tests

cognitive_mapping_probe/concepts.py

@@ -26,7 +26,8 @@ def get_concept_vector(llm: LLM, concept: str, baseline_words: List[str] = BASEL
         inputs = llm.tokenizer(prompt, return_tensors="pt").to(llm.model.device)
         # Ensure the operation does not build a computation graph
         with torch.no_grad():
-            outputs = ll.model(**inputs, output_hidden_states=True)
+            # KORREKTUR: Hier stand fälschlicherweise 'll.model'. Korrigiert zu 'llm.model'.
+            outputs = llm.model(**inputs, output_hidden_states=True)
         # We take the hidden state from the last layer [-1], for the last token [0, -1, :]
         last_hidden_state = outputs.hidden_states[-1][0, -1, :].cpu()
         assert last_hidden_state.shape == (llm.config.hidden_size,), \