---
license: apache-2.0
tags:
  - robotics
  - vla
  - quantization
  - w4a4
  - pi0.5
  - libero
---

# Omega-QVLA — pi0.5 LIBERO W4A4 quantization packs

W4A4 quantization packs for the **pi0.5 (openpi) LIBERO** action policies, produced
by [Omega-QVLA](https://github.com/UCMP13753/Omega-QVLA).

## Recipe (per side)

| side | rotation | quantizer | per-step |
|------|----------|-----------|----------|
| **PaliGemma** backbone | DuQuant `svd_hadamard` | **GPTQ** | single bucket |
| **Gemma Expert** (action head) | DuQuant `svd_hadamard` | **RTN** residual | **yes** (`act_scale_table`, 10 steps) |

All weights are **W4**, activations **A4**. The DuQuant rotation is stored in
compact **block form** (`duquant_rotation_blocks` + `duquant_rotation_perm`).

## What these files are

These are **quantization packs**, NOT standalone models. Each `quantized.pt` is a
dict keyed by layer name; every record holds the quantized weight
(`weight_res_q` / `baseline_q`), the block-form rotation, and (Expert side) the
per-step `act_scale_table`. They are loaded at inference time **on top of the
original pi0.5 FP checkpoint** by Omega-QVLA's `GptqLinear`. You cannot
`from_pretrained` them directly.

| file | suite | records |
|------|-------|---------|
| `pi05_object/quantized.pt`  | libero_object  | 252 (126 PaliGemma + 126 Expert) |
| `pi05_spatial/quantized.pt` | libero_spatial | 252 |
| `pi05_goal/quantized.pt`    | libero_goal    | 252 |
| `pi05_long/quantized.pt`    | libero_10      | 252 |

## Usage

```bash
# 1. Get the repo + the original pi0.5 PyTorch checkpoint
git clone https://github.com/UCMP13753/Omega-QVLA && cd Omega-QVLA

SUITE=object
PACK=/path/to/pi05_${SUITE}/quantized.pt
INCLUDE_BOTH='.*paligemma_with_expert\.(paligemma\.model\.language_model|gemma_expert\.model)\.layers\.[0-9]+\..*\.(q_proj|k_proj|v_proj|o_proj|gate_proj|up_proj|down_proj).*'

env CONDA_ROOT=$HOME/miniconda3 METHOD=gptq SUITE=$SUITE WBITS=4 ABITS=4 \
    GPU_LIST=0,1 PORT_BASE=8600 NUM_TRIALS_PER_TASK=10 GR00T_EVAL_INIT_OFFSET=10 \
    OPENPI_ROOT=$HOME/openpi OPENPI_PY=$HOME/openpi/.venv/bin/python \
    OPENPI_CONFIG=pi05_libero OPENPI_CHECKPOINT=/path/to/pi05_libero_pytorch \
    OPENPI_GPTQ_PATH="$PACK" OPENPI_GPTQ_INCLUDE="$INCLUDE_BOTH" \
    OUTPUT_ROOT=results/eval/pi05_${SUITE} \
    bash scripts/run_pi05_libero_benchmark.sh
```

Both sides load through the GPTQ pack path (`GptqLinear`); the block-form rotation
and per-step scales are applied automatically.