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---
license: cc-by-sa-4.0
base_model:
- vikp/texify
pipeline_tag: image-to-text
---
## texify-quantized-onnx
https://huggingface.co/vikp/texify with quantized ONNX weights, shoutout to https://huggingface.co/Xenova/texify
## Usage (`optimum[onnxruntime]`)
If you haven't already, you can install the optimum with the onnxrumtime backend
```bash
pip install "optimum[onnxruntime]"
```
**Example:**
```python
from optimum.onnxruntime import ORTModelForVision2Seq
from optimum.pipelines import pipeline
model = ORTModelForVision2Seq.from_pretrained("Spedon/texify-quantized-onnx")
texify = pipeline(
"image-to-text",
model,
feature_extractor="Spedon/texify-quantized-onnx",
image_processor="Spedon/texify-quantized-onnx",
)
image = (
"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex.png"
)
latex = texify(image, max_new_tokens=384)
print(latex)
# [{'generated_text': "The potential $V_i$ of cell $\\mathcal{C}_i$ centred at position $\\mathbf{r}_i$ is related to the surface charge densities $\\sigma_j$ of cells $\\mathcal{C}_j$ $j\\in[1,N]$ through the superposition principle as: $$V_i\\,=\\,\\sum_{j=0}^{N}\\,\\frac{\\sigma_j}{4\\pi\\varepsilon_0}\\,\\int_{\\mathcal{C}_j}\\frac{1}{\\|\\mathbf{r}_i-\\mathbf{r}'\\|}\\,\\mathrm{d}^2\\mathbf{r}'\\,=\\,\\sum_{j=0}^{N}\\,Q_{ij}\\,\\sigma_j,$$ where the integral over the surface of cell $\\mathcal{C}_j$ only depends on $\\mathcal{C}_j$ shape and on the relative position of the target point $\\mathbf{r}_i$ with respect to $\\mathcal{C}_j$ location, as $\\sigma_j$ is assumed constant over the whole surface of cell $\\mathcal{C}_j$. "}]
```
| Input image | Visualized output |
| ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
|  |  |
|