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--- |
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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- Qwen/Qwen2.5-VL-3B-Instruct |
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pipeline_tag: image-text-to-text |
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--- |
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# TBAC-VLR1-3B-preview |
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## Overview |
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This is a multimodal language model fine-tuned by **Tencent PCG Basic Algorithm Center**. Based on Qwen2.5-VL-3B-Instruct, TBAC-VLR1-3B-preview uses Group Relative Policy Optimization |
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(GRPO) to enhance multimodal reasoning ability, achieving **state-of-the-art** results on several multimodal reasoning benchmarks among models of the same size. |
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## Performance |
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| Model | **Average** | **MathVista**| **MathVision** | **MathVerse** | **DynaMath** | **WeMath**| **LogicVista** | |
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| :-------------------: | :---------: | :-----------:| :------------: | :-----------: | :-----------: | :-------: | :----------: | |
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| Qwen2-VL-2B | 20.5 | 48.0 | 16.1 | 17.5 | 3.8 | 10.8 | 26.6 | |
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| InternVL2.5-2B | 21.2 | 51.1 | 14.0 | 22.3 | 4.4 | 8.0 | 27.3 | |
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| InternVL3-2B | 29.1 | 57.6 | 20.2 | 24.5 | 14.8 | 22.9 | 40.3 | |
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| Qwen2.5-VL-3B | 31.8 | 61.2 | 21.9 | 31.2 | 13.2 | 22.9 | 40.3 | |
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| VLM-R1-3B-Math-0305 | 33.4 | 62.7 | 21.9 | 32.2 | 13.0 | 30.0 | 40.5 | |
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| Taichu-VLR-3B | 33.6 | 64.9 | 23.1 | 32.1 | 12.6 | 30.4 | 38.7 | |
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| VLAA-Thinker-Qwen2.5VL-3B | 35.4 | 61.0 | 24.4 | 36.4 | 18.2 | 33.8 | 38.5 | |
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| **TBAC-VLR1-3B-preview** | **35.7** | 64.8 | 25.0 | 33.2 | 17.7 | 32.4 | 40.8 | |
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The compared results are sourced from https://opencompass.org.cn. |
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The results of our model are self-reported, obtained by running evaluations offline on each benchmark. |
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## Usage |
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```python |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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"TencentBAC/TBAC-VLR1-3B-preview", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("TencentBAC/TBAC-VLR1-3B-preview") |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a helpful assistant. The user asks a question, and you solve it. You need first think about the reasoning process in the mind and then provides the user with the answer. The answer are enclosed within \\boxed{} tags i.e., reasoning process here \\boxed{ answer here }." |
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}, |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "image", |
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"image": image_path, |
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}, |
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{"type": "text", "text": query}, |
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], |
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} |
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] |
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# Preparation for inference |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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# Inference: Generation of the output |
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generated_ids = model.generate(**inputs, max_new_tokens=128, do_sample=False) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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output_text = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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print(output_text) |
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``` |
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## Citation |
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If you find our model useful in your research, please consider giving ❤️ and citations. Thanks! |
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``` |
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@misc{Xu2025tbacvlr1, |
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title={TBAC-VLR1-3B-preview}, |
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author={Junzhe Xu and Yuyang yin}, |
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url={https://huggingface.co/TencentBAC/TBAC-VLR1-3B-preview}, |
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year={2025}, |
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} |
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``` |
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--- |
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**About** |
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Created by the Tencent PCG Basic Algorithm Center. All rights reserved. |
