What is the artificial intelligence model you should use? (Check the criteria)

There are more artificial intelligence models than what you see in news and on social media. There are hundreds, including open source, private, private, tech giant’s: gemini, clade, openai, GROK, Deepseek. What is the model really? Is it just a block box of data? barely! You can think about it as a compact Internet file with a little C ++ code that communicates with the Zip file. I appreciate this measurement for Andrej Currei, although I am not sure whether his idea is, but he is the real industry expert.

The artificial intelligence model is a nerve network that uses a set of huge data to identify specific patterns. It is time to benefit from it and choose wisely whether to enhance business, personal assistance or improve creativity. The goal of this guide is not about “training for models”; It is directed to new individuals in the field of artificial intelligence who want a better understanding and benefit from technology. You can build with artificial intelligence, not above it, so after reading this guide, acquired knowledge should be an understanding of general concepts, use and accuracy measuring.

In this artificial intelligence guide, you will learn the following, so that you can move to any section, but if you are a beginner, read this entire article:

1. Models category
2. The tasks corresponding to the forms
3. Models naming agreement
4. Performing accuracy of the forms
5. Standard references

Beginners or just hearing common tools, note that there is no single model, a model for a multi -use condition that does everything you require. From the interface, you may seem to be just writing to Chatbot, but there is a lot to be implemented in the background. Business analysts, product managers and engineers who adopt artificial intelligence can determine the goal they have and choose from a category of artificial intelligence models.

Here are 4 categories of models between many:

• Natural language treatment (general)
• Generative (Image, Video, Voice, Text, Code)
• Discrimination (computer vision, text analysis)
• Learning reinforcement

While most models are specialized in one category, others are multimedia with different levels of accuracy. Each model has been trained on specific data, and therefore, it can carry out specific tasks related to the data that have been trained on it. Below is a list of common tasks that each category can do:

Natural language processing

Computers enable the interpretation, understanding and generation of natural human language using the distinctive symbol and statistical models. Examples include Chatbots, the most common is ChatGPT, which belongs to a “pre -training training adapter”. Most models are actually pre -trained transformers.

Generative (Image, Video, Voice, Text, Code)

They are aggressive networks (GANS), which use sub -models known as the generator and discrimination. Realistic images, sound, text and symbol can be produced based on tons of data trained on them. Stably spread is the most popular way to create photos and videos.

Discrimination (computer vision, text analysis)

These algorithms are used to learn different categories of data groups to make decisions. It includes the analysis of feelings, visual recognition, and feelings analysis.

Learning reinforcement

Using experimental methods, error and human enforcement to produce results directed towards the target, such as robots, gameplay, and self -rule.

Models naming agreement

Now that you understand the types and tasks of the models, the next step is to determine the quality of the model and performance. This begins with the name of the models. Let’s collapse the naming of a model. There is an official agreement to name artificial intelligence models, but it will only be the most popular name followed by the version number, such as ChatGPT #, Claude #, GROK #, Gemini #.

However, the smaller models of their sources and their sources have longer names. This can be seen on Hugingface.co, which will contain the name of the Foundation, the name of the model, the size of the parameter, and finally the size of the context.

Let’s explain examples:

MistRalai/Mistral-Small-3.1-24B-Instruct-2053

1. Mistralai is the organization
2. Mistral-Small is the name of the model
3. 3.1 is the version number
4. 24B-Instruct is the number of teachers in billions of training data
5. 2053 is the size of the context or the number of the distinctive symbol

Google/Gemma-3-27B

1. Google is the organization
2. Gemma is the name of the model
3. 3 is the version number
4. 27 B is the size of the teacher in billions

Additional details, which you will see and need to know, are the formatting of quantitative measurement in bits. The higher the quantitative measurement format, the more random access memory and storage to run the form. The measurement format is represented in the floating point, such as 4, 6, 8 and 16. Other formats can include GPTQ, NF4 and GGML, which indicate the use of specific devices formations.

Performing accuracy of the forms

If you see news headlines about the release of a new model, do not immediately trust the results that have been claimed. Competition for artificial intelligence is so competitive to the point where companies cook performance numbers for marketing noise. How many people will test them alone instead of confidence in the marketing noise? Not much at all, so do not fall into “hallucinogenic characters”. References and

The real way to determine the quality of the model is to check the standard grades and the tops of the leaders. There were many tests that could say they are almost as standard or perhaps completely united, but in reality, we are testing “black boxes” with many variables. The best measure is to verify answers and responses from artificial intelligence with facts and other scientific sources.

Leaders’ sites will show sorting categories with sounds, confidence -connection scores, usually in a percentage value. Common criteria are tests that demand the artificial intelligence model with questions and obtain measuring answers. It can include: logic challenge AI2, helloswag, mmlu, struduleqa, Winogrande, GSM8K, HumaneVal.

Below is a brief description of the measurement methods:

AI2 thinking challenge (ARC) -7787 multi -options scientific questions from primary school

Hellaswag Logical thinking exercises by completing the sentence

mmlu Understanding a huge multi -task language to solve problems

sincerity – Evaluating honesty by encouraging lies and avoiding responses such as “I am not sure.”

Winogrande Challenge the Winograd scheme with two semi -extremist sentences on the basis of the word trigger

GSM8K – 8000 mathematics questions at the primary school level

Humaneval It measures the ability to generate the correct snake symbol over 164 challenges

Leaders ’sites to refer to:

I originally published here