Kuis Decoding Spekulatif

Jawaban dan penjelasan muncul saat Anda mengikuti kuis.

1. How does speculative decoding speed up generation from a large model?
   • Model draf kecil menebak beberapa token yang diverifikasi oleh model besar secara paralel
   • Mengompresi model menjadi setengah ukurannya secara permanen
   • Menghapus mekanisme perhatian
   • Melatih model pada lebih banyak data
2. For a large model at low batch size, why can one-token autoregressive decoding be slow?
   • Each token requires internet access
   • Repeated reads of large model weights can make decoding memory-bandwidth bound
   • The model permanently stores a full copy of every possible output
   • The tokenizer performs the model’s matrix multiplications
3. In greedy speculative decoding, what happens at the first token where draft and target predictions disagree?
   • The whole conversation is thrown away and restarted from scratch
   • Draf model segera dilatih ulang
   • The matching prefix is kept, the target token is used at the mismatch, and later draft tokens are discarded
   • Kualitas keluaran menurun secara permanen
4. Under exact speculative sampling, how does the output distribution compare with sampling from the target model alone?
   • It is always lower quality but faster
   • It is identical only when the draft model is larger
   • It becomes deterministic regardless of target sampling
   • It is identical in distribution
5. Apa yang paling langsung menentukan percepatan dari penguraian kode spekulatif?
   • Tingkat penerimaan dugaan rancangan model
   • Warna GPU
   • Panjang nama model
   • Waktu hari ini
6. Apa kesamaan Medusa dan EAGLE sebagai pendekatan?
   • Mereka membutuhkan perangkat keras kuantum
   • Hal ini menghilangkan kebutuhan akan rancangan model mandiri yang terpisah
   • Mereka menghapus model target
   • Mereka hanya mengerjakan gambar
7. What should a serving team measure before claiming speculative decoding will speed up its workload?
   • Only the target model’s parameter count
   • Only whether the draft produces fluent text
   • Draft cost, target verification cost, accepted tokens and end-to-end latency under representative traffic
   • The number of tokenizer files
8. In exact speculative sampling, what happens when a proposed token is rejected?
   • The whole conversation is discarded
   • The target model is permanently changed
   • The rejected token is kept without verification
   • A correction step samples from the distribution needed to preserve the target model’s output distribution