Quantization in LLM
Contents
Quantization in LLMยถ
Summaryยถ
Quantization in Large Language Models (LLMs) is a model compression technique aimed at reducing the size and computational requirements of these models by converting their weights and activations from high-precision data types to lower-precision ones. This process involves mapping continuous values to a smaller set of discrete values, which significantly decreases the modelโs memory footprint and computational needs, making it more efficient and accessible, particularly for deployment on resource-constrained devices. Various quantization techniques, such as linear quantization, post-training quantization (PTQ), and quantization-aware training (QAT), are employed to achieve this goal while minimizing the impact on model performance.
Key Conceptsยถ
Quantization: A technique used to reduce the precision of model weights and activations, converting them from high-precision data types (e.g., FP32) to lower-precision ones (e.g., INT8).
Linear Quantization: A method that maps the range of floating-point values to a range of fixed-point values evenly, using a scale factor and zero-point to ensure numerical accuracy.
Post-Training Quantization (PTQ): A technique where quantization is performed after the model has been trained, aiming to find a simpler version of the weights that still yields good results.
Quantization-Aware Training (QAT): A method where quantization is integrated into the training process, allowing the model to learn to be robust to quantization noise.
Calibration: The process of selecting the optimal range for quantization, which includes techniques such as choosing a percentile of the input range, optimizing the mean squared error (MSE), and minimizing entropy (KL-divergence).
Referencesยถ
URL Name |
URL |
|---|---|
A Visual Guide to Quantization |
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LLM Quantization: Techniques, Advantages, and Models |
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Quantization for Large Language Models (LLMs): Reduce AI Model Size |
https://www.datacamp.com/tutorial/quantization-for-large-language-models |
The Ultimate Handbook for LLM Quantization |
https://towardsdatascience.com/the-ultimate-handbook-for-llm-quantization-88bb7cb0d9d7 |
A Guide to Quantization in LLMs |
https://symbl.ai/developers/blog/a-guide-to-quantization-in-llms/ |