V0 Framework and Traditional Model Documentation Navigation (Evolution Stopped)¶
The documents in this directory are organized by model type and task scenario to facilitate quick scanning.
1. Traditional Model Quantization and Calibration¶
- Traditional Model Quantization and Calibration
- Includes post-training quantization (PTQ) and quantization-aware training (QAT) for PyTorch, ONNX, and MindSpore.
2. Foundation Model Quantization and Compression¶
- Foundation Model Quantization and Compression
- Includes low-memory quantization, mixed calibration datasets, and FA3 quantization.
- Compression and Structure Optimization (Mainly for Foundation Models)(foundation_model_compression.md)
- Includes sparse quantization, weight compression, long-sequence compression, and low-rank decomposition.
3. Training Acceleration and Model Reconstruction¶
- Training Acceleration and Model Reconstruction
- Includes importance-based pruning, transformer model pruning, Sparse Tool description, and model distillation.
- Sparse Training Acceleration
- Includes sparse training acceleration workflows for width-expanded and depth-expanded models.
4. Tool and Ecosystem Adaptation¶
- Quantized Weight Format Description
- Includes descriptions of the quantized weight file and the weight description file, alongside dequantization formulas and KV Cache quantization specifications.
- MindSpeed Adapter
- Includes MindSpeed-LLM model quantization adaptation workflows and examples.
- Fake Quantization Accuracy Testing Tool
- Includes the usage and testing process of the Precision Tool.
- Inference Optimization for Multimodal Generative Models
- Includes Diffusion Transformer (DiT) cache optimization and adaptive sampling optimization workflows.
- Quantization Code Samples
- Includes code samples for common quantization and sparse quantization scenarios.