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Model export #1
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Model export #1
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* position ids for rope * cleanup * no need for mask * no mask * more cleanup * add back filtering * more cleanup * revert the signature of llm's generate and forward * use self.decoder.lm_use_tokens * use torch inference_mode * add back comment * fix bug * add back comments * add back comments
Co-authored-by: Kashif Rasul <[email protected]>
Co-authored-by: Kashif Rasul <[email protected]>
always use
Implementing KV Cache for inference
Multi-node training (and a few other things, should have created more PRs!)
- Add export_executorch.py with dynamic shapes and int8 quantization - Add test_executorch_export.py for end-to-end inference testing - Add test_executorch_accuracy.py for numerical accuracy validation - Add ONNX export scripts in onnx_export/ - Fix language_model.py for export compatibility: - Remove dynamic RoPE scaling (data-dependent control flow) - Add position_ids parameter to forward() - Fix SDPA to use explicit masks instead of is_causal ExecuTorch export produces 6.0GB (unquantized) or 2.3GB (quantized) models that generate accurate descriptions. Both exports tested and working. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>
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