Maswali ya Kuunganisha Mfano

Majibu na maelezo huonekana unapojibu maswali.

1. Kuunganisha kwa mfano kunachanganya nini kimsingi?
   • Uzito (vigezo) vya mifano mingi
   • Seti za data za mafunzo za miundo mingi
   • Utabiri wa matokeo ya miundo mingi kwa wakati wa makisio
   • Viongeza kasi vya vifaa vinavyotumika kwa mafunzo
2. 'Vekta ya kazi' inaelezewa vyema kama:
   • Orodha ya kazi ambazo mfano unaweza kufanya
   • Tofauti kati ya uzani wa modeli iliyowekwa vizuri na uzani wa modeli yake ya msingi
   • Vekta ya tokeni za ingizo za kazi
   • Upinde rangi ulikokotolewa wakati wa hatua moja ya mafunzo
3. In the described weight-space merging methods, what can be avoided?
   • Evaluation of the merged checkpoint
   • Compatibility requirements between model weights
   • Additional gradient training and access to the original training data
   • Testing the result on intended tasks
4. How do TIES-Merging and DARE differ in addressing parameter interference?
   • Both use the same sign-voting method and differ only in scale
   • DARE uses prompt engineering while TIES retrains on joint data
   • Both average model outputs instead of changing weights
   • They use different techniques: TIES resolves sign conflicts, while DARE drops and rescales task-vector entries
5. Sharti kuu la mifano miwili kuunganishwa kwa mafanikio ni kwamba:
   • Shiriki usanifu sawa na kwa kawaida hushuka kutoka kwa muundo wa kawaida wa msingi
   • Walipewa mafunzo juu ya data sawa
   • Kuwa na idadi sawa ya hatua za mafunzo
   • Tumia saizi sawa ya msamiati wa tokenizer pekee
6. In task arithmetic, what do coefficients control when combining task vectors?
   • Which tokenizer each checkpoint must use
   • How strongly each parameter-change vector contributes to the merged checkpoint
   • How many training examples are generated
   • Whether an evaluation set is required
7. What are the three main stages named by TIES-Merging?
   • Tokenize text, infer emotions, and export audio
   • Quantize weights, shard GPUs, and route network packets
   • Trim small changes, elect a sign direction, then merge aligned parameter updates
   • Train a reward model, collect labels, and run PPO
8. Why should teams evaluate each task after a model merge?
   • Merging guarantees every benchmark improves
   • Evaluation is needed only to calculate the file size
   • A merged model cannot be run without retraining
   • Combining weight updates can create interference or reduce performance on a task