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---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
tags:
- mm math reasoning
datasets:
- open-r1/OpenR1-Math-220k
metrics:
- accuracy
---
# TBAC-VLR1-3B
## Overview
This is a multimodal language model fine-tuned by **Tencent PCG Basic Algorithm Center**. Based on Qwen2.5-VL-3B-Instruct, TBAC-VLR1-3B-SFT undergoes SFT
training using 40k sft data filtered from OpenR1-Math-220k. TBAC-VLR1-3B then employs GRPO (Group Relative Policy Optimization) and adapts Clip-Higher from DAPO,
achieving **state-of-the-art** results on several multimodal reasoning benchmarks among models of the same size.
## Performance
| Model | **Average** | **MathVista**| **MathVision** | **MathVerse** | **DynaMath** | **LogicVista** |
| :-------------------: | :---------: | :-----------:| :------------: | :-----------: | :-----------: | :----------: |
| Qwen2-VL-2B | 22.4 | 48.0 | 16.1 | 17.5 | 3.8 | 26.6 |
| InternVL2.5-2B | 23.8 | 51.1 | 14.0 | 22.3 | 4.4 | 27.3 |
| InternVL3-2B | 31.5 | 57.6 | 20.2 | 24.5 | 14.8 | 40.3 |
| Qwen2.5-VL-3B | 33.6 | 61.2 | 21.9 | 31.2 | 13.2 | 40.3 |
| VLM-R1-3B-Math-0305 | 34.1 | 62.7 | 21.9 | 32.2 | 13.0 | 40.5 |
| Taichu-VLR-3B | 34.3 | 64.9 | 23.1 | 32.1 | 12.6 | 38.7 |
| VLAA-Thinker-Qwen2.5VL-3B | 35.7 | 61.0 | 24.4 | 36.4 | 18.2 | 38.5 |
| TBAC-VLR1-3B-preview | 36.3 | 64.8 | 25.0 | 33.2 | 17.7 | 40.8 |
| TBAC-VLR1-3B-SFT | 35.3 | 57.0 | 27.4 | 41.1 | 15.0 | 36.1 |
| TBAC-VLR1-3B | **36.7** | 57.5 | 28.7 | 41.1 | 16.1 | 40.0 |
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<!-- The compared results are sourced from https://opencompass.org.cn. -->
The results of our model are self-reported, obtained by running evaluations offline on each benchmark.
## Usage
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"TencentBAC/TBAC-VLR1-3B", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("TencentBAC/TBAC-VLR1-3B")
messages = [
{
"role": "system",
"content": "You FIRST think about the reasoning process as an internal monologue and then provide the final answer. The reasoning process MUST BE enclosed within <think> </think> tags. The final answer MUST BE put in \\boxed{}."
},
{
"role": "user",
"content": [
{
"type": "image",
"image": image_path,
},
{"type": "text", "text": query},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
## Citation
If you find our model useful in your research, please consider giving ❤️ and citations. Thanks!
```
@misc{Ou2025TBACVLR1,
title = {TBAC-VLR1-3B},
author = {Ou, Linyu and Xu, Junzhe and Yin, Yuyang},
year = {2025},
url = {https://huggingface.co/TencentBAC/TBAC-VLR1-3B},
}
```
---
**About**
Created by the Tencent PCG Basic Algorithm Center. All rights reserved. |