Instructions to use Color2333/pbr-scorer-qwen25-vl-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Color2333/pbr-scorer-qwen25-vl-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("models/Qwen2.5-VL-7B-Instruct") model = PeftModel.from_pretrained(base_model, "Color2333/pbr-scorer-qwen25-vl-lora") - Notebooks
- Google Colab
- Kaggle
PBR Scorer — Qwen2.5-VL QLoRA 打分器
把 Qwen2.5-VL-7B 微调成 PBR 逐通道质量打分器(Q-Align/DeQA 范式)。本仓库是 LoRA adapter,需配基座 Qwen/Qwen2.5-VL-7B-Instruct。
模型
- QLoRA(4-bit NF4 冻结基座 + LoRA on LLM proj + 视觉塔 blocks,~47.6M 可训)。
- 无自定义头:读 answer 位 6 个数字 token("0"…"5")softmax,期望值 = 分数。
- 训练:60k 样本,DeQA 软标签(高斯 σ=0.75)CE。
- 输入:
[通道图, 渲染图, base_color]+ 每通道评分准则 prompt。
结果(old-test 4917, SRCC)
| mean | base | normal | rough | metallic |
|---|---|---|---|---|
| 0.792 | 0.832 | 0.801 | 0.902 | 0.631 |
1/16 数据暴露追平 DINOv2-0.31B。读出可换温度(T≈0.25)以恢复两端、提升"守护精品"。
用法
from transformers import AutoModelForImageTextToText
from peft import PeftModel
base = AutoModelForImageTextToText.from_pretrained("Qwen/Qwen2.5-VL-7B-Instruct", load_in_4bit=True)
model = PeftModel.from_pretrained(base, "<this-repo>")
# 推理:构造 [channel, render, base_color] + prompt,读 6 数字 token softmax 取期望
# 完整逻辑见代码仓库 vlm_scorer_eval.py
局限
同 DINOv2 卡:聚合 SRCC 受单人标注噪声封顶 ~0.79;metallic 最弱;训练数据私有不分发。
License
继承 Qwen2.5-VL 基座许可;本 adapter 为衍生物,研究用途。
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Base model
Qwen/Qwen2.5-VL-7B-Instruct