Instructions to use shreshthsaini/brightrate-lm-7b-multiexposure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use shreshthsaini/brightrate-lm-7b-multiexposure with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct") model = PeftModel.from_pretrained(base_model, "shreshthsaini/brightrate-lm-7b-multiexposure") - Notebooks
- Google Colab
- Kaggle
BrightRate-LM 7B multi-exposure adapters
This PEFT adapter is one result from the BrightRate-LM controlled input and scaling study.
Base model
Qwen/Qwen2.5-VL-7B-Instruct
Input interface
Eight uniformly sampled frames are each rendered at -2, 0, and +2 stops. The 24 images are passed in temporal-major order.
Training data and recipe
Five adapters were trained independently on the five content-separated BrightVQ splits. Training uses two epochs, a three-epoch cosine schedule horizon, learning rate 1e-4, micro-batch 1, gradient accumulation 8, and rank-16 LoRA with alpha 32 and dropout 0.05. MOS targets are interpolated across five quality words. The root adapter is split 0; splits/split-1 through split-4 contain the remaining adapters.
Training data: BrightVQ.
Metrics
Held-out metrics for the five 420-video test splits:
| Split | SROCC | PLCC | KRCC | RMSE |
|---|---|---|---|---|
| 0 | 0.9110 | 0.9120 | 0.7347 | 5.7437 |
| 1 | 0.9311 | 0.9287 | 0.7666 | 5.0971 |
| 2 | 0.9175 | 0.9245 | 0.7469 | 5.0977 |
| 3 | 0.8904 | 0.8957 | 0.6996 | 5.9874 |
| 4 | 0.8760 | 0.8925 | 0.6928 | 5.7481 |
| Mean | 0.9052 | 0.9107 | 0.7281 | 5.5348 |
Intended use
This adapter is intended for research on no-reference perceptual quality assessment of user-generated HDR video. Scores are not calibrated for other datasets, display pipelines, or video domains.
Code and input construction are available in BrightRate-LM.
Citation
@article{saini2026brightratelm,
title = {BrightRate-LM: Representation-Aware Quality Assessment for User-Generated HDR Video},
author = {Saini, Shreshth and Wang, Yilin and Birkbeck, Neil and Adsumilli, Balu and Bovik, Alan C.},
journal = {Machine Vision and Applications},
year = {2026},
note = {Submitted}
}
Links
Code and evaluation: github.com/shreshthsaini/BrightRate-LM. Dataset: BrightVQ on Hugging Face. Related papers: Beyond8Bits, CVPR 2026 (arXiv 2603.00938) and CHUG, ICIP 2025 (arXiv 2510.09879).
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Model tree for shreshthsaini/brightrate-lm-7b-multiexposure
Base model
Qwen/Qwen2.5-VL-7B-Instruct