AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Paper: https://arxiv.org/abs/2306.00978
Code: https://github.com/mit-han-lab/llm-awq/
Organization: MIT
ZeroQuant-FP: A Leap Forward in LLMs Post-Training W4A8 Quantization Using Floating-Point Formats
Paper: https://arxiv.org/abs/2307.09782
Code: https://github.com/microsoft/DeepSpeed
Organization: Microsoft
QUIK: Towards End-to-end 4-Bit Inference on Generative Large Language Models
Paper: https://arxiv.org/abs/2310.09259
Code: https://github.com/IST-DASLab/QUIK
Organization: ETH Zurich
SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
Paper: https://arxiv.org/abs/2306.03078
Code: https://github.com/Vahe1994/SpQR
Organization: University of Washington
Optimize Weight Rounding via Signed Gradient Descent for the Quantization of Large Language Models
Paper: https://arxiv.org/abs/2309.05516
Code: https://github.com/intel/neural-compressor
Organization: Intel
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models
Paper: https://arxiv.org/abs/2309.02784
Code: None
Organization: Meituan
Review: H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models
Level: Average, Not Recommend
QWEN7B to LLAMA GPTQ model structure
QWEN7B to LLAMA7B Model Structure
GGML Q4_0 Quantize Analysis in llama.cpp
US License Plates Datasets
A Convolutional Fully Connected Structure with Hard Sample Memory Pool for Land Use Classification
COCO Class Name
Useful Links to Understand Knowledge Distillation
Useful Links to Understand CTC Loss
Excel: Apply Color to Alternate Rows
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