Set relevance to zero when completion is empty, and lower the logging…

… level of save state to debug

prompting/base/neuron.py

@@ -165,11 +165,11 @@ def should_set_weights(self) -> bool:
         ) > self.config.neuron.epoch_length
 
     def save_state(self):
-        bt.logging.warning(
+        bt.logging.debug(
             "save_state() not implemented for this neuron. You can implement this function to save model checkpoints or other useful data."
         )
 
     def load_state(self):
-        bt.logging.warning(
+        bt.logging.debug(
             "load_state() not implemented for this neuron. You can implement this function to load model checkpoints or other useful data."
         )

prompting/rewards/relevance.py

@@ -32,13 +32,17 @@ def reward(
         self, reference: str, completions: List[str]
     ) -> BatchRewardOutput:
         reference_embedding = self.model.encode(reference, to_numpy=False)
-        completions_embeddings = self.model.encode(completions, to_numpy=False)
         rewards = []
         timings = []
 
-        for emb in completions_embeddings:
+        for comp in completions:
             t0 = time.time()
-            rewards.append(cosine_similarity(reference_embedding.reshape(1, -1), emb.reshape(1, -1)))
+            score = 0
+            if comp:
+                emb = self.model.encode(completions, to_numpy=False)
+                score = cosine_similarity(reference_embedding.reshape(1, -1), emb.reshape(1, -1))
+
+            rewards.append(score)
             timings.append(time.time() - t0)
 
         output = BatchRewardOutput(