| --- |
| title: LoopVL |
| emoji: 🔁 |
| colorFrom: purple |
| colorTo: yellow |
| sdk: gradio |
| sdk_version: 6.30.0 |
| app_file: app.py |
| short_description: Recurrent VLM with a controllable compute budget |
| python_version: "3.12" |
| startup_duration_timeout: 30m |
| --- |
| |
| # LoopVL: Recurrent Vision-Language Model |
|
|
| **LoopVL** ([TierFlow/LoopVL](https://huggingface.co/TierFlow/LoopVL)) is a ~1.6B |
| vision-language model that reuses its 16-layer transformer stack in nested loops |
| instead of stacking 128 layers. The distinctive control this demo exposes is the |
| **recurrent inference budget**: the H (high) and L (low) cycle counts decide how |
| many times the stack is applied per forward pass — unrolled depth |
| `16 × H × (L+1)` effective layers. The schedule can be changed at runtime without |
| reloading weights. |
|
|
| Ask any question about an image, pick a schedule, and compare answers across |
| compute budgets. |
|
|
| - **Model**: [TierFlow/LoopVL](https://huggingface.co/TierFlow/LoopVL) (BF16 |
| vision/language cores + FP32 projector, visual gate and anchor) |
| - **Code**: [Tier-Flow/LoopVL](https://github.com/Tier-Flow/LoopVL) — the |
| inference runtime (`hrm_penguin`, `penguin_encoder`, `modeling_loopvl.py`, |
| `metadata.py`) is vendored here from that Apache-2.0 repository; the Penguin |
| vision encoder keeps its Tencent `LICENSE.txt` alongside. |
| - **Examples**: images from |
| [linoyts/repo-to-space-example-inputs](https://huggingface.co/datasets/linoyts/repo-to-space-example-inputs) |
| (free to use, CC0-style). |
|
|
| ## How it works |
|
|
| 1. The image is resized under a dynamic-resolution policy (256–2048 visual |
| tokens) and patched by the PenguinVL vision encoder. |
| 2. Visual tokens are injected into the text sequence; the HRM-Text recurrent |
| LM loops its stack `H × (L+1)` times per forward pass, re-injecting an |
| anchored version of the visual tokens after each high cycle, gated by a |
| learned saturation-aware gate. |
| 3. Decoding is greedy, recomputing the full sequence per token (no KV cache). |
|
|
| ## Reproducing the reference |
|
|
| The `answer` endpoint follows the authors' `runtime/infer.py` one-to-one: |
| same prompt template (`condition\nImage:\n` + placeholders + `\nInstruction:\n…`), |
| same greedy decode, same `decode(skip_special_tokens=True).strip()` postprocess. |
