Comments (1)
Hi @gr8nishan,
The feature you select enables you to export your model to the ONNX format for a given topology. You can find here the list of the features you can use. In your case, you should use the "sequence-classification"
feature.
from optimum.
Related Issues (20)
- Add support for porting CLIPVisionModelWithProjection
- onnx optimum ORTOptimizer inference runs slower than setfit.export_onnx runtime.InferenceSession inference HOT 1
- Trying to export a cohere model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`.
- RuntimeError: Expected all tensors to be on the same device, but found at least two devices HOT 2
- Support for `torch.export.export` HOT 2
- Lift upper version limit of transformers for habana HOT 4
- Add support for export SigLIP models HOT 8
- I had a problem mimicking this site: https://community.amd.com/t5/ai/developer-blog-build-a-chatbot-with-ryzen-ai-processors/ba-p/680693
- TypeError: QDQQuantizer.__init__() got an unexpected keyword argument 'static'
- [Bettertransformer] `Transformers 4.41.0 (torch.SDPA-Bert)` breaks bettertransformers Bert, but works in `Transformers 4.40.2` HOT 7
- Custom MT5 model ONNX export not able to run
- Add feature to export CLIPVisionModel from transformers to onnx
- Add feature to export CLIPVisionModel from transformers to onnx
- exporting ORTModelForVision2Seq doesn't work correctly on Pytorch 1.11 HOT 4
- Add ORT support for the StableDiffusionControlNetPipeline. HOT 1
- Could you provide the official onnx model of Qwen-VL-Chat(-Int4)?
- `merge_decoders` mixes up `decoder_model` and `decoder_model_with_past`
- ONNX export support for 4-bit quantized models HOT 1
- Support for phi3-v Vision Model HOT 1
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from optimum.