feat: Add native Gemma3 support for ONNX export#87
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- Add Gemma3OnnxConfig class with proper configuration - Register gemma3 model type for text generation and classification tasks - Add Gemma3 to supported architectures and test mappings - Set minimum transformers version requirement to 4.50.0 - Follow same pattern as existing Gemma/Gemma2 implementations Fixes: ValueError when exporting Gemma3 models to ONNX format Resolves: 'gemma3 model, that is a custom or unsupported architecture' error
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The docs for this PR live here. All of your documentation changes will be reflected on that endpoint. The docs are available until 30 days after the last update. |
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Thanks ! This was already done in #70 |
Author
Oh,I didn't notice that. I will try the gemma3 VLM variant if possible. |
Member
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yes please do, you can use #50 for reference |
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🎯 Problem
Users trying to export fine-tuned Gemma3 models to ONNX format encounter this error:
This prevents users from converting their Gemma3 models to ONNX format for deployment and optimization.
🔧 Solution
This PR adds native Gemma3 support to optimum by implementing the
Gemma3OnnxConfigclass and properly registering it in the tasks manager.📝 Changes Made
✨ Core Implementation
Added
Gemma3OnnxConfigclass** inoptimum/exporters/onnx/model_configs.pyTextDecoderOnnxConfig(same pattern as Gemma2)GemmaDummyPastKeyValuesGeneratorfor compatibility🔧 Supporting Changes
optimum/exporters/onnx/utils.py** - Added "gemma3" toMODEL_TYPES_REQUIRING_POSITION_IDS🧪 Testing & Verification
📋 Usage Example
After this change, users can export Gemma3 models seamlessly: