Add method to print TLM model name

cleanlab_studio/internal/constants.py

@@ -12,6 +12,7 @@
     "claude-3-sonnet",
     "claude-3.5-sonnet",
 ]
+_TLM_DEFAULT_MODEL: str = "gpt-4o-mini"
 _TLM_MAX_RETRIES: int = 3  # TODO: finalize this number
 TLM_MAX_TOKEN_RANGE: Tuple[int, int] = (64, 512)  # (min, max)
 TLM_NUM_CANDIDATE_RESPONSES_RANGE: Tuple[int, int] = (1, 20)  # (min, max)

cleanlab_studio/studio/trustworthy_language_model.py

@@ -24,6 +24,7 @@
 from cleanlab_studio.internal.constants import (
     _TLM_MAX_RETRIES,
     _VALID_TLM_QUALITY_PRESETS,
+    _TLM_DEFAULT_MODEL,
 )
 from cleanlab_studio.internal.tlm.concurrency import TlmRateHandler
 from cleanlab_studio.internal.tlm.validation import (
@@ -97,10 +98,16 @@ def __init__(
             )
 
         self._return_log = False
-        if options is not None:
-            validate_tlm_options(options)
-            if "log" in options.keys():
-                self._return_log = True
+
+        options_dict = options or {}
+        validate_tlm_options(options_dict)
+        if "log" in options_dict.keys():
+            self._return_log = True
+
+        # explicitly specify the default model
+        self._options = {**{"model": _TLM_DEFAULT_MODEL}, **options_dict}
+
+        self._quality_preset = quality_preset
 
         if timeout is not None and not (isinstance(timeout, int) or isinstance(timeout, float)):
             raise ValidationError("timeout must be a integer or float value")
@@ -110,8 +117,6 @@ def __init__(
 
         is_notebook_flag = is_notebook()
 
-        self._quality_preset = quality_preset
-        self._options = options
         self._timeout = timeout if timeout is not None and timeout > 0 else None
         self._verbose = verbose if verbose is not None else is_notebook_flag
 
@@ -616,6 +621,10 @@ async def _get_trustworthiness_score_async(
                 return None
             raise e
 
+    def get_model_name(self) -> str:
+        """Returns the underlying LLM used to obtain responses and scoring trustworthiness."""
+        return cast(str, self._options["model"])
+
 
 class TLMResponse(TypedDict):
     """A typed dict containing the response, trustworthiness score and additional logs from the Trustworthy Language Model.

cleanlab_studio/utils/tlm_hybrid.py

@@ -4,7 +4,7 @@
 **This module is not meant to be imported and used directly.** Instead, use [`Studio.TLMHybrid()`](/reference/python/studio/#method-tlmhybrid) to instantiate a [TLMHybrid](#class-tlmhybrid) object, and then you can use the methods like [`prompt()`](#method-prompt) documented on this page.
 """
 
-from typing import List, Optional, Union, cast, Sequence
+from typing import List, Dict, Optional, Union, cast, Sequence
 import numpy as np
 
 from cleanlab_studio.errors import ValidationError
@@ -253,3 +253,10 @@ def _try_batch_score(
             ]
         else:
             raise ValueError(f"score_response has invalid type")
+
+    def get_model_names(self) -> Dict[str, str]:
+        """Returns the underlying LLMs used to obtain responses and scoring trustworthiness."""
+        return {
+            "response_model": self._tlm_response.get_model_name(),
+            "score_model": self._tlm_score.get_model_name(),
+        }