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Model Details
Model Provider
opendatalab
Model Tree
Qwen/Qwen3-VL-8B-Instruct
Input Modalities
Output Modalities
Supported Functionality
Supervised Fine-Tuning (SFT):
Reinforcement Learning (RL):
python
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infofrom PIL import Image # 1. Load Modelmodel_path = "opendatalab/ChartVerse-8B"model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained(model_path) # 2. Prepare Inputimage_path = "path/to/your/chart.png"query = "Which region demonstrates the greatest proportional variation in annual revenue compared to its typical revenue level?" messages = [ { "role": "user", "content": [ {"type": "image", "image": image_path}, {"type": "text", "text": query}, ], }] # 3. Inferencetext = 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, padding=True, return_tensors="pt",).to("cuda")generated_ids = model.generate(**inputs, max_new_tokens=16384)output_text = processor.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text[0])bibtex
@misc{liu2026chartversescalingchartreasoning, title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch}, author={Zheng Liu and Honglin Lin and Chonghan Qin and Xiaoyang Wang and Xin Gao and Yu Li and Mengzhang Cai and Yun Zhu and Zhanping Zhong and Qizhi Pei and Zhuoshi Pan and Xiaoran Shang and Bin Cui and Conghui He and Wentao Zhang and Lijun Wu}, year={2026}, eprint={2601.13606}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2601.13606}, }This model is released under the Apache 2.0 License.
from transformers import Qwen3VLForConditionalGeneration, AutoProcessorfrom qwen_vl_utils import process_vision_infofrom PIL import Image # 1. Load Modelmodel_path = "opendatalab/ChartVerse-8B"model = Qwen3VLForConditionalGeneration.from_pretrained( model_path, torch_dtype="auto", device_map="auto")processor = AutoProcessor.from_pretrained(model_path) # 2. Prepare Inputimage_path = "path/to/your/chart.png"query = "Which region demonstrates the greatest proportional variation in annual revenue compared to its typical revenue level?" messages = [ { "role": "user", "content": [ {"type": "image", "image": image_path}, {"type": "text", "text": query}, ], }] # 3. Inferencetext = 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, padding=True, return_tensors="pt",).to("cuda")generated_ids = model.generate(**inputs, max_new_tokens=16384)output_text = processor.batch_decode( generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)print(output_text[0])@misc{liu2026chartversescalingchartreasoning, title={ChartVerse: Scaling Chart Reasoning via Reliable Programmatic Synthesis from Scratch}, author={Zheng Liu and Honglin Lin and Chonghan Qin and Xiaoyang Wang and Xin Gao and Yu Li and Mengzhang Cai and Yun Zhu and Zhanping Zhong and Qizhi Pei and Zhuoshi Pan and Xiaoran Shang and Bin Cui and Conghui He and Wentao Zhang and Lijun Wu}, year={2026}, eprint={2601.13606}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2601.13606}, }