Deghsek success shows why interest is the key to Ai

January 2025 shaped the form. The one seems to be very interesting and the power of the Americas was surprised by what we can to make in the underldog in large languages ​​(LLMS). The depths of deep, degree of Chinese not on everybody’s Radar, suddenly he condemned Taoi. Not only deriseek-r1 was better than high colors from Americans; It was in front of Benchmarks, suddenly caused everyone to think about the hardware and asevani.

Since the unrelation of the highest devices, it seems to be the tank of the tanks encouraged to weaken in the area, which was a minor stress of playing. Recommended to have evidence Black Perhaps they have an example of their example, but we do not have any advice to help this situation. So, whether it’s true or by just to open up just trying to please their seller with the opposite head. However, Dighsesek has published their work, and people have proved that the results are the reasons for the smallest scale.

But how is it possible Black Get the money to buy the prices when American companies could not be over? The short answer is simple: she was very serious. A long answer requires a technical explanation.

Duariseek used KV-cache preparation

Once a once required in the GPU memory was a very important Catch Catch used in any time of llm care.

Llms made of the variable, each one has a strong impression of a vanilla with vanilla. The process of exceeding the highest form of high quality, but in the activities, it is difficult to determine all the time in data. The layer will solve the problem by imitating the language.

The example has a model shows the tokens, but because of the simplicity, we’ll call them like words. In the llm, each word is given vector in a higher level (say, a thousand dimensions). Easily, each parameter represents the idea, like a temperature or cold, to be soft, to be a name. The type of vector’s vector’s definition is the meaning of its attitude in accordance with each part.

However, our language allows other words to change the meaning of each word. For example, an apple has a meaning. But we can have a green apple as a variable color. A further example of the change may be that apple in the iPhone’s story is different from apple in meadow story. How do we allow VERCor systems to the element of the meaning of another word? Here’s where interest goes in.

The type of attention gives two other veters to each word: Password and question. The question represents the words of words that can be changed, and the key represents the update of the updates that can give you some words. For example, the word ‘green’ can give a painting notice and green-ness. So, the secret of the word ‘green’ will have a higher value on ‘ness size. On the other hand, the word ‘Apple’ can be green or not, thus Vercle Vercle ‘Apple’ can have a higher green value. If we take a ‘Green Downway,’ Apple ‘A “apple” is the key to the word’. “Apple ‘to” Apple’ is translated to the ‘Apple’.

When Llm makes a word, says one word after another. When it brings out the words, all the words that are already made of their part. However, keys and points of these words have already been counted. Another word is added by the whole story, the importance requires adjusted according to his question and keys and ideas of all previous words. This is why all of this beliefs are kept in a GPU image. These and KV cigs.

Dekoka has determined that the key is the value of the word agreed. So, the meaning of the green word and the ability to disrupt the greenness is very close. So, it is possible to include all as a vector (and maybe a small) when it is easily fixed. Dighiseeee has noticed that affects the performance of BenchmarkBut it saves the great memory of GPU.

Dungeseek used Moe

Network Network Network is that all Network should be tested (or connected) for any question. However, it’s not all this that is useful. Knowing about the world is a dose or network partition. Knowing about the Eiffel Tower does not be used to answer questions about the history of South America. Knowing that apple and fruit is not helpful in response to the questions about the common sense of interest. However, when the network is attached, all the network parts are fixed regardless of. This results in large amounts of money during the text that you have to be avoided. This is where the idea of ​​a mixture of a mixture (Moe) comes.

In the Moe version, Network network is divided into small territories. Note, ‘a specialist’ of the story is not fully described; The network is found in the study. However, the network provides the importance of each question and only causes the parts that have higher levels. This gives a lot of money in making fun. Some questions need an expert in many places to be answered, and such staff is devastated. However, because the country was aware of the data, the amount of questions like this slows.

The importance of being encouraged to study

Llm is trained to think through the imaginary version, it’s the best version to imitate before you give an answer. The color is asked to explain their thoughts (make a suggestion before making a solution). Then the sample is reviewed on the suggestion, and they are trained in studyments (blessed with accurate matches and murder the wrong matches with training.

This requires a lot of teaching with a sign. Duachesk just asks the machine to make the mind between tags with and to make answers between tags with . This type is paid or punished according to the form (using tags) and a match of answers. This requires a lot of cheap training. On the original part of RL, a representative of the test that required a very small idea, which brought the wrong answers. Afterward, the national was studied to make up to the nearest and unit, which has Highsek calls ‘A-Ha’. After that, the type of responses is very well.

Dariseek uses a number of tricks. However, they are very professional, so I will not be an examiner here.

The last thoughts about Deghsek is a larger market

In each technical study, we need to see what is possible before you are ready to achieve success. This is a larger. Deecek’s contribution to the llm form of the LLM is useful. The training of education does not be ignored, whether or not they are trained to produce produces. Can also change the way things work. But there is no need to open or other Americans despair. This is the way A Responsibility – One group benefits by the other groups. Dyyyeeeeeeeeeek, benefited from the old Google study, Opeii and many other researchers.

However, the idea of ​​tnberry will rule the land of LLM forever. There is no amount of controls or pointing to the finger to reap arriving. The technology is already in the hands of many and the open open, makes her path not moved. Although this may be a little head for Oretii sellers, it’s the success of all of us. When the future belongs to many, we will be very grateful to the offering of the first tools like Google and Treatai.

Debsish Ray Chawdhuri is a big bigger Talentica programs.