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…N pipeline, detailing core components, conversion functions, compatibility layers, testing framework, CLI entry points, output formats, and error handling.
…g the complete process for converting pretrained transformer LLMs to spiking neural networks. Includes implementation phases, quick start commands, key components, validation checklist, and troubleshooting tips.
…work, detailing conversion, inference, testing requirements, platform compatibility, cloud deployment options, performance benchmarks, and troubleshooting tips.
…al networks. Includes command line interface, calibration data generation, model conversion logic, and SNN-specific model saving functionality.
…ions for STAC. Updated key features, core components, implementation status, and testing guidelines to provide clearer guidance for users. Improved structure and added detailed commands for conversion and testing processes.
… temporary outputs, log files, experimental scripts, and OS-specific artifacts. This helps maintain a cleaner repository by preventing unnecessary files from being tracked.
…ng core libraries for PyTorch, spiking neural networks, efficient training, machine learning utilities, and optional development tools. This file outlines version constraints and installation instructions for CUDA compatibility and Python version requirements.
…. This script includes command-line arguments for model conversion, component testing, and TorchScript export. It also incorporates logging for tracking the conversion process and compatibility checks for SpikingJelly.
… The script includes model loading, inference with original and ReLU models, SNN conversion, and prediction comparison, along with comprehensive logging for tracking the process.
…s of the SNN model. The script includes argument parsing, conversation simulation, and various tests for position ID boundaries, attention mask continuity, multi-turn coherence, energy consumption, mixed precision, and Loihi compatibility. Comprehensive logging is implemented for tracking test progress and results.
…iking Neural Network. The script includes model loading, SNN-specific layer replacements, calibration data generation, and a command-line interface for conversion parameters. It implements logging for tracking the conversion process and ensures compatibility with SpikingJelly.
…, testing cells, and model definitions for the spiking neural network framework. This file is no longer needed as the project structure has been updated to streamline the conversion and testing processes.
…kingJelly components. This file includes version checks, neuron and converter retrieval functions, a custom quantizer class for model quantization, and a surrogate function. It ensures cross-version compatibility for smoother integration and usage of SpikingJelly features.
…and testing cells for the spiking neural network framework. This file is no longer necessary due to updates in the project structure that streamline conversion and testing processes. Additionally, add a CI workflow configuration in .github/workflows/ci.yml to automate testing and documentation checks across multiple Python versions.
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Summary: Comprehensive cleanup and optimization of STAC codebase for academic publication and production deployment.
✅ Key Updates:
🎯 Impact:
Result: STAC is now a professional, publication-grade repository showcasing the world's first working multi-turn conversational SNN implementation. ⚡