Merge branch 'main' into add-watsonx-inference-adapter

llama_stack/models/llama/datatypes.py

@@ -195,11 +195,23 @@ register_schema(SamplingStrategy, name="SamplingStrategy")
 
 @json_schema_type
 class SamplingParams(BaseModel):
+    """Sampling parameters.
+
+    :param strategy: The sampling strategy.
+    :param max_tokens: The maximum number of tokens that can be generated in the completion. The token count of
+        your prompt plus max_tokens cannot exceed the model's context length.
+    :param repetition_penalty: Number between -2.0 and 2.0. Positive values penalize new tokens
+        based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
+    :param stop: Up to 4 sequences where the API will stop generating further tokens.
+        The returned text will not contain the stop sequence.
+    """
+
     strategy: SamplingStrategy = Field(default_factory=GreedySamplingStrategy)
 
     max_tokens: Optional[int] = 0
     repetition_penalty: Optional[float] = 1.0
     additional_params: Optional[dict] = {}
+    stop: Optional[List[str]] = None
 
 
 class CheckpointQuantizationFormat(Enum):

llama_stack/providers/utils/inference/openai_compat.py

@@ -147,6 +147,9 @@ def get_sampling_options(params: SamplingParams) -> dict:
         if params.repetition_penalty is not None and params.repetition_penalty != 1.0:
             options["repeat_penalty"] = params.repetition_penalty
 
+        if params.stop is not None:
+            options["stop"] = params.stop
+
     return options