Public KG record
AffineQuant
Recorded overview
AffineQuant is a novel quantization method that uses an affine transformation matrix to change the distribution of weights and activations, aimed at optimizing the distribution of weight activations and reducing quantization errors. By introducing an affine transformation matrix, AffineQuant can better align the data distribution with the quantization function, thereby reducing quantization errors. The matrix optimization objective is to minimize the mean squared error between pre- and post-quantization feature map
An evidence-backed entity from the project knowledge graph; detailed tool metadata is still being added.
Recorded details
- Integrations: safetensors integration
- License: apache-2.0
Evidence coverage
The KG currently records 6 known information dimensions and has 11 undocumented dimensions, linked to 1 public source articles. These counts describe record coverage, not model performance or verified fit.
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