<prompt> Bolela lintlha tse hlano tse susumetsang katleho ea mohlala oa GPT.nan  <response>: Instruction in English: Name five factors that influence the success of a GPT model. . Response in English: 1. Quality and Quantity of Training Data: A GPT model relies on vast amounts of high-quality training data to accurately predict the next word or sequence. The amount and quality of the data that the model is trained on can greatly influence its ability to make accurate predictions.2. Computing Power: GPT models are complex and require significant computational resources to train and run. The availability of powerful computer systems with high processing capability can influence the success of a GPT model.3. Hyperparameter Tuning: The selection of appropriate hyperparameters, such as learning rate, batch size, and number of hidden layers, can greatly affect the performance and success of a GPT model.4. Model Architecture: The architecture of the GPT model, such as the number of layers, the size of the hidden states, and the use of attention mechanisms, can play a crucial role in the models success. An effective architecture can improve the accuracy and efficiency of the model.5. Regularization Techniques: Regularization techniques such as dropout and weight decay can help prevent overfitting and improve the generalization ability of the GPT model. Effective use of these techniques can improve the models performance and success. . Response in Sesotho: 1. Boleng le Bongata ba Boitsebiso ba Koetliso: Moetso oa GPT o itšetlehile ka boitsebiso bo bongata ba koetliso ea boleng bo phahameng ho bolela esale pele ka nepo lentsoe kapa tatellano e latelang. Palo le boleng ba lintlha tseo mohlala o koetlisitsoeng ka tsona li ka ama bokhoni ba eona ba ho bolela esale pele ka nepo. 2. Matla a Khomphutha: Mefuta ea GPT e rarahane me e hloka lisebelisoa tsa bohlokoa tsa khomphutha ho koetlisa le ho sebetsa. Ho fumaneha ha litsamaiso tse matla tsa likhomphutha tse nang le bokhoni bo phahameng ba ho sebetsa ho ka susumetsa katleho ea mohlala oa GPT. 3. Hyperparameter Tuning : Khetho ea li-hyperparameter tse loketseng, tse kang tekanyo ea ho ithuta, boholo ba batch, le palo ea lihlopha tse patehileng, li ka ama haholo tsebetso le katleho ea mohlala oa GPT. 4. Moetso oa Architecture: Mehaho ea mohlala oa GPT, joalo ka palo ea li-layers, boholo ba libaka tse patehileng, le tšebeliso ea mekhoa ea ho ela hloko, e ka bapala karolo ea bohlokoa katlehong ea mohlala. Mohaho o atlehileng o ka ntlafatsa ho nepahala le ho sebetsa hantle ha mohlala. 5. Mekhoa ea ho Regularization: Mekhoa ea kamehla e kang ho tlohela sekolo le ho senyeha ha boima ba mele e ka thusa ho thibela ho feta le ho ntlafatsa bokhoni ba kakaretso ba mohlala oa GPT. Tšebeliso e nepahetseng ea mekhoa ena e ka ntlafatsa tsebetso le katleho ea mohlala. 