<prompt> Hlalosa mokhoa oa ho sebelisa mohlala oa GPT ho hlahisa mongolo oa puo ea tlhaho.nan  <response>: Instruction in English: Describe a process of using a GPT model to generate natural language text. . Response in English: GPT, or Generative Pre-trained Transformer, is a popular model for generating natural language text. The following is a basic process of how to use a GPT model for text generation:1. The first step in using a GPT model is to choose an appropriate pre-trained model that suits the requirements of the task. There are several versions of GPT models with varying sizes, trained on different corpora.2. After selecting a suitable model, the next step is to prepare the input text, also known as the prompt or context. The input text can be in the form of a sentence, a paragraph, or a few paragraphs, depending on the desired output text. The quality of the output text heavily depends on the quality of the input text, so it is essential to provide a relevant and coherent prompt.3. Once the input text is prepared, the GPT model tokenizes the input text, breaking it down into individual tokens, mapping each token to its corresponding integer value using a pre-defined vocabulary.4. The tokenized input text is then fed into the GPT model, where it processes the input through several layers of stacked self-attention and feed-forward neural network layers. This generates a probability distribution over the possible next tokens for each position.5. Given the probability distribution, the next token is selected using a decoding strategy such as greedy or beam search, with a certain level of randomness introduced using a temperature parameter. The chosen token is then added to the input.6. The above step is repeated until the desired output length is achieved or a stop token is generated. The generated output is then decoded back into human-readable text, resulting in natural language text.7. The quality of the generated text can be further improved by fine-tuning the GPT model on a similar task, providing it with more relevant training data to adapt it to the desired output.This process can be computationally expensive, so powerful hardware may be required to generate text quickly. Nonetheless, GPT models are a state-of-the-art approach for natural language text generation, providing coherent and fluent outputs with remarkable results. . Response in Sesotho: GPT, kapa Generative Pre-trained Transformer, ke mmotlolo o tsebahalang wa ho hlahisa mongolo wa puo ya tlhaho. E latelang ke mokhoa oa motheo oa mokhoa oa ho sebelisa mohlala oa GPT bakeng sa tlhahiso ea mongolo: 1. Mohato oa pele oa ho sebelisa mohlala oa GPT ke ho khetha mohlala o nepahetseng o koetlisitsoeng esale pele o lumellanang le litlhoko tsa mosebetsi. Ho na le mefuta e maloa ea mefuta ea GPT e nang le boholo bo fapaneng, e koetlisitsoeng mekhatlong e fapaneng. 2. Ka mora ho khetha mohlala o loketseng, mohato o latelang ke ho lokisetsa mongolo oa ho kenya, o tsejoang hape e le prompt or context. Mongolo o kenang o ka ba ka sebopeho sa polelo, serapa, kapa lirapa tse maloa, ho ipapisitsoe le sephetho se lakatsehang. Boleng ba sengolwa sa tlhahiso bo itshetlehile haholo hodima boleng ba mongolo o kenngwang, kahoo ho bohlokwa ho fana ka molaetsa o nepahetseng le o momahaneng. 3. Hang ha mongolo oa ho kenya o se o lokisitsoe, mohlala oa GPT o tšoaea mongolo oa ho kenya, o o arole ka li-tokens ka bomong, ho etsa mapa oa letšoao le leng le le leng ho palo ea lona e lekanang le palo ka ho sebelisa pokello ea mantsoe e hlalositsoeng esale pele. 4. Sengoloa sa tokenized se kenngoa mofuteng oa GPT, moo se sebetsanang le ho kenya letsoho ka likarolo tse maloa tsa ho itlhokomela le li-feed-forward neural network layers. Sena se hlahisa kabo ea monyetla holima li-tokens tse latelang tse ka khonehang bakeng sa boemo bo bong le bo bong. 5. Ho fanoe ka kabo ea monyetla, letšoao le latelang le khethiloe ho sebelisoa leano la ho etsa li-decoding joalo ka meharo kapa patlo ea maballo, ka boemo bo itseng ba tšohanyetso bo hlahisitsoeng ho sebelisoa paramethara ea mocheso. Letšoao le khethiloeng le eketsoa ho kenyelletsong. 6. Mohato o ka holimo o phetoa ho fihlela bolelele ba tlhahiso e lakatsehang bo finyelloa kapa letšoao la ho emisa le hlahisoa. Sephetho se hlahisoang se khutlisetsoa mongolong o ka baloang ke batho, e leng se hlahisang mongolo oa puo ea tlhaho. 7. Boleng ba mongolo o hlahisitsoeng bo ka ntlafatsoa ka ho eketsehileng ka ho lokisa hantle mokhoa oa GPT mosebetsing o tsoanang, ho o fa boitsebiso bo eketsehileng ba koetliso bo loketseng ho o fetola ho latela tlhahiso e lakatsehang. Tsebetso ena e ka bitsa chelete e ngata, kahoo ho ka hlokahala hardware e matla ho hlahisa mongolo kapele. Leha ho le joalo, mefuta ea GPT ke mokhoa oa morao-rao bakeng sa tlhahiso ea mongolo oa puo ea tlhaho, e fanang ka liphello tse lumellanang le tse hlakileng tse nang le liphello tse tsotehang. 