Merge 85c450c8c4 into 6af45d7b69

docs/swarms/examples/vllm.md

@@ -47,13 +47,15 @@ Here's a complete example of setting up the stock analysis swarm:
 from swarms import Agent, ConcurrentWorkflow
 from swarms.utils.vllm_wrapper import VLLMWrapper
 
-# Initialize the VLLM wrapper
+# Initialize the VLLM wrapper (model loads when used)
 vllm = VLLMWrapper(
     model_name="meta-llama/Llama-2-7b-chat-hf",
     system_prompt="You are a helpful assistant.",
 )
 ```
 
+The model is initialized when `run()` or `batched_run()` is first called.
+
 !!! note "Model Selection"
     The example uses Llama-2-7b-chat, but you can use any VLLM-compatible model. Make sure you have the necessary permissions and resources to run your chosen model.
 

docs/swarms/examples/vllm_integration.md

@@ -28,7 +28,7 @@ Here's a simple example of how to use vLLM with Swarms:
 ```python title="basic_usage.py"
 from swarms.utils.vllm_wrapper import VLLMWrapper
 
-# Initialize the vLLM wrapper
+# Initialize the vLLM wrapper (model loads on first use)
 vllm = VLLMWrapper(
     model_name="meta-llama/Llama-2-7b-chat-hf",
     system_prompt="You are a helpful assistant.",
@@ -41,6 +41,8 @@ response = vllm.run("What is the capital of France?")
 print(response)
 ```
 
+The first call to `run()` lazily loads the model weights.
+
 ## VLLMWrapper Class
 
 !!! abstract "Class Overview"

examples/demos/swarms_of_vllm.py

@@ -4,7 +4,7 @@ from dotenv import load_dotenv
 
 load_dotenv()
 
-# Initialize the VLLM wrapper
+# Initialize the VLLM wrapper (model loads lazily on first run)
 vllm = VLLMWrapper(
     model_name="meta-llama/Llama-2-7b-chat-hf",
     system_prompt="You are a helpful assistant.",

examples/models/vllm_example.py

@@ -2,8 +2,8 @@ from swarms.utils.vllm_wrapper import VLLMWrapper
 
 
 def main():
-    # Initialize the vLLM wrapper with a model
+    # Initialize the vLLM wrapper.
-    # Note: You'll need to have the model downloaded or specify a HuggingFace model ID
+    # The actual model weights load lazily on the first call to `run()`.
     llm = VLLMWrapper(
         model_name="meta-llama/Llama-2-7b-chat-hf",  # Replace with your model path or HF model ID
         temperature=0.7,
@@ -17,7 +17,7 @@ def main():
     response = llm.run(task)
     print("Response:", response)
 
-    # Example with system prompt
+    # Example with system prompt. Model initialization is still lazy.
     llm_with_system = VLLMWrapper(
         model_name="meta-llama/Llama-2-7b-chat-hf",  # Replace with your model path or HF model ID
         system_prompt="You are a helpful AI assistant that provides concise answers.",

swarms/utils/vllm_wrapper.py

@@ -61,12 +61,22 @@ class VLLMWrapper:
         self.tool_choice = tool_choice
         self.parallel_tool_calls = parallel_tool_calls
 
-        # Initialize vLLM
+        # store kwargs for later lazy initialization
-        self.llm = LLM(model=model_name, **kwargs)
+        self._llm_kwargs = kwargs
-        self.sampling_params = SamplingParams(
+
-            temperature=temperature,
+        # Initialize attributes for lazy loading
-            max_tokens=max_tokens,
+        self.llm = None
-        )
+        self.sampling_params = None
+
+    def _ensure_initialized(self):
+        """Lazily initialize the underlying vLLM objects if needed."""
+        if self.llm is None:
+            self.llm = LLM(model=self.model_name, **self._llm_kwargs)
+        if self.sampling_params is None:
+            self.sampling_params = SamplingParams(
+                temperature=self.temperature,
+                max_tokens=self.max_tokens,
+            )
 
     def _prepare_prompt(self, task: str) -> str:
         """
@@ -95,6 +105,7 @@ class VLLMWrapper:
             str: The model's response.
         """
         try:
+            self._ensure_initialized()
             prompt = self._prepare_prompt(task)
 
             outputs = self.llm.generate(prompt, self.sampling_params)
@@ -133,6 +144,7 @@ class VLLMWrapper:
         Returns:
             List[str]: List of model responses.
         """
+        self._ensure_initialized()
         # Calculate the worker count based on 95% of available CPU cores
         num_workers = max(1, int((os.cpu_count() or 1) * 0.95))
         with concurrent.futures.ThreadPoolExecutor(