<prompt> Hlalosa mekhahlelo e meli e fapaneng ea koetliso ea GPT.nan  <response>: Instruction in English: Explain the two different phases of GPT training. . Response in English: GPT Generative Pre-training Transformer is a type of neural network-based language model that is widely used in natural language processing tasks. The training process of GPT consists of two distinct phases: pre-training and fine-tuning.1. Pre-training Phase: In the pre-training phase, GPT model is trained on a large amount of text data. During this phase, the model learns to predict the next word in a sequence based on the context of the words that came before it. The pre-training phase is typically unsupervised or self-supervised, meaning that no explicit labels are provided to the model. Instead, the model is trained to predict the masked tokens in the input sequence, a process known as Masked Language Modeling MLM. The model uses the contextual information from the words surrounding the masked token to make its prediction. After pre-training, the model has a good understanding of the semantic and syntactic structure of the language and can generate coherent and contextually appropriate texts.2. Fine-tuning Phase: In the fine-tuning phase, the pre-trained model is further trained on a smaller, task-specific dataset. During this phase, the weights of the model are adjusted to improve its performance on the specific task it is being trained for. This could be a wide range of natural language processing tasks such as text classification, sentiment analysis, named entity recognition, translation, and others. Unlike pre-training, fine-tuning is a supervised process, meaning that the model is trained using labeled data. The model uses the knowledge it acquired during the pre-training phase to perform the specific task it is being fine-tuned for.In summary, GPT model training consists of two phases: an unsupervised pre-training phase where the model learns the general structure of the language, and a supervised fine-tuning phase where the model is adapted to perform a specific natural language processing task. . Response in Sesotho: GPT Generative Pre-training Transformer ke mofuta oa puo e thehiloeng ho marang-rang ea neural e sebelisoang haholo mesebetsing ea puo ea tlhaho. Mokhoa oa koetliso oa GPT o na le mekhahlelo e meli e fapaneng: koetliso ea pele le tokiso e ntle. 1. Mokhahlelo oa Pele ho Koetliso: Mokhahlelong oa pele ho koetliso, mohlala oa GPT o koetlisoa ka bongata bo boholo ba data ea mongolo. Nakong ena, mohlala o ithuta ho bolela esale pele lentsoe le latelang ka tatellano ho latela moelelo oa mantsoe a tlileng pele ho lona. Karolo ea pele ho koetliso hangata ha e laoloe kapa e itlhokomela, ho bolelang hore ha ho li-labels tse hlakileng tse fanoang ho mohlala. Ho e-na le hoo, mohlala o koetliselitsoe ho bolela esale pele li-tokens tse koahetsoeng ka tatellano ea ho kenya, mokhoa o tsejoang e le Masked Language Modeling MLM. Mohlala o sebelisa lintlha tsa maemo ho tsoa ho mantsoe a potileng letsoao le koahetsoeng ho etsa ponelopele ea lona. Ka mora thupelo, mmotlolo o na le kutlwisiso e ntle ya sebopeho sa puo le puo ya puo mme a ka hlahisa dingolwa tse hokahaneng le maemo a nepahetseng. 2. Mokhahlelo oa Tokiso e Ntle: Mokhahlelong oa tokiso e ntle, moetso o koetlisitsoeng esale pele o rupeloa ho ea ka pokello ea lintlha tse nyane, tse ikhethileng. Nakong ea mohato ona, litekanyo tsa mohlala li fetoloa ho ntlafatsa tsebetso ea eona mosebetsing o khethehileng oo o o koetlisetsoang. Ena e ka ba mefuta e mengata e fapaneng ea mesebetsi ea tsebetso ea puo ea tlhaho joalo ka ho hlophisa mongolo, tlhahlobo ea maikutlo, temoho ea mokhatlo, phetolelo, le tse ling. Ho fapana le koetliso ea pele, ho lokisa hantle ke mokhoa o hlokometsoeng, ho bolelang hore mohlala o koetlisoa ka boitsebiso bo ngotsoeng. Moetso ona o sebelisa tsebo eo o e fumaneng nakong ea mokhahlelo oa pele ho koetliso ho etsa mosebetsi o itseng oo o ntseng o o lokisetsoa hantle. Ka kakaretso, koetliso ea mohlala oa GPT e na le mekhahlelo e meli: mohato o sa laoleheng oa koetliso ea pele moo mohlala o ithutang sebopeho se akaretsang sa puo, le mohato oa tokiso e hlokometsoeng moo mohlala o lokiselitsoeng ho etsa mosebetsi o itseng oa puo ea tlhaho. 