truncate thinking tags in generations (#3145)

* feat: add postprocessing for generated text to strip stop sequences and thinking tokens

* nit

* fix: trim leading whitespace after stripping thinking tokens from generation

* feat: add think_end_token to model_args

* nit

* nit

* nit

* add to readme

* nit

README.md

@@ -5,7 +5,7 @@
 ---
 
 ## Latest News 📣
-
+- [2025/07] Added `think_end_token` arg to `hf` (token/str), `vllm` and `sglang` (str) for stripping CoT reasoning traces from models that support it.
 - [2025/03] Added support for steering HF models!
 - [2025/02] Added [SGLang](https://docs.sglang.ai/) support!
 - [2024/09] We are prototyping allowing users of LM Evaluation Harness to create and evaluate on text+image multimodal input, text output tasks, and have just added the `hf-multimodal` and `vllm-vlm` model types and `mmmu` task as a prototype feature. We welcome users to try out this in-progress feature and stress-test it for themselves, and suggest they check out [`lmms-eval`](https://github.com/EvolvingLMMs-Lab/lmms-eval), a wonderful project originally forking off of the lm-evaluation-harness, for a broader range of multimodal tasks, models, and features.

lm_eval/models/huggingface.py

@@ -34,6 +34,7 @@ from lm_eval.models.utils import (
     get_dtype,
     handle_stop_sequences,
     pad_and_concat,
+    postprocess_generated_text,
     stop_sequences_criteria,
 )
 
@@ -95,6 +96,9 @@ class HFLM(TemplateLM):
         autogptq: Optional[Union[bool, str]] = False,
         gptqmodel: Optional[bool] = False,
         gguf_file: Optional[str] = None,
+        # end token for thinking, either the string or int token id.
+        # splits to get response after this token (if provided).
+        think_end_token: Union[str, int, None] = None,
         **kwargs,
     ) -> None:
         super().__init__()
@@ -224,6 +228,11 @@ class HFLM(TemplateLM):
             self.model.eval()
             self.model.tie_weights()
 
+        self.think_end_token = (
+            int(think_end_token)
+            if (isinstance(think_end_token, str) and think_end_token.isdigit())
+            else think_end_token
+        )
         self.truncation = truncation
         self.logits_cache = logits_cache
         self.vocab_size = self.tokenizer.vocab_size
@@ -1427,15 +1436,30 @@ class HFLM(TemplateLM):
                 if self.backend == "causal":
                     cont_toks = cont_toks[context_enc.shape[1] :]
 
+                # Handle integer think_end_token: find last occurrence and strip tokens after it
+                if isinstance(self.think_end_token, int):
+                    think_token_indices = [
+                        i
+                        for i, token in enumerate(cont_toks)
+                        if token == self.think_end_token
+                    ]
+                    if think_token_indices:
+                        cont_toks = cont_toks[think_token_indices[-1] + 1 :]
+
                 s = self.tok_decode(cont_toks)
 
-                # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
-                for term in until:
-                    if len(term) > 0:
-                        # ignore '' separator,
-                        # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
-                        s = s.split(term)[0]
+                # Strip leading whitespace if we removed thinking tokens
+                if isinstance(self.think_end_token, int):
+                    s = s.lstrip()
 
+                # Apply post-processing: remove stop sequences and string-based thinking tokens
+                s = postprocess_generated_text(
+                    generation=s,
+                    stop=until,
+                    think_end_token=self.think_end_token
+                    if isinstance(self.think_end_token, str)
+                    else None,
+                )
                 res.append(s)
 
                 self.cache_hook.add_partial("generate_until", (context, gen_kwargs), s)

lm_eval/models/sglang_causallms.py

@@ -11,6 +11,7 @@ from lm_eval.api.registry import register_model
 from lm_eval.models.utils import (
     Collator,
     handle_stop_sequences,
+    postprocess_generated_text,
 )
 from lm_eval.utils import (
     get_rolling_token_windows,
@@ -59,6 +60,8 @@ class SGLangLM(TemplateLM):
         dp_size: int = 1,
         tp_size: int = 1,
         prefix_token_id: Optional[int] = None,
+        # End marker for thinking tags - splits to get response after this token (if provided).
+        think_end_token: Optional[str] = None,
         **kwargs,
     ):
         super().__init__()
@@ -74,6 +77,7 @@ class SGLangLM(TemplateLM):
             "Either context_length or max_model_len may be provided, but not both"
         )
         # Initialize your sglang model here
+        self.think_end_token = think_end_token
         self._max_length = (
             max_model_len if max_model_len is not None else context_length
         )
@@ -263,6 +267,9 @@ class SGLangLM(TemplateLM):
             # cache generations
             for output, context in zip(cont, context):
                 generated_text = output.get("text", "")
+                generated_text = postprocess_generated_text(
+                    generated_text, until, self.think_end_token
+                )
                 res.append(generated_text)
                 self.cache_hook.add_partial(
                     "generate_until", (context, gen_kwargs), generated_text

lm_eval/models/utils.py

@@ -852,3 +852,32 @@ def truncate_tokens(
         right_length = max_length - left_length
         return tokens[:left_length] + tokens[-right_length:]
     return None
+
+
+def postprocess_generated_text(
+    generation: str, stop: Union[list[str], str, None], think_end_token: Optional[str]
+) -> str:
+    """
+    Post-processes the generated text by stripping stop sequences and optional thinking markers.
+
+    Args:
+        generation (str): The generated text to be processed.
+        stop (Optional[list[str]]): Stop sequence(s) to remove. Text is truncated
+            at the first occurrence of any stop sequence.
+        think_end_token (Optional[str]): Token marking end of thinking section. If provided,
+            returns only the text after this token (discarding thinking content).
+
+    Returns:
+        str: The processed generation - text before stop sequences and after thinking sections.
+    """
+    if stop:
+        stop = [stop] if isinstance(stop, str) else stop
+        for term in stop:
+            if len(term) > 0:
+                # ignore '' separator,
+                # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
+                generation = generation.split(term)[0]
+    if think_end_token:
+        generation = generation.split(think_end_token)[-1].lstrip()
+
+    return generation

lm_eval/models/vllm_causallms.py

@@ -22,6 +22,7 @@ from lm_eval.models.utils import (
     Collator,
     configure_pad_token,
     handle_stop_sequences,
+    postprocess_generated_text,
     undistribute,
 )
 from lm_eval.utils import (
@@ -133,7 +134,10 @@ class VLLM(TemplateLM):
         device: str = "cuda",
         data_parallel_size: int = 1,
         lora_local_path: str = None,
-        enable_thinking: bool = False,
+        # VLLM: enable thinking tags in the prompt.
+        enable_thinking: bool = True,
+        # End marker for thinking tags - splits to get response after this token (if provided).
+        think_end_token: Optional[str] = None,
         max_lora_rank: int = 16,
         **kwargs,
     ):
@@ -148,6 +152,7 @@ class VLLM(TemplateLM):
         assert max_length is None or max_model_len is None, (
             "Either max_length or max_model_len may be provided, but not both"
         )
+        self.think_end_token = think_end_token
         self.V1 = os.environ.get("VLLM_USE_V1", "1") != "0"
         self._max_length = max_model_len if max_model_len is not None else max_length
         self.tensor_parallel_size = int(tensor_parallel_size)
@@ -630,11 +635,11 @@ class VLLM(TemplateLM):
 
             # cache generations
             for output, context in zip(cont, context):
-                generated_text = output.outputs[0].text
+                generated_text: str = output.outputs[0].text
                 # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
-                for term in until:
-                    if len(term) > 0:
-                        generated_text = generated_text.split(term)[0]
+                generated_text = postprocess_generated_text(
+                    generated_text, until, self.think_end_token
+                )
                 res.append(generated_text)
                 self.cache_hook.add_partial(
                     "generate_until", (context, gen_kwargs), generated_text