Muenzaniso Kubatanidza Quiz

Mhinduro uye tsananguro zvinoonekwa paunenge uchitora bvunzo.

1. Kubatanidzwa kwemuenzaniso kunonyanya kusanganisirei?
   • Zviyero (zviyero) zvemhando dzakawanda
   • Iyo dataset yekudzidziswa yemamodheru akawanda
   • Mafungiro ekubuda kwemamodheru akawanda panguva yekufungidzira
   • Iyo hardware accelerators inoshandiswa pakudzidzisa
2. A 'task vector' inotsanangurwa zvakanyanya se:
   • Rondedzero yemabasa anogona kuitwa nemuenzaniso
   • Musiyano uripo pakati pehuremu hwemodhi-yakarongeka uye huremu hwemodhi yayo
   • A vector of input tokens yebasa
   • Iyo gradient yakaverengerwa panguva imwe nhanho yekudzidziswa
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. Chinhu chakakosha chekuti mamodheru maviri abatanidzwe zvinobudirira ndechekuti iwo:
   • Goverana zvivakwa zvakafanana uye kazhinji kudzika kubva kune yakajairwa base modhi
   • Vakadzidziswa pane zvakafanana data
   • Iva nenhamba chaiyo yematanho ekudzidzira
   • Shandisa saizi yemazwi tokenizer yakafanana chete
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