Quantize your LLM for llama.cpp/Ollama
Want to deploy a large language model (LLM) on a device with limited resources? How expensive do you think it would be to run a model with billions of parameters on your laptop (or gaming PC)? Very expensive, right? Well, it doesn't have to be.
In this tutorial, we'll see how to quantize a fine-tuned LLM and run it on your machine with llama.cpp or Ollama. We'll evaluate the model's performance and see if we lose any accuracy compared to the full-precision model.
Tutorial Goals
In this tutorial, you will:
- Download our existing fine-tuned Llama 3.2 model
- Quantize and convert to GGUF
- Load and evaluate using llama.cpp
- Learn how to use the quantized model in Ollama
What is quantization?
Quantization is the process of making your big fat model smaller and faster. With that, you would want to preserve the accuracy as much as you can. To do it, we'll try to preserve the information from the weights of the model as much as possible.
Model weights are usually stored as 16 or 32-bit floating-point numbers. Quantization allows us to convert these numbers to 8-bit (or smaller) integers. This reduces the size of the model by 4x or more. Which in turn makes the model faster and run and require less memory.
In the world of llama.cpp and GGUF1 (GPT-Generated Unified Format), the primary quantization approach involves transforming model weights into lower-precision integer formats through block-wise quantization techniques. These methods divide model weights into small blocks (typically 32 or 64 elements) and apply different quantization strategies - such as using block-wise scaling factors and zero points - to minimize information loss during compression.
Will you lose accuracy?
Yes, you do lose some accuracy. But it can be very negleagible within a certain quantization range - 8-bit to 4-bit. This approach allows complex models like LLaMA to run efficiently on consumer-grade hardware, that is, your laptop or desktop.
In the paper "A Comprehensive Evaluation of Quantization Strategies for Large Language Models"2 dicusses the trade-offs between model size, speed, and accuracy. Here's a quote from what they found:
4-bit quantization offers a trade-off between the LLMs' capacity and the number of bits in the low-precision format. As the number of quantized bits decreases to 3 bits or lower, there is a noticeable performance discrepancy between the LLMs and their quantized counterparts.
We'll go with the 8-bit quantization for this example and see how it affects the model's performance.