Adapt examples to use LLAMA 3.1 to 3.3

Signed-off-by: Martín Santillán Cooper <msantillancooper@ibm.com>

Author: martinscooper

docs/docs/llm_as_judge.rst

@@ -46,7 +46,7 @@ An LLM as a Judge metric consists of several essential components:
 1. The judge model, such as *Llama-3-8B-Instruct* or *gpt-3.5-turbo*, which evaluates the performance of other models.
 2. The platform responsible for executing the judge model, such as Huggingface, OpenAI API and IBM's deployment platforms such as WatsonX and RITS.
    A lot of these model and catalog combinations are already predefined in our catalog. The models are prefixed by metrics.llm_as_judge.direct followed by the platform and the model name.
-   For instance, *metrics.llm_as_judge.direct.rits.llama3_1_70b* refers to *llama3 70B* model that uses RITS deployment service.
+   For instance, *metrics.llm_as_judge.direct.rits.llama3_3_70b* refers to *llama3 70B* model that uses RITS deployment service.
 
 3. The criteria to evaluate the model's response. There are predefined criteria in the catalog and the user can also define a custom criteria.
    Each criteria specifies fine-grained options that help steer the model to evaluate the response more precisely.
@@ -86,7 +86,7 @@ We pass the criteria to the judge model's metric as criteria and the question as
 
     criteria = "metrics.llm_as_judge.direct.criteria.answer_relevance"
     metrics = [
-    f"metrics.llm_as_judge.direct.rits.llama3_1_70b[criteria={criteria}, context_fields=[question]]"
+    f"metrics.llm_as_judge.direct.rits.llama3_3_70b[criteria={criteria}, context_fields=[question]]"
     ]
 
 Once the metric is created, a dataset is created for the appropriate task.
@@ -155,13 +155,13 @@ Below is an example where the user mandates that the model respond with the temp
 End to end Direct example
 ----------------------------
 Unitxt can also obtain model's responses for a given dataset and then run LLM-as-a-judge evaluations on the model's responses.
-Here, we will get *llama-3.2 1B* instruct's responses and then evaluate them for answer relevance, coherence and conciseness using *llama3_1_70b* judge model
+Here, we will get *llama-3.2 1B* instruct's responses and then evaluate them for answer relevance, coherence and conciseness using *llama3_3_70b* judge model
 
 .. code-block:: python
 
     criteria = ["answer_relevance", "coherence", "conciseness"]
     metrics = [
-    "metrics.llm_as_judge.direct.rits.llama3_1_70b"
+    "metrics.llm_as_judge.direct.rits.llama3_3_70b"
     "[context_fields=[context,question],"
     f"criteria=metrics.llm_as_judge.direct.criteria.{criterion},"
     f"score_prefix={criterion}_]"
@@ -298,7 +298,7 @@ Below is an example where we compare the responses of three models for two quest
             reference_fields={"criteria": Any},
             prediction_type=List[str],
             metrics=[
-                "metrics.llm_as_judge.pairwise.watsonx.llama3_1_70b[context_fields=question,criteria_field=criteria]"
+                "metrics.llm_as_judge.pairwise.watsonx.llama3_3_70b[context_fields=question,criteria_field=criteria]"
             ],
             default_template=NullTemplate(),
         ),

examples/evaluate_existing_dataset_by_llm_as_judge_direct.py

@@ -14,7 +14,7 @@
 # We set loader_limit to 20 to reduce download time.
 criteria = ["answer_relevance", "coherence", "conciseness"]
 metrics = [
-    "metrics.llm_as_judge.direct.rits.llama3_1_70b"
+    "metrics.llm_as_judge.direct.rits.llama3_3_70b"
     "[context_fields=[context,question],"
     f"criteria=metrics.llm_as_judge.direct.criteria.{criterion}]"
     for criterion in criteria

examples/evaluate_llm_as_judge_direct_criteria_from_dataset.py

@@ -28,7 +28,7 @@
         reference_fields={"criteria": Any},
         prediction_type=str,
         metrics=[
-            "metrics.llm_as_judge.direct.watsonx.llama3_1_70b[context_fields=question,criteria_field=criteria]"
+            "metrics.llm_as_judge.direct.watsonx.llama3_3_70b[context_fields=question,criteria_field=criteria]"
         ],
     ),
 )

examples/evaluate_llm_as_judge_direct_predefined_criteria.py

@@ -11,7 +11,7 @@
 
 criterion = "metrics.llm_as_judge.direct.criteria.answer_relevance"
 metrics = [
-    f"metrics.llm_as_judge.direct.rits.llama3_1_70b[criteria={criterion}, context_fields=[question]]"
+    f"metrics.llm_as_judge.direct.rits.llama3_3_70b[criteria={criterion}, context_fields=[question]]"
 ]
 
 dataset = create_dataset(

examples/evaluate_llm_as_judge_pairwise_criteria_from_dataset.py

@@ -29,7 +29,7 @@
         reference_fields={"criteria": Any},
         prediction_type=List[str],
         metrics=[
-            "metrics.llm_as_judge.pairwise.rits.llama3_1_70b[context_fields=question,criteria_field=criteria]"
+            "metrics.llm_as_judge.pairwise.rits.llama3_3_70b[context_fields=question,criteria_field=criteria]"
         ],
         default_template=NullTemplate(),
     ),

examples/evaluate_llm_as_judge_pairwise_predefined_criteria.py

@@ -29,7 +29,7 @@
         reference_fields={"criteria": Any},
         prediction_type=List[str],
         metrics=[
-            "metrics.llm_as_judge.pairwise.watsonx.llama3_1_70b[context_fields=question,criteria_field=criteria]"
+            "metrics.llm_as_judge.pairwise.watsonx.llama3_3_70b[context_fields=question,criteria_field=criteria]"
         ],
         default_template=NullTemplate(),
     ),

src/unitxt/assistant/assessment/assistant_inference_engine.py

@@ -33,7 +33,7 @@ def _infer(self, dataset: Union[List[Dict[str, Any]], Dataset], return_meta_data
 
     criteria = "metrics.llm_as_judge.direct.criteria.answer_completeness"
     metrics = [
-        f"metrics.llm_as_judge.direct.rits.llama3_1_70b[criteria={criteria}, context_fields=[answers]]"
+        f"metrics.llm_as_judge.direct.rits.llama3_3_70b[criteria={criteria}, context_fields=[answers]]"
     ]
 
     dataset = create_dataset(

tests/library/test_metrics.py

@@ -2945,7 +2945,7 @@ def test_llm_as_judge(self):
 
         criterion = "metrics.llm_as_judge.direct.criteria.answer_relevance"
         metrics = [
-            f"metrics.llm_as_judge.direct.rits.llama3_1_70b[criteria={criterion}, context_fields=[question]]"
+            f"metrics.llm_as_judge.direct.rits.llama3_3_70b[criteria={criterion}, context_fields=[question]]"
         ]
 
         dataset = create_dataset(