Maswali ya Kudhibiti ya Mirostat Perplexity

Majibu na maelezo huonekana unapojibu maswali.

1. What does Mirostat try to control during generation?
   • A target level of output perplexity or surprise
   • Idadi ya vigezo vya mfano
   • The training dataset size
   • Kiwango cha joto cha GPU
2. Je, Mirostat ina tofauti gani kimsingi na sampuli za kawaida za top-k au top-p?
   • Kamwe haitumii kubahatisha
   • Hurekebisha upunguzaji kwa nguvu kwa kutumia maoni badala ya mkato usiobadilika
   • Inafanya kazi kwenye picha tu
   • Inahitaji kufundisha tena mfano
3. Katika Mirostat, parameta eta ina jukumu gani?
   • Inaweka ukubwa wa msamiati
   • Inarekebisha urefu wa pato
   • Ni kiwango cha kujifunza kinachodhibiti kasi ya kizingiti inabadilika
   • Inalemaza sampuli
4. Ni uboreshaji gani kuu wa toleo la 2 la Mirostat juu ya toleo la 1?
   • Inazalisha picha pia
   • Inahitaji kumbukumbu zaidi
   • Huondoa kigezo cha tau
   • Inapunguza dhana ya usambazaji wa Zipfian kwa udhibiti rahisi na thabiti zaidi
5. Ikiwa Mirostat itagundua kuwa matokeo yanatabirika sana, itakuwa:
   • Legeza upunguzaji ili kukubali tokeni tofauti zaidi
   • Kaza upunguzaji ili kuruhusu tokeni chache
   • Acha kuzalisha mara moja
   • Ongeza ukubwa wa mfano
6. Which Mirostat settings does current llama.cpp expose?
   • Tokenizer vocabulary and number of layers
   • Mode, target tau, and learning-rate eta
   • Only the prompt length
   • Training epochs and validation loss
7. What does eta control in Mirostat’s feedback update?
   • The size of the model vocabulary
   • The target number of output tokens
   • How strongly the threshold changes in response to the observed error
   • The probability of a correct factual answer
8. What does a Mirostat target not guarantee by itself?
   • That the sampler reads token probabilities
   • That the setting can influence surprise
   • That the output contains tokens
   • That the generated text is factually correct