataset (224×224, ~260GB) for faster training:
+```bash
+uv run python - <<'PY'
+from huggingface_hub import snapshot_download
+snapshot_download(
+  repo_id="IliaLarchenko/behavior_224_rgb",
+  repo_type="dataset",
+  local_dir="./data/behavior_224_rgb",
+  local_dir_use_symlinks=False
+)
+PY
+```
+
+### Pre-training Setup
+
+Compute dataset statistics and train FAST tokenizer:
+
+```bash
+# Compute normalization statistics with correlation matrix
+uv run scripts/compute_norm_stats.py --config-name pi_behavior_b1k_fast --correlation
+
+# Train FAST tokenizer for action discretization
+uv run scripts/train_fast_tokenizer.py \
+  --config-name pi_behavior_b1k_fast \
+  --encoded-dims="0:6,7:23" \
+  --vocab-size=1024
+```
+
+### Training
+
+**Single GPU Training**:
+```bash
+uv run scripts/train.py pi_behavior_b1k_fast \
+  --batch_size=16 \
+  --num_train_steps=200000 \
+  --save_interval=2000 \
+  --keep_period=10000 \
+  --log_interval=100
+```
+
+**Multi-GPU Training**:
+```bash
+uv run scripts/train.py pi_behavior_b1k_fast \
+  --batch_size=2048 \
+  --num_train_steps=200000 \
+  --fsdp_devices=8 \
+  --save_interval=250 \
+  --keep_period=4000 \
+  --log_interval=25
+```
+
+### Evaluation
+
+Start the policy server:
+
+```bash
+uv run scripts/serve_b1k.py policy:checkpoint \
+  --policy.config pi_behavior_b1k_fast \
+  --policy.dir /path/to/checkpoint
+```
+
+In a separate terminal, [run evaluation](https://behavior.stanford.edu/challenge/baselines.html) (requires BEHAVIOR-1K environment):
+
+```bash
+python BEHAVIOR-1K/omnigibson/learning/eval.py \
+  log_path=./eval_logs \
+  policy=websocket \
+  task.name=make_microwave_popcorn \
+  model.host=localhost \
+  eval_instance_ids="[0,1,2,3]"
+```
 
 ## Citation