---
license: apache-2.0
tags:
  - robotics
  - vla
  - pi0
  - droid
  - molmobot
  - lora
---

# MolmoBot-Pi0 — DROID-toys, absolute joint, r64 LoRA (merged)

Whole-model rank-64 LoRA fine-tune of
[MolmoBot-Pi0-DROID](https://huggingface.co/shrg7/MolmoBot-Pi0-DROID-equad) on
[`shrg7/droid-toys`](https://huggingface.co/datasets/shrg7/droid-toys), with
**absolute joint-position** action targets.

**The LoRA has already been merged into the base weights.** The uploaded
`model.safetensors` is flat (777 tensors, identical key set to the full-FT
counterpart) and loads directly into a plain `PI0Pytorch` — no PEFT, no adapter
folding needed at load time.

## Checkpoint

- **Step 3500**, `val_action_loss = 0.0274`
- Merged with `scripts/merge_pi0_lora.py` (folds `lora_B @ lora_A * alpha/r`, α/r = 128/64 = 2.0; 333 modules folded)
- `assets/` holds the norm stats; `metadata.pt` the run metadata
- Optimizer state is **not** included (inference/eval only, no resume)

## Training

| | |
|---|---|
| Config | `molmobot_pi0_lerobot_droid_absjoint` |
| Dataset | `shrg7/droid-toys` (LeRobot v3.0) — 31 episodes / 6,952 frames @15fps |
| Action repr | `joint_absolute` — targets are future absolute joint positions `q[t+1]`, 8-dim (7 arm + gripper) |
| Action horizon | 16 |
| Cameras | `exterior_1_left` (exo) + `wrist_left`, 224×224 resize-with-pad |
| LoRA | r=64, α=128, dropout 0.05, targets `q/k/v/o/gate/up/down_proj` across the VLM + action expert |
| Trainable | 118,112,256 params (3.26% of 3.62B); `vision_tower` frozen |
| Batch | 32, 1× NVIDIA L40 |
| LR | 5e-5 constant (300-step warmup; `peak_lr == decay_lr`, so the cosine is flat) |
| Steps | 3,500 of a planned 8,000 |

### Validation curve

| step | val_action_loss |
|---|---|
| 2000 | 0.0331 |
| 2500 | 0.0313 |
| 3000 | 0.0273 |
| 3500 | **0.0274** |

## Caveats

This checkpoint is **under-trained**: the run was cut short by a wall-clock limit
at 3,500 of 8,000 steps, with the loss curve still descending steeply.

Do **not** read the gap against the full-FT checkpoint
([`MolmoBot-Pi0-DROID-toys-absjoint-fullft`](https://huggingface.co/shrg7/MolmoBot-Pi0-DROID-toys-absjoint-fullft),
val 0.0079) as a LoRA-vs-full-FT result. That run completed its full schedule at
4× the effective batch; the two are not comparable at this point in training.