update

examples/run_async_agent_vllm.py

@@ -14,7 +14,7 @@
 asyncio.set_event_loop(loop)
 # initialize the model
 model = AsyncVllmModel(
-    path='internlm/internlm2_5-7b-chat',
+    path='Qwen/Qwen2-7B-Instruct',
     meta_template=INTERNLM2_META,
     tp=1,
     top_k=1,

lagent/agents/stream.py

@@ -314,35 +314,3 @@ async def forward(self, message: AgentMessage, session_id=0, **kwargs):
                 iter(self.interpreter_executor.actions.values()))
             if interpreter.name == 'AsyncIPythonInterpreter':
                 await interpreter.close_session(session_id)
-
-
-if __name__ == '__main__':
-    from lagent.llms import GPTAPI
-
-    interpreter_prompt = (
-        'Below is a math problem. Please solve it step by step with the assistance of Python programming. Consider using Sympy or Numpy library '
-        'to facilitate your derivation, calculation and equation solving. Utilize the "pi" symbol and "Rational" from Sympy '
-        'for $$\pi$$ and fractions, and simplify all fractions and square roots without converting them to decimal values. '
-        'Please encapsulate each generated Jupyter Python code block with tags "{begin}" and "{end}". Conclude the '
-        r'final answer when observations are sufficient and encapsulate the numerical result with LaTeX syntax "\boxed{{}}" '
-        'without any unit, and end your conclusion with the special token "[END]" to denote the completion of your response. '
-        'Keep the following points in mind:\n'
-        '- You must alternately use human and programming languages in the chain of thought;\n'
-        '- The number of your reasoning steps should not exceed **three**, which means you may merge some intermediate steps when the original answer is tedious.'
-    )
-
-    llm = dict(
-        type=GPTAPI,
-        model_type='gpt-4o-2024-05-13',
-        retry=50,
-        key=None,
-        max_new_tokens=2048,
-        stop_words=['</python'],
-    )
-    agent = MathCoder(
-        llm=llm,
-        output_format=InterpreterParser(
-            template=interpreter_prompt, begin='<python>', end='</python>'),
-        aggregator=InternLMToolAggregator(
-            environment_begin='<output>\n', environment_end='\n</output>'),
-        finish_condition=lambda m: '[END]' in m.content)

requirements/runtime.txt

@@ -12,9 +12,9 @@ jsonschema
 jupyter==1.0.0
 jupyter_core==5.7.2
 jupyter_client==8.6.2
+pydantic==2.6.4
 requests
 stdlib_list==0.10.0
-streamlit
 termcolor
 tiktoken
 timeout-decorator