linkedin/QuantEase
QuantEase, a layer-wise quantization framework, frames the problem as discrete-structured non-convex optimization. Our work leverages Coordinate Descent techniques, offering high-quality solutions without the need for matrix inversion or decomposition.
This tool helps machine learning engineers and researchers deploy large language models (LLMs) more efficiently by making them smaller and faster without losing much accuracy. It takes a pre-trained LLM and converts its internal weights into a smaller, more optimized format. The result is a quantized LLM that performs nearly as well as the original but uses significantly less memory and computational power.
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Use this if you need to reduce the size and improve the inference speed of large language models like BLOOM, OPT, or Falcon for deployment on resource-constrained hardware, while maintaining high accuracy.
Not ideal if you are developing new LLM architectures or require full floating-point precision for niche applications where even minor accuracy trade-offs are unacceptable.
Stars
19
Forks
3
Language
Python
License
BSD-2-Clause
Category
Last pushed
Feb 22, 2024
Commits (30d)
0
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