<prompt> Bapisa le ho bapisa likarolo tsa mofuta oa GPT le marang-rang a tloaelehileng a methapo.nan  <response>: Instruction in English: Compare and contrast the features of a GPT model and a recurrent neural network. . Response in English: A GPT model or Generative Pre-trained Transformer model is a type of neural network architecture that belongs to the class of transformer models, which are large in size and designed to perform well with long range dependency of the data. In contrast, Recurrent Neural Networks, or RNNs, are another type of neural network architecture that processes inputs sequentially, with the output of one step becoming an input to the next step.One difference between the two models is their handling of sequential data. GPT models can process the entire sequence all at once, removing the need for time dependencies, while RNNs must process the sequence one element at a time. As a result, GPT models are often much faster than RNNs.Another difference is in their ability to capture long-range dependencies. GPT models were designed to handle long-range dependencies through the use of attention mechanisms, whereas RNNs can struggle to learn long-term dependencies because of the vanishing gradient problem.GPT models are pre-trained on large datasets, providing them with a good foundation for transfer learning, i.e., using the same model but adjusting the output layers and learning through fine-tuning for a specific task. In contrast, RNNs are usually trained from scratch on a specific task.In terms of architecture, GPT models are composed of many layers of transformers that use multi-head attention to allow the model to focus on different parts of the input at each layer. RNNs, on the other hand, have a looping mechanism and a hidden state that allows them to remember information from the previous time steps.In summary, GPTs and RNNs are both types of neural network architectures but differ in their handling of sequential data, ability to learn long-range dependencies, and architecture. GPT models are typically faster and better at learning long-range dependencies, but RNNs may be preferable for certain tasks and may require less computational resources. . Response in Sesotho: Mohlala oa GPT kapa Generative Pre-trained Transformer ke mofuta oa meralo ea marang-rang ea neural eo e leng ea sehlopha sa mefuta ea li-transformer, tse kholo ka boholo le tse etselitsoeng ho sebetsa hantle ka nako e telele e itšetlehileng ka data. Ka lehlakoreng le leng, Recurrent Neural Networks, kapa RNNs, ke mofuta o mong oa meralo ea marang-rang ea neural e sebetsanang le lintho tse kenang ka tatellano, me tlhahiso ea mohato o le mong e fetoha karolo ea mohato o latelang. Phapang e ngoe lipakeng tsa mefuta ena e meli ke ho sebetsana le data ea tatellano. Mefuta ea GPT e ka sebetsana le tatellano eohle ka nako e le ngoe, e tlosa tlhoko ea ho itšetleha ka nako, ha RNN e tlameha ho sebetsana le tatellano ea ntho e le ngoe ka nako. Ka lebaka leo, mefuta ea GPT hangata e potlakile ho feta RNNs. Phapang e ngoe ke bokhoni ba bona ba ho hapa litšepe tsa nako e telele. Mefuta ea GPT e ne e etselitsoe ho sebetsana le ho itšetleha ka nako e telele ka ho sebelisa mekhoa ea tlhokomelo, athe li-RNN li ka thatafalloa ho ithuta ho itšetleha ka nako e telele ka lebaka la bothata bo nyamelang. Mehlala ea GPT e koetlisitsoe esale pele ho li-dataset tse kholo, e ba fa motheo o motle oa ho ithuta ho fetisa, ke hore, ho sebelisa mohlala o le mong empa ho lokisa likarolo tsa tlhahiso le ho ithuta ka ho lokisa hantle bakeng sa mosebetsi o itseng. Ka lehlakoreng le leng, li-RNN hangata li koetlisoa ho tloha qalong ka mosebetsi o itseng. Mabapi le meralo ea meralo, mehlala ea GPT e entsoe ka likarolo tse ngata tsa li-transformer tse sebelisang tlhokomelo ea lihlooho tse ngata ho lumella mohlala hore o tsepamise maikutlo likarolong tse fapaneng tsa ho kenya letsoho karolong e ngoe le e ngoe. Ka lehlakoreng le leng, li-RNN li na le mochine oa looping le boemo bo patehileng bo ba lumellang hore ba hopole boitsebiso bo tsoang mehatong e fetileng ea nako. Ka bokhutšoanyane, GPTs le RNNs ke mefuta e meli ea meralo ea marang-rang ea neural empa e fapane ka ho sebetsana le data e latellanang, bokhoni ba ho ithuta ho itšetleha ka nako e telele, le boqapi. Hangata mefuta ea GPT e potlakile ebile e betere ho ithuta ho itšetleha ka nako e telele, empa li-RNN li ka ba molemo bakeng sa mesebetsi e itseng me li ka hloka lisebelisoa tse fokolang tsa khomphutha. 