Understanding FP32 and FP64: Single and Double Precision Floating Point
作者：XD / 发表： 2024年8月5日 05:44 / 科研学习/ 阅读量：2270
Understanding FP32 and FP64: Single and Double Precision Floating Point
Understanding FP32 and FP64: Single and Double Precision Floating Point
Understanding BF16: Brain Floating Point Format
Understanding FP16: Half-Precision Floating Point
Lucid Plugin from ChatGPT to Creating the Diagram
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Paper: https://arxiv.org/abs/2211.10438
Code: https://github.com/mit-han-lab/smoothquant
Organization: MIT
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
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