Large language models (LLMs) are a type of artificial intelligence that can generate and understand human language. They are trained on massive datasets of text and code, and can be used for a variety of tasks, including machine translation, text summarization, and question answering.
One of the challenges with LLMs is that they can be very large and computationally expensive to run. This can make them difficult to deploy on mobile devices and cloud-based servers.
Quantization is a technique that can be used to reduce the size and computational complexity of LLMs without sacrificing too much accuracy. Quantization works by converting the floating-point numbers used to represent the weights of the LLM to lower-precision integer values.
Best Quantization to Use for LLM
Q5 and Q4 are the best combinations of performance and speed for quantization of LLMs. They offer a good trade-off between accuracy and efficiency.
Q2 and Q8 can achieve better performance and speed than Q5 and Q4, but they also lead to a greater loss of accuracy.
Which quantization level is best for a particular application will depend on the specific requirements of the application, such as the desired accuracy and performance.
In general, Q5 and Q4 are a good choice for applications where performance and speed are critical, but accuracy is still important. Q2 and Q8 are a good choice for applications where performance and speed are the most important factors, and accuracy can be sacrificed to some extent.
Here is a table comparing the different quantization levels:
| Quantization Level | Accuracy | Performance |
|---|---|---|
| Q2 | Low | Highest |
| Q4 | Medium | High |
| Q5 | High | High |
| Q8 | Very High | Lower |
| Q2 XS | Medium | Highest |
| Q2 XXS | Medium | Highest |
| HQQ | Medium | Highest |
When choosing a quantization level, it is important to consider the following factors:
Although HQQ doesn’t strictly follow the same pattern as the traditional quantization levels, considering it has competitive compression quality with calibration-based methods and demonstrates outstanding performance, it deserves recognition as having “Very High” accuracy and “Highest” performance among non-calibration-based techniques.
- Required accuracy: How much accuracy is required for the application?
- Target hardware platform: What hardware platform will the application be running on?
- Available resources: How much time and resources are available to train and deploy the application?
If accuracy is the most important factor, then a higher quantization level, such as Q5 or Q8, should be chosen. If performance and speed are the most important factors, then a lower quantization level, such as Q2 or Q4, should be chosen.
