जाम्बा हाइब्रिड ट्रांसफार्मर-माम्बा मॉडल प्रश्नोत्तरी

जैसे ही आप प्रश्नोत्तरी लेते हैं उत्तर और स्पष्टीकरण दिखाई देते हैं।

1. जांबा किन दो कोर परत प्रकारों को आपस में जोड़ता है?
   • संवेगात्मक और पूलिंग परतें
   • आरएनएन और सीएनएन परतें
   • ट्रांसफार्मर का ध्यान और मांबा राज्य-अंतरिक्ष परतें
   • प्रसार और ध्यान परतें
2. सक्रिय मापदंडों को कम रखते हुए क्षमता बढ़ाने के लिए जांबा किस अतिरिक्त तकनीक का उपयोग करता है?
   • विशेषज्ञों का मिश्रण (एमओई)
   • ज्ञान आसवन
   • परिमाणीकरण-जागरूक प्रशिक्षण
   • मानवीय प्रतिक्रिया से सुदृढीकरण सीखना
3. How does a Mamba layer's recurrent representation differ from an attention layer's KV cache?
   • It removes every model parameter from memory
   • It skips all tokens beyond the first one
   • It stores an independent full copy of every earlier attention matrix
   • It maintains a fixed-size recurrent state instead of retaining a growing set of past keys and values
4. पहला जाम्बा मॉडल किस संदर्भ विंडो का समर्थन करता था?
   • 4K टोकन
   • 256K टोकन
   • 32K टोकन
   • 8 मिलियन टोकन
5. What is a reason to retain attention components alongside state-space layers in a Jamba-style hybrid?
   • To combine different access and representation mechanisms while evaluating their quality-efficiency tradeoff
   • To guarantee perfect retrieval of every token on every task
   • To make all attention computation independent of sequence length
   • To eliminate the need for any model evaluation
6. How should Jamba's reported long-context memory benefit be evaluated for deployment?
   • Assume every 256K request fits any single GPU
   • Count only the recurrent state while ignoring weights and attention cache
   • Measure the actual model, precision, batch size, context, and hardware configuration
   • Treat every hybrid model as having zero sequence-state cost
7. Does original Jamba's use of many Mamba layers make the whole model's sequence memory constant as input length grows?
   • Yes; every hybrid attention cache has a fixed one-token size
   • No; its retained attention layers still store a growing KV cache
   • Yes; model weights disappear once the sequence becomes long
   • Only the tokenizer's file size changes
8. Why are total MoE parameters and active parameters per token different quantities in Jamba?
   • Every stored expert is necessarily deleted when inactive
   • A token must run all experts regardless of routing
   • The model's supported context directly sets its vocabulary size
   • Routing can activate only a subset of stored experts for each token