README.md

---
title: RynnValue-8B
emoji: 🦾
colorFrom: yellow
colorTo: gray
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
short_description: How much longer will this robot take?
python_version: "3.10"
startup_duration_timeout: 1h
pinned: false
license: apache-2.0
models:
  - Alibaba-DAMO-Academy/RynnValue-8B
tags:
  - robotics
  - value-model
  - video
---

# RynnValue-8B

Demo of [`Alibaba-DAMO-Academy/RynnValue-8B`](https://huggingface.co/Alibaba-DAMO-Academy/RynnValue-8B),
a ~9.6B robotic value model built on Qwen3-VL. Given a robot manipulation video and the task
instruction, it predicts the **remaining time to task completion (seconds)** at every point along
the clip — rendered as a curve synchronised with the video — plus a short **Analysis** block
(video description, instruction `Match`, task `Success`).

The app follows the official reference implementation
([`rynn_infer/inference.py`](https://github.com/alibaba-damo-academy/RynnValue/blob/main/rynn_infer/inference.py)):
prefix-uniform sampling, where evaluation step *i* resamples `frames[0:i]` to `num_frames` frames
and reads the model's **last** prediction slot, so each score only conditions on frames seen so
far; the custom `pred_slot_isolated_eager` attention implementation; and a greedy Analysis
generation pass over the full-video prefix. `plot_utils.py` is vendored from the same repo
(Apache-2.0) with the trend plot memoised per prediction step (it is otherwise re-rendered once
per output video frame).

Deviations from the reference, forced by the ZeroGPU time budget: fewer evaluation steps
(16 vs one-per-frame), fewer frames per step (32 vs 64), smaller frames (384 px vs 640 px), and the
rendered video is temporally subsampled to ≤320 frames (playback fps scaled to match, so durations
and the ground-truth reference curve are unchanged). All are adjustable in *Advanced settings*.

`transformers` is pinned to 4.57.x (what the checkpoint's remote code targets), which requires
`huggingface-hub<1.0` — hence the Gradio 5.x SDK version.

## Example assets & attribution

- `examples/franka_box_into_drawer.mp4` — the demo clip bundled with
  [alibaba-damo-academy/RynnValue](https://github.com/alibaba-damo-academy/RynnValue)
  (Apache-2.0), re-encoded to 640 px.
- `examples/soar_put_green_stick_in_brown_bowl.mp4`, `examples/berkeley_rpt_stack_cup.mp4`,
  `examples/jaco_play_pick_up_green_cup.mp4` — RoboMeter benchmark clips bundled in the same
  repository (MIT), originating from
  [Open X-Embodiment](https://robotics-transformer-x.github.io/) (SOAR / Berkeley RPT /
  Jaco Play), CC BY 4.0. The task strings are the ones used in the RoboMeter README.
- `examples/so101_lego_into_box.mp4` — episode 1 of
  [`lerobot/svla_so101_pickplace`](https://huggingface.co/datasets/lerobot/svla_so101_pickplace)
  (Apache-2.0), side camera.

The last example row pairs the Franka video with an unrelated instruction to show the
video/instruction matching behaviour (`Match: No`).