Namespace AiDotNet.ModelCompression
Classes
- CompressionAnalyzer<T>
Analyzes model weights to determine optimal compression strategies.
- CompressionMetrics<T>
Provides metrics and statistics for model compression operations.
- CompressionResult<T>
Result of a compression operation.
- DeepCompressionMetadata<T>
Metadata for Deep Compression containing information from all three stages.
- DeepCompressionStats
Statistics about Deep Compression performance.
- DeepCompression<T>
Implements the Deep Compression algorithm from Han et al. (2015).
- HuffmanEncodingCompression<T>
Implements Huffman encoding compression for model weights using variable-length encoding.
- HuffmanEncodingMetadata<T>
Metadata for Huffman encoding compression.
- HuffmanNode<T>
Represents a node in the Huffman tree.
- HybridCompressionMetadata
Legacy non-generic metadata for backward compatibility.
- HybridCompressionMetadata<T>
Metadata for hybrid compression combining clustering and Huffman encoding.
- LowRankFactorizationCompression<T>
Implements Low-Rank Factorization compression using SVD-like decomposition.
- LowRankFactorizationMetadata<T>
Metadata for Low-Rank Factorization compression.
- MatrixCompressionMetadata<T>
Metadata for matrix compression operations that wraps the underlying vector compression metadata.
- ModelCompressionBase<T>
Provides a base implementation for model compression techniques used to reduce model size while preserving accuracy.
- ProductQuantizationCompression<T>
Implements Product Quantization (PQ) compression for model weights.
- ProductQuantizationMetadata<T>
Metadata for Product Quantization compression.
- SparseCompressionResult<T>
Result of sparse compression operation.
- SparsePruningCompression<T>
Implements sparse pruning compression by zeroing out small-magnitude weights.
- SparsePruningMetadata<T>
Metadata for sparse pruning compression.
- TensorCompressionMetadata<T>
Metadata for N-dimensional tensor compression operations that wraps the underlying vector compression metadata.
- WeightAnalysisResult<T>
Analysis results for model weights to guide compression decisions.
- WeightClusteringCompression<T>
Implements weight clustering compression using K-means clustering to group similar weights.
- WeightClusteringMetadata<T>
Metadata for weight clustering compression.
Enums
- SparseFormat
Sparse storage formats.