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---
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license: cc-by-sa-4.0
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base_model:
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- vikp/texify
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pipeline_tag: image-to-text
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---
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## texify-fp16-onnx
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https://huggingface.co/vikp/texify with fp16 ONNX weights, shoutout to https://huggingface.co/Xenova/texify
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## Usage (`optimum[onnxruntime]`)
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If you haven't already, you can install the optimum with the onnxrumtime backend
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```bash
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pip install "optimum[onnxruntime-gpu]"
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```
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**Example:**
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```python
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from optimum.onnxruntime import ORTModelForVision2Seq
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from optimum.pipelines import pipeline
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model = ORTModelForVision2Seq.from_pretrained("Spedon/texify-fp16-onnx", provider="CUDAExecutionProvider")
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texify = pipeline(
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"image-to-text",
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model,
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feature_extractor="Spedon/texify-fp16-onnx",
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image_processor="Spedon/texify-fp16-onnx",
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)
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image = (
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"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex.png"
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)
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latex = texify(image, max_new_tokens=384)
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print(latex)
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# [{'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 $\\mathcal{C}_j$. "}]
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```
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| Input image | Visualized output |
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| ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
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|  |  |
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