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weathon/
qwen_2_5_vision_reward

Name: qwen_2_5_vision_reward
Availability: InStock

transformers

safetensors

generated_from_trainer

trl

sft

Qwen/Qwen2.5-VL-3B-Instruct

finetune

endpoints_compatible

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Model Card for neg_aware_qwen

This model is a fine-tuned version of Qwen/Qwen2.5-VL-3B-Instruct. It has been trained using TRL on zai-org/VisionRewardDB-Image. It is aimming for evaluating image's quality similar to VisionReward but with a much smaller and faster model.

Quick start

To use this model, you can just ask the model on an image using one of the dim in VisionReward.

python

from datasets import load_dataset

# load dataset, you can also use any images
train_ds = load_dataset("zai-org/VisionRewardDB-Image", split='train[:40000]')
test_ds = load_dataset("zai-org/VisionRewardDB-Image", split='train[40000:]')

from transformers import pipeline
pipe = pipeline("image-text-to-text", model="weathon/qwen_2_5_vision_reward")

from transformers import pipeline
import pandas as pd
df = pd.read_csv("rules.csv")
import pandas as pd
import re
from PIL import Image

df.columns = df.columns.str.strip()
df['Dimension'] = df['Dimension'].ffill()

df['dim_key'] = df['Dimension'].apply(lambda x: re.search(r'\((.*?)\)', x).group(1) if re.search(r'\((.*?)\)', x) else x)

guide = {
    dim_key: {
        int(row['Score']): str(row['Description']).strip()
        for _, row in group.iterrows()
    }
    for dim_key, group in df.groupby('dim_key')
}

question = f"You need to rate the quality of an image, guideline: {guide}."

import json
def rate(image):
  messages = [
      {
          "role": "system",
          "content": [{"type": "text", "text": question}],
      },
      {
          "role": "user",
          "content": [
              {
                  "type": "image",
                  "image": image.resize((512, 512)),
              }
          ],
  }]
  gen = pipe(text=messages, return_full_text=False)
  return sum(json.loads(gen[0]["generated_text"].replace("'", '"')).values())

rate(test_ds[3]["image"])

sum(test_ds[3]["annotation"].values())

Training procedure

Visualize in Weights & Biases

This model was trained with SFT.

Framework versions

TRL: 0.24.0.dev0
Transformers: 4.56.1
Pytorch: 2.8.0+cu126
Datasets: 4.0.0
Tokenizers: 0.22.0

Citations

We are still working on the Paper, please keep an eye on the update.