2 dépôts
Awesome GitHub RepositoriesModel Runtime Compatibility Verifications
Tools for verifying that a model can be parsed and executed by a specific runtime version.
Distinct from Model Compatibility Layers: Distinct from Model Compatibility Layers: provides verification tools rather than the actual execution layer.
Explore 2 awesome GitHub repositories matching artificial intelligence & ml · Model Runtime Compatibility Verifications. Refine with filters or upvote what's useful.
Awesome Model Runtime Compatibility Verifications GitHub Repositories
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- onnx/onnx-tensorrt
onnx/onnx-tensorrt
3,187Voir sur GitHubThis project is a deep learning model compiler and parser that converts ONNX models into optimized TensorRT engines. It functions as a bridge that maps standardized ONNX operators to vendor-specific kernels to enable high-performance inference on NVIDIA GPUs. The system operates as a GPU inference optimizer, selecting hardware-specific kernels and tuning memory allocation to maximize throughput. It transforms neural network graphs into serialized binary execution plans to reduce runtime overhead. The toolset covers deep learning model deployment and edge AI performance tuning. It includes ca
Provides command-line tools to verify if a model can be parsed and built into an engine before deployment.
C++deep-learningnvidiaonnx - transformerlensorg/transformerlens
TransformerLensOrg/TransformerLens
3,098Voir sur GitHubTransformerLens is a library for mechanistic interpretability research designed to reverse engineer the learned algorithms within large language models. It provides a standardized framework for wrapping diverse transformer architectures, allowing researchers to extract, manipulate, and analyze internal activations and weights through a consistent interface. The project distinguishes itself through a comprehensive system of activation hooks that can capture, patch, and ablate internal tensors during the forward pass. It includes specialized utilities for decomposing fused projections, material
Checks if a specific model ID is supported and retrieves detailed architecture and verification status.
Python
