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--- |
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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen2.5-VL-7B-Instruct |
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tags: |
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- mm math reasoning |
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datasets: |
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- open-r1/OpenR1-Math-220k |
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metrics: |
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- accuracy |
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--- |
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# TBAC-VLR1-7B |
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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-7B-Instruct, TBAC-VLR1-7B-SFT undergoes SFT |
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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, |
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achieving strong performance 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** | **LogicVista** | |
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| :--------------------------------: | :---------: | :-----------: | :------------: | :-----------: | :----------: | :------------: | |
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| Qwen2.5-VL-7B | 40.5 | 68.0 | 25.7 | 45.5 | 21.8 | 41.2 | |
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| VLAA-Thinker-Qwen2.5-7B | 42.7 | 68.0 | 26.4 | 48.2 | 22.4 | 48.5 | |
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| VL-Rethinker-7B | 41.8 | 73.7 | 28.4 | 46.4 | 17.8 | 42.7 | |
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| TBAC-VLR1-7B-RL | 41.3 | 70.1 | 25.4 | 43.4 | 19.0 | 48.4 | |
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| TBAC-VLR1-7B-SFT | 41.8 | 65.1 | 28.5 | 49.1 | 20.6 | 45.5 | |
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| TBAC-VLR1-7B | **43.4** | 66.7 | **31.4** | **50.1** | **22.6** | 46.4 | |
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<!--  --> |
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<!--  --> |
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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-VLR1-7B", torch_dtype="auto", device_map="auto" |
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) |
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processor = AutoProcessor.from_pretrained("TencentBAC/TBAC-VLR1-7B") |
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messages = [ |
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{ |
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"role": "system", |
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"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{}." |
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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{Ou2025TBACVLR1, |
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title = {TBAC-VLR1-7B}, |
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author = {Ou, Linyu and Xu, Junzhe and Yin, Yuyang}, |
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year = {2025}, |
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url = {https://huggingface.co/TencentBAC/TBAC-VLR1-7B}, |
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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. |
