Image-to-Text
Transformers
Safetensors
English
Chinese
qwen2_5_vl
image-text-to-text
mathematical-reasoning
visual-reasoning
code-generation
qwen2.5-vl
text-generation-inference
Instructions to use gogoduan/MatPlotCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gogoduan/MatPlotCode with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="gogoduan/MatPlotCode")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("gogoduan/MatPlotCode") model = AutoModelForImageTextToText.from_pretrained("gogoduan/MatPlotCode") - Notebooks
- Google Colab
- Kaggle
| { | |
| "min_pixels": 3136, | |
| "max_pixels": 12845056, | |
| "patch_size": 14, | |
| "temporal_patch_size": 2, | |
| "merge_size": 2, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "image_processor_type": "Qwen2VLImageProcessor", | |
| "processor_class": "Qwen2_5_VLProcessor" | |
| } | |