feat: add reasoning_effort to chat completions API

llama_cpp/llama.py

@@ -1973,6 +1973,9 @@ def create_chat_completion(
         logit_bias: Optional[Dict[int, float]] = None,
         logprobs: Optional[bool] = None,
         top_logprobs: Optional[int] = None,
+        reasoning_effort: Optional[
+            Literal["none", "minimal", "low", "medium", "high", "xhigh"]
+        ] = None,
     ) -> Union[
         CreateChatCompletionResponse, Iterator[CreateChatCompletionStreamResponse]
     ]:
@@ -2005,6 +2008,8 @@ def create_chat_completion(
             logits_processor: A list of logits processors to use.
             grammar: A grammar to use.
             logit_bias: A logit bias to use.
+            reasoning_effort: Optional reasoning hint forwarded to chat handlers as a
+                chat-template keyword argument.
 
         Returns:
             Generated chat completion or a stream of chat completion chunks.
@@ -2044,6 +2049,7 @@ def create_chat_completion(
             logits_processor=logits_processor,
             grammar=grammar,
             logit_bias=logit_bias,
+            reasoning_effort=reasoning_effort,
         )
 
     def create_chat_completion_openai_v1(

llama_cpp/llama_chat_format.py

@@ -243,6 +243,7 @@ def raise_exception(message: str):
             tools=tools,
             tool_choice=tool_choice,
             strftime_now=self.strftime_now,
+            **kwargs,
         )
 
         stopping_criteria = None
@@ -617,6 +618,7 @@ def chat_completion_handler(
             function_call=function_call,
             tools=tools,
             tool_choice=tool_choice,
+            **kwargs,
         )
         prompt = llama.tokenize(
             result.prompt.encode("utf-8"),
@@ -734,7 +736,9 @@ def format_autotokenizer(
         **kwargs: Any,
     ) -> ChatFormatterResponse:
         tokenizer.use_default_system_prompt = False  # type: ignore
-        prompt: str = tokenizer.apply_chat_template(messages, tokenize=False)  # type: ignore
+        prompt: str = tokenizer.apply_chat_template(  # type: ignore
+            messages, tokenize=False, **kwargs
+        )
         assert isinstance(prompt, str)
         # Return formatted prompt and eos token by default
         return ChatFormatterResponse(
@@ -791,6 +795,7 @@ def format_tokenizer_config(
             messages=messages,
             bos_token=bos_token,
             eos_token=eos_token,
+            **kwargs,
         )
         return ChatFormatterResponse(
             prompt=prompt, stop=[eos_token, bos_token], added_special=True

llama_cpp/server/types.py

@@ -235,6 +235,12 @@ class CreateChatCompletionRequest(BaseModel):
     response_format: Optional[llama_cpp.ChatCompletionRequestResponseFormat] = Field(
         default=None,
     )
+    reasoning_effort: Optional[
+        Literal["none", "minimal", "low", "medium", "high", "xhigh"]
+    ] = Field(
+        default=None,
+        description="Optional reasoning-effort hint exposed to chat templates as the `reasoning_effort` keyword argument.",
+    )
 
     # ignored or currently unsupported
     model: Optional[str] = model_field

tests/test_llama_chat_format.py

@@ -1,12 +1,13 @@
 import json
+import inspect
 
 import jinja2
 
-from llama_cpp import (
-    ChatCompletionRequestUserMessage,
-)
+import llama_cpp
+from llama_cpp import ChatCompletionRequestUserMessage
 import llama_cpp.llama_types as llama_types
 import llama_cpp.llama_chat_format as llama_chat_format
+import llama_cpp.server.types as server_types
 
 from llama_cpp.llama_chat_format import hf_tokenizer_config_to_chat_formatter
 
@@ -92,3 +93,108 @@ def test_hf_tokenizer_config_str_to_chat_formatter():
     )
 
     assert chat_formatter_respoonse.prompt == ("<s>[INST] Hello, world! [/INST]</s>")
+
+
+def test_jinja2_chat_formatter_passes_template_kwargs():
+    chat_formatter = llama_chat_format.Jinja2ChatFormatter(
+        template="{{ reasoning_effort | default('unset') }} {{ messages[0]['content'] }}",
+        bos_token="<s>",
+        eos_token="</s>",
+    )
+    response = chat_formatter(
+        messages=[
+            ChatCompletionRequestUserMessage(role="user", content="Hello, world!"),
+        ],
+        reasoning_effort="low",
+    )
+
+    assert response.prompt == "low Hello, world!"
+
+
+def test_hf_tokenizer_config_chat_formatter_passes_template_kwargs():
+    tokenizer_config = {
+        "chat_template": "{{ bos_token }}{{ reasoning_effort | default('unset') }} {{ messages[0]['content'] }}",
+        "bos_token": "<s>",
+        "eos_token": "</s>",
+    }
+    chat_formatter = hf_tokenizer_config_to_chat_formatter(
+        tokenizer_config, add_generation_prompt=False
+    )
+    response = chat_formatter(
+        messages=[
+            ChatCompletionRequestUserMessage(role="user", content="Hello, world!"),
+        ],
+        reasoning_effort="medium",
+    )
+
+    assert response.prompt == "<s>medium Hello, world!"
+
+
+def test_chat_completion_handler_passes_template_kwargs():
+    captured = {}
+
+    def chat_formatter(*, messages, **kwargs):
+        captured["messages"] = messages
+        captured["kwargs"] = kwargs
+        return llama_chat_format.ChatFormatterResponse(prompt="Hello")
+
+    handler = llama_chat_format.chat_formatter_to_chat_completion_handler(
+        chat_formatter
+    )
+
+    class DummyLlama:
+        verbose = False
+
+        def tokenize(self, data, add_bos, special):
+            return [1]
+
+        def create_completion(self, **kwargs):
+            return {
+                "id": "cmpl-test",
+                "object": "text_completion",
+                "created": 0,
+                "model": "dummy",
+                "choices": [
+                    {
+                        "text": "world",
+                        "index": 0,
+                        "logprobs": None,
+                        "finish_reason": "stop",
+                    }
+                ],
+                "usage": {
+                    "prompt_tokens": 1,
+                    "completion_tokens": 1,
+                    "total_tokens": 2,
+                },
+            }
+
+    response = handler(
+        llama=DummyLlama(),
+        messages=[
+            ChatCompletionRequestUserMessage(role="user", content="Hello, world!"),
+        ],
+        reasoning_effort="high",
+    )
+
+    assert response["choices"][0]["message"]["content"] == "world"
+    assert captured["kwargs"]["reasoning_effort"] == "high"
+
+
+def test_create_chat_completion_exposes_reasoning_effort_parameter():
+    parameter = inspect.signature(llama_cpp.Llama.create_chat_completion).parameters[
+        "reasoning_effort"
+    ]
+
+    assert parameter.default is None
+
+
+def test_server_chat_completion_request_accepts_reasoning_effort():
+    request = server_types.CreateChatCompletionRequest(
+        messages=[
+            ChatCompletionRequestUserMessage(role="user", content="Hello, world!")
+        ],
+        reasoning_effort="minimal",
+    )
+
+    assert request.reasoning_effort == "minimal"