Quantize your LLMs with AutoGPTQ

LLMs require massive memory and compute resources and are difficult to run. To address this problem you can use LLM compression techniques such as “quantization,” a technique that reduces the size of the models by storing them in lower-precision variable formats.

One popular and efficient quantization model is GPTQ, an algorithm that can be applied to trained LLMs to reduce the size of trained LLMs while mostly preserving their accuracy.

Hugging Face supports GPTQ through the AutoGPTQ library. AutoGPTQ supports quantization for a wide range of LLM families, including Llama, OPT, BLOOM, GPT-Neo, and Falcon. You can use AutoGPTQ to quantize your own models, publish them on Hugging Face, and host them for your applications.

Read more on TechTalks.

For more tips on using LLMs:

• The complete guide to LLM compression

• How to customize LLMs like ChatGPT with your own data and documents

• How to fine-tune GPT-3.5 or Llama 2 with a single instruction

• How to run multiple fine-tuned LLMs for the price of one

Recommendations:

• My go-to platform for working with ChatGPT, GPT-4, and Claude is ForeFront.ai, which has a super-flexible pricing plan and plenty of good features for writing and coding.