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  ## Typesense Public Embedding Models
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- We store our current supported embedding models in this repo and you can also convert your own models to ONNX format and create a PR to add it to our supported models list.
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  ### Convert a model to ONNX format
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  #### Converting a Hugging Face Transformers Model
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- You can follow instructions from [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) to convert any model from Hugging Face to ONNX format using ```optimum-cli```.
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  #### Converting a PyTorch Model
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- You can use ```torch.onnx``` [APIs](https://pytorch.org/docs/stable/onnx.html) to convert PyTorch models to ONNX.
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  #### Converting a Tensorflow Model
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- You can use ```tf2onnx``` [tool](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx) to convert Tensorflow models to ONNX.
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  ### Creating model config
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- Before creating a PR with your ONNX model, you should store model file, vocab file and model config file under a folder with model name. Your model config must be named as ```config.json``` and should contain those keys:
 
 
 
 
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  | Key | Description | Optional |
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  |-----|-------------|----------|
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  |model_md5| MD5 checksum of model file as string| No |
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  |vocab_file_name| File name of vocab file| No |
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  |indexing_prefix| Prefix to be added before embedding documents| Yes |
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  |query_prefix| Prefix to be added before embedding queries | Yes |
 
 
 
 
 
 
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  ## Typesense Public Embedding Models
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+ We maintain a repository of currently supported embedding models, and we welcome contributions from the community. If you have a model that you would like to add to our supported list, you can convert it to the ONNX format and create a Pull Request (PR) to include it.
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  ### Convert a model to ONNX format
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  #### Converting a Hugging Face Transformers Model
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+ To convert any model from Hugging Face to ONNX format, you can follow the instructions in [this link](https://huggingface.co/docs/transformers/serialization#export-to-onnx) using the ```optimum-cli```.
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  #### Converting a PyTorch Model
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+ If you have a PyTorch model, you can use the ```torch.onnx``` APIs to convert it to the ONNX format. More information on the conversion process can be found [here](https://pytorch.org/docs/stable/onnx.html).
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  #### Converting a Tensorflow Model
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+ For Tensorflow models, you can utilize the tf2onnx tool to convert them to the ONNX format. Detailed guidance on this conversion can be found [here](https://onnxruntime.ai/docs/tutorials/tf-get-started.html#getting-started-converting-tensorflow-to-onnx).
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  ### Creating model config
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+ Before submitting your ONNX model through a PR, you need to organize the necessary files under a folder with the model's name. Ensure that your model configuration adheres to the following structure:
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+ - **Model File**: The ONNX model file.
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+ - **Vocab File**: The vocabulary file required for the model.
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+ - **Model Config File**: Named as config.json, this file should contain the following keys:
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  | Key | Description | Optional |
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  |-----|-------------|----------|
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  |model_md5| MD5 checksum of model file as string| No |
 
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  |vocab_file_name| File name of vocab file| No |
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  |indexing_prefix| Prefix to be added before embedding documents| Yes |
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  |query_prefix| Prefix to be added before embedding queries | Yes |
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+ Please make sure that the information in the configuration file is accurate and complete before submitting your PR.
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+ We appreciate your contributions to expand our collection of supported embedding models!