---
license: apache-2.0
base_model: lerobot/Robometer-4B
pipeline_tag: robotics
library_name: coreai
tags:
- coreai
- core-ai
- coreai-fabric
- aimodel
- coreml
- apple
- apple-silicon
- on-device
- robotics
- reward-model
- lerobot
---

> **Canonical:** [`kevinqz/Robometer-4B-CoreAI`](https://huggingface.co/kevinqz/Robometer-4B-CoreAI) — source of truth. 

# Robometer-4B Reward (fabric)

An Apple Core AI conversion of
[lerobot/Robometer-4B](https://huggingface.co/lerobot/Robometer-4B) — the deployable
**reward-head core** of a robot-policy reward model. It maps per-frame
vision-language hidden states to **progress** (a distribution over discrete bins)
and **success** logits, for reward/progress estimation in robot learning.
Produced by [coreai-fabric](https://github.com/kevinqz/coreai-fabric/blob/main/recipes/robometer-4b.yaml) and indexed by
[coreai-catalog](https://github.com/kevinqz/coreai-catalog).

> **Reward heads, not the whole model — this needs the VLM backbone you supply.**
> Following the split discipline of the VLA lanes (EVO1 / VLA-JEPA / pi0), this
> asset ships ONLY the small MLP reward heads. The **host owns the Qwen3-VL
> backbone** (a standard VLM), the `<|prog_token|>` hidden-state extraction, and
> the decode (progress = softmax-weighted bin-mean clamped to `[0,1]`; success =
> sigmoid). Without the backbone + processor the graph is inert. This is a
> conversion-fidelity artifact, **not** a benchmarked reward signal.

## Model facts

| Field | Value |
|---|---|
| Parameters (full model) | 4.45B |
| Architecture | transformer |
| Capabilities | reward-modeling, robotics |
| Hidden dim (VLM) | 2560 |
| Progress bins | 10 |
| Max frames (static) | 8 |
| Outputs | progress_logits, success_logits |
| Quantization / precision | none / float32 |
| On-disk size | 25 MB |
| Asset kind | MLP reward heads (VLM hidden states -> progress + success logits) |
| assetVersion | 2.0 |


## Use it — this needs host code you supply

The bundle is a single static graph: per-frame hidden states
`frame_embeddings [1, T, hidden]` in → `progress_logits [1, T, bins]` +
`success_logits [1, T]` out. **You supply** the Qwen3-VL backbone that produces
those hidden states at the `<|prog_token|>` positions, plus the decode, in your
host code (Swift or Python). Use the upstream repo for the backbone + processor.

```bash
pip install coreai-catalog && coreai-catalog install robometer-4b
```

## Requirements

- **Deployment: macOS 27.0+ / iOS 27.0+, Xcode 27+.** The asset serializes with `minimum_os v27`,
  so the on-device Swift runtime requires macOS/iOS 27+. A Mac on macOS 26 can
  convert and inspect it but not run it on-device.
- Apple Silicon.
- The upstream Qwen3-VL backbone + Robometer processor (host-side) to produce the
  input hidden states.

## Verification (output parity)

- **Gate A (structure): passed** — the bundle's layout + metadata were
  validated; the graph loads.
- **Gate B — graph_output_cosine: 1.000000 min output cosine** (median 1.000000) vs the fp32 torch reward heads over 8 seeded hidden-state inputs (worst of the progress + success heads), measured on apple_silicon. Certifies the export computes the SAME reward-head logits as the source — a conversion-fidelity metric, not reward quality.
- This certifies the export is **numerically faithful to the source reward heads** —
  it does **NOT** certify reward quality or downstream task success. Reproduce with
  `coreai-fabric verify`.

## Provenance

| Field | Value |
|---|---|
| Base model | [lerobot/Robometer-4B](https://huggingface.co/lerobot/Robometer-4B) @ `db167a7c369a3ee59cda801fe33ca9da560b1662` |
| Converted by | `models/robometer/export.py` (version not reported) |
| Recipe | [robometer-4b](https://github.com/kevinqz/coreai-fabric/blob/main/recipes/robometer-4b.yaml) (recipe_source: fabric) |
| Precision / quantization | float32 / none |
| Conversion date | 2026-07-07 |

Machine-readable, in this repo:
[`parity-report.json`](./parity-report.json) ·
[`reproduce-manifest.json`](./reproduce-manifest.json) · [`LICENSE`](./LICENSE).

## License and attribution

Weights licensed **apache-2.0** — see the bundled `LICENSE`. This artifact is a **converted derivative** of the base model's reward
heads: their weights were converted to Apple Core AI format. The conversion itself
is community work.

## Links

- **Base model:** [lerobot/Robometer-4B](https://huggingface.co/lerobot/Robometer-4B)
- **Reproduce:** [recipe `robometer-4b`](https://github.com/kevinqz/coreai-fabric/blob/main/recipes/robometer-4b.yaml)
- **Index:** [coreai-catalog](https://github.com/kevinqz/coreai-catalog)
- [HF Collection](https://huggingface.co/collections/kevinqz/coreai-apple-on-device-6a4879f21c7e1a87c99bcf5a)


## The on-device Core AI ecosystem

- [coreai-fabric](https://github.com/kevinqz/coreai-fabric) — the reproducible
  recipe → `.aimodel` pipeline that produced this asset.
- [coreai-catalog](https://github.com/kevinqz/coreai-catalog) — the index of Core
  AI models with provenance and integration snippets.
- [apple/coreai-models](https://github.com/apple/coreai-models) — Apple's official
  exporters and runtimes.

## Not affiliated with Apple

Community conversion. Not produced, hosted, or endorsed by Apple. Apple and Core
AI are trademarks of Apple Inc., used here only to describe the target
runtime/format.