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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-quantized-onnx |
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https://huggingface.co/vikp/texify with quantized 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]" |
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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-quantized-onnx") |
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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-quantized-onnx", |
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image_processor="Spedon/texify-quantized-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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