Add system prompt and vary temp and new tokens

prompting/tasks/inference.py

@@ -30,21 +30,36 @@ class InferenceRewardConfig(BaseRewardConfig):
 
 Ask a question about the text and nothing else:"""
 
+SYSTEM_PROMPTS = [
+    "",
+    "You are a helpful AI assistant. Provide concise, accurate answers to any questions asked.",
+    "You are a friendly and patient assistant. Communicate your responses in a clear, easy-to-understand way, ensuring the user feels supported.",
+    "You are a creative helper. Offer engaging, imaginative responses that keep the user interested, while maintaining accuracy and clarity.",
+]
+
 
 class InferenceTask(BaseTextTask):
     name: ClassVar[str] = "inference"
     # TODO: Once we want to enable the 'actual' inference task with exact models
     query: str | None = None
     reference: str | None = None
+    system_prompt: str | None = None
     llm_model: ModelConfig | None = None
     llm_model_id: ModelConfig | None = random.choice(ModelZoo.models_configs).llm_model_id
     seed: int = Field(default_factory=lambda: random.randint(0, 1_000_000), allow_mutation=False)
-    sampling_params: dict[str, float] = shared_settings.SAMPLING_PARAMS
+    sampling_params: dict[str, float] = shared_settings.SAMPLING_PARAMS.copy()
 
     @model_validator(mode="after")
     def random_llm_model_id(self):
         if self.query:  # If we are already defining query, as in the case of organics, we also specify model.
             return self
+        # Choose system prompt and randomize inference settings
+        self.system_prompt = random.choice(SYSTEM_PROMPTS)
+        self.messages = []
+        if self.system_prompt:
+            self.messages.append({"role": "system", "content": self.system_prompt})
+        self.sampling_params["temperature"] = random.randint(0, 10) / 10
+        self.sampling_params["max_new_tokens"] = random.choice([256, 512, 1024, 2048])
 
         if np.random.rand() < 0.2:
             self.llm_model_id = None
@@ -55,8 +70,9 @@ def random_llm_model_id(self):
     def make_query(self, dataset_entry: ChatEntry) -> str:
         if self.query:
             return self.query
-        self.query = dataset_entry.messages
-        self.messages = dataset_entry.messages
+        self.messages.extend(dataset_entry.messages)
+        self.query = self.messages
+
         return self.query
 
     def make_reference(self, dataset_entry: ChatEntry) -> str:

pyproject.toml

@@ -31,7 +31,7 @@ max-line-length = 120
 extend-ignore = "D203,E203,E251,E266,E302,E305,E401,E402,E501,F401,F403,W503"
 exclude = ".git,__pycache__,dist,.venv,venv,*/lib/python*/site-packages,*/lib64/python*/site-packages"
 # TODO: Decrease to at least 10 (prompting/weight_setting/weight_setter.py).
-max-complexity = 13
+max-complexity = 14
 
 [tool.isort]
 atomic = true