fix gemini model set up issue

aios/llm_core/adapter.py

@@ -65,9 +65,9 @@ def __init__(
                                   variables making this needless.
         """
         if isinstance(llm_name, list) != isinstance(llm_backend, list):
-            raise ValueError
+            raise ValueError("llm_name and llm_backend do not be the same type")
         elif isinstance(llm_backend, list) and len(llm_name) == len(llm_backend):
-            raise ValueError
+            raise ValueError("llm_name and llm_backend do not have the same length")
 
         self.llm_name            = llm_name if isinstance(llm_name, list) else [llm_name]
         self.max_gpu_memory      = max_gpu_memory
@@ -76,9 +76,7 @@ def __init__(
         self.log_mode            = log_mode
         self.llm_backend         = llm_backend if isinstance(llm_backend, list) else [llm_backend]
         self.context_manager     = SimpleContextManager() if use_context_manager else None
-
-        if strategy == RouterStrategy.SIMPLE:
-            self.strategy = SimpleStrategy(self.llm_name)
+
 
         # Set all supported API keys
         api_providers = {
@@ -111,6 +109,8 @@ def __init__(
                 else:
                     print(f"- Not found in config.yaml or environment variables")
 
+        # breakpoint()
+
         # Format model names to match backend or instantiate local backends
         for idx in range(len(self.llm_name)):
             if self.llm_backend[idx] is None:
@@ -140,18 +140,23 @@ def __init__(
                 case None:
                     continue
                 case _:
-                    prefix = self.llm_backend[idx] + "/"
-                    is_formatted = self.llm_name[idx].startswith(prefix)
-
-                    # Google backwards compatibility fix
                     if self.llm_backend[idx] == "google":
                         self.llm_backend[idx] = "gemini"
-                        if is_formatted:
-                            self.llm_name[idx] = "gemini/" + self.llm_name[idx].split("/")[1]
-                            continue
+
+                    # Google backwards compatibility fix
+
+                    prefix = self.llm_backend[idx] + "/"
+                    is_formatted = self.llm_name[idx].startswith(prefix)
+
+                    # if not is_formatted:
+                    # self.llm_name[idx] = "gemini/" + self.llm_name[idx].split("/")[1]
+                    # continue
 
                     if not is_formatted:
                         self.llm_name[idx] = prefix + self.llm_name[idx]
+
+        if strategy == RouterStrategy.SIMPLE:
+            self.strategy = SimpleStrategy(self.llm_name)
 
     def tool_calling_input_format(self, messages: list, tools: list) -> list:
         """Integrate tool information into the messages for open-sourced LLMs
@@ -265,12 +270,13 @@ def address_syscall(
         llm_syscall.set_start_time(time.time())
 
         restored_context = None
+
         if self.context_manager:
             pid = llm_syscall.get_pid()
             if self.context_manager.check_restoration(pid):
                 restored_context = self.context_manager.gen_recover(pid)
 
-        if restored_context is not None:
+        if restored_context:
             messages += [{
                 "role": "assistant",
                 "content": "" + restored_context,

aios/llm_core/local.py

@@ -3,6 +3,8 @@
 
 import os
 
+from aios.config.config_manager import config
+
 class HfLocalBackend:
     def __init__(self, model_name, device="auto", max_gpu_memory=None, hostname=None):
         print("\n=== HfLocalBackend Initialization ===")
@@ -35,12 +37,12 @@ def __init__(self, model_name, device="auto", max_gpu_memory=None, hostname=None
         self.tokenizer.chat_template = "{% for message in messages %}{% if message['role'] == 'user' %}{{ ' ' }}{% endif %}{{ message['content'] }}{% if not loop.last %}{{ ' ' }}{% endif %}{% endfor %}{{ eos_token }}"
 
     def inference_online(self, messages, temperature, stream=False):
-        return str(completion(
+        return completion(
             model="huggingface/" + self.model_name,
             messages=messages,
             temperature=temperature,
             api_base=self.hostname,
-        ))
+        ).choices[0].message.content
 
     def __call__(
         self,
@@ -83,7 +85,8 @@ def __init__(self, model_name, device="auto", max_gpu_memory=None, hostname=None
         self.model_name = model_name
         self.device = device
         self.max_gpu_memory = max_gpu_memory
-        self.hostname = hostname
+        # self.hostname = hostname
+        self.hostname = "http://localhost:8001"
 
         # If a hostname is given, then this vLLM instance is hosted as a web server.
         # Therefore, do not start the AIOS-based vLLM instance.
@@ -98,19 +101,22 @@ def __init__(self, model_name, device="auto", max_gpu_memory=None, hostname=None
                 tensor_parallel_size=1 if max_gpu_memory is None else len(max_gpu_memory)
             )
             self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+            self.sampling_params = vllm.SamplingParams(temperature=temperature)
+
         except ImportError:
             raise ImportError("Could not import vllm Python package"
                               "Please install it with `pip install python`")
         except Exception as err:
             print("Error loading vllm model:", err)
 
     def inference_online(self, messages, temperature, stream=False):
-        return str(completion(
+        breakpoint()
+        return completion(
             model="hosted_vllm/" + self.model_name,
             messages=messages,
             temperature=temperature,
             api_base=self.hostname,
-        ))
+        ).choices[0].message.content
 
     def __call__(
         self,
@@ -121,14 +127,16 @@ def __call__(
         if self.hostname is not None:
             return self.inference_online(messages, temperature, stream=stream)
 
-        assert vllm
+        assert self.model
+        assert self.sampling_params
+        # breakpoint()
         if stream:
             raise NotImplemented
 
-        parameters = vllm.SamplingParams(temperature=temperature)
+        # parameters = vllm.SamplingParams(temperature=temperature)
         prompt     = self.tokenizer.apply_chat_template(messages,
                                                         tokenize=False)
-        response   = self.model.generate(prompt, parameters)
+        response   = self.model.generate(prompt, self.sampling_params)
         result     = response[0].outputs[0].text
 
         return result

scripts/run_agent.sh

@@ -1,13 +1,34 @@
+# run agent with gemini-1.5-flash
 run-agent \
-    --llm_name llama3:8b \
-    --llm_backend ollama \
+    --llm_name gemini-1.5-flash \
+    --llm_backend google \
     --agent_name_or_path demo_author/demo_agent \
     --task "Tell me what is core idea of AIOS" \
     --aios_kernel_url http://localhost:8000
 
+# run agent with gpt-4o-mini using openai
 run-agent \
     --llm_name gpt-4o-mini \
     --llm_backend openai \
     --agent_name_or_path demo_author/demo_agent \
     --task "Tell me what is core idea of AIOS" \
+    --aios_kernel_url http://localhost:8000
+
+# run agent with gpt-4o-mini using openai
+vllm serve meta-llama/Meta-Llama-3-8B-Instruct --dtype auto --port 8001 # start the vllm server
+run-agent \
+    --llm_name meta-llama/Meta-Llama-3-8B-Instruct \
+    --llm_backend vllm \
+    --agent_name_or_path demo_author/demo_agent \
+    --task "Tell me what is core idea of AIOS" \
+    --aios_kernel_url http://localhost:8000
+
+# run agent with llama3:8b using ollama
+ollama pull llama3:8b # pull the model
+ollama serve # start the ollama server
+run-agent \
+    --llm_name llama3:8b \
+    --llm_backend ollama \
+    --agent_name_or_path demo_author/demo_agent \
+    --task "Tell me what is core idea of AIOS" \
     --aios_kernel_url http://localhost:8000