make ollamaInferenceEngine handle return_meta_data

src/unitxt/inference.py

@@ -1461,7 +1461,18 @@ def _infer(
                 options=args,
             )
             results.append(response)
-
+        if return_meta_data:
+            return [
+                TextGenerationInferenceOutput(
+                    prediction=element["message"]["content"],
+                    generated_text=element["message"]["content"],
+                    input_tokens=element.get("prompt_eval_count", 0),
+                    output_tokens=element.get("eval_count", 0),
+                    model_name=self.model,
+                    inference_type=self.label,
+                )
+                for element in results
+            ]
         return [element["message"]["content"] for element in results]
 
 

tests/inference/test_inference_engine.py

@@ -159,7 +159,7 @@ def test_llava_inference_engine(self):
 
     def test_watsonx_inference(self):
         model = WMLInferenceEngineGeneration(
-            model_name="google/flan-t5-xl",
+            model_name="ibm/granite-3-8b-instruct",
             data_classification_policy=["public"],
             random_seed=111,
             min_new_tokens=1,
@@ -193,7 +193,7 @@ def test_watsonx_inference_with_external_client(self):
         from ibm_watsonx_ai.client import APIClient, Credentials
 
         model = WMLInferenceEngineGeneration(
-            model_name="google/flan-t5-xl",
+            model_name="ibm/granite-3-8b-instruct",
             data_classification_policy=["public"],
             random_seed=111,
             min_new_tokens=1,
@@ -279,7 +279,7 @@ def test_option_selecting_by_log_prob_inference_engines(self):
         ]
 
         watsonx_engine = WMLInferenceEngineGeneration(
-            model_name="meta-llama/llama-3-2-1b-instruct"
+            model_name="ibm/granite-3-8b-instruct"
         )
 
         for engine in [watsonx_engine]:
@@ -383,7 +383,7 @@ def test_lite_llm_inference_engine(self):
 
     def test_lite_llm_inference_engine_without_task_data_not_failing(self):
         LiteLLMInferenceEngine(
-            model="watsonx/meta-llama/llama-3-2-1b-instruct",
+            model="watsonx/meta-llama/llama-3-2-11b-vision-instruct",
             max_tokens=2,
             temperature=0,
             top_p=1,

tests/library/test_metrics.py

@@ -2708,7 +2708,7 @@ def test_perplexity(self):
             metric=perplexity_question, predictions=prediction, references=references
         )
         self.assertAlmostEqual(
-            first_instance_target, outputs[0]["score"]["instance"]["score"]
+            first_instance_target, outputs[0]["score"]["instance"]["score"], places=5
         )
 
     def test_fuzzyner(self):