Update README.md

README.md

@@ -1,5 +1,5 @@
 ---
-datasets: bdhillon/PI-0.5-11.19.2025-v3
+datasets: bdhillon/PI-0.5-11.19.2025-v3-quantiles
 library_name: lerobot
 license: apache-2.0
 model_name: pi05
@@ -10,33 +10,53 @@ tags:
 - pi05
 ---
 
-# Model Card for pi05
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Training Config:
+
+```python
+CONFIG = {
+  # Dataset (pre-converted v3.0 format)
+  "dataset_repo_id": "bdhillon/PI-0.5-11.19.2025-v3-quantiles",
+  "dataset_root": os.path.expanduser("~/lerobot-training/dataset/PI-0.5-11.19.2025-v3-quantiles"),
+
+  # Model
+  "policy_type": "pi05",
+  "pretrained_path": "lerobot/pi05_base",
+
+  # HuggingFace upload settings
+  "repo_id": "bdhillon/PIv1",
+  "push_to_hub": True,
+
+  # Training hyperparameters
+  "batch_size": 4,
+  "policy.dtype": "bfloat16",
+  "policy.use_amp": True,
+  "steps": 1500,           # ~3-4 epochs for 11 episodes with 6953 frames
+  "eval_freq": 250,        # Evaluate every 250 steps
+  "log_freq": 50,          # Log to WandB every 50 steps
+  "save_freq": 250,        # Save checkpoint every 250 steps
+
+  # Evaluation settings
+  "eval_n_episodes": 5,
+  "eval_batch_size": 5,    # Must be <= eval_n_episodes
+
+  # Output
+  "output_dir": "./PIv1",
+
+  # Logging
+  "wandb_enable": True,
+}
+```
 
 **π₀.₅ (Pi05) Policy**
 
 π₀.₅ is a Vision-Language-Action model with open-world generalization, from Physical Intelligence. The LeRobot implementation is adapted from their open source OpenPI repository.
 
-**Model Overview**
-
-π₀.₅ represents a significant evolution from π₀, developed by Physical Intelligence to address a big challenge in robotics: open-world generalization. While robots can perform impressive tasks in controlled environments, π₀.₅ is designed to generalize to entirely new environments and situations that were never seen during training.
-
 For more details, see the [Physical Intelligence π₀.₅ blog post](https://www.physicalintelligence.company/blog/pi05).
 
-
-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
+### Train From Scratch
 
 ```bash
 lerobot-train \
@@ -51,7 +71,7 @@ lerobot-train \
 
 _Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
 
-### Evaluate the policy/run inference
+### Evaluate / Run inference
 
 ```bash
 lerobot-record \
@@ -64,7 +84,3 @@ lerobot-record \
 Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
 
 ---
-
-## Model Details
-
-- **License:** apache-2.0