Convert JAX/Python ML models to ONNX format via Google Colab notebooks.
npx skills add https://github.com/HopeArtOrg/hope-re --skill onnx-export
Please help me install this Agent Skill. Skill: onnx-export Source: https://github.com/HopeArtOrg/hope-re/tree/main/.gemini/skills/onnx-export Command: npx skills add https://github.com/HopeArtOrg/hope-re --skill onnx-export
This Skill helps users convert JAX/Python machine learning models to ONNX format, enabling them to be used in various environments that require ONNX compatibility.
Run the onnx-export skill to initiate the conversion process of your ML model into ONNX format following the Hope:RE pipeline.
High-intent search queries and answers about installing and using this skill.
You can convert JAX models to ONNX format by running this skill, which automates weight extraction and ONNX export through predefined Google Colab notebooks using the Hope:RE pipeline.
Yes, you can export PyTorch CLIP models to ONNX as this skill requires PyTorch CLIP libraries alongside JAX. The pipeline is designed to handle JAX/Python ML models and convert them into ONNX format for broad deployment compatibility.
Before converting ML models to ONNX, you need JAX, jaxlib, jax2onnx, onnx, onnxsim, onnxruntime, transformers, torch, and numpy installed. These libraries provide the necessary environment for weight extraction and model format conversion.
Yes, this ONNX conversion process supports automated execution in Google Colab. It utilizes a series of predefined notebooks to automate the entire pipeline, from weight extraction and algorithm training to the final ONNX model export.
Using jax2onnx for model conversion directly integrates with JAX and automates the pipeline via Google Colab notebooks. This streamlines weight extraction and ONNX export specifically for JAX/Python ML models, ensuring compatibility with ONNX runtime environments.
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