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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "995f4d22",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/zonca/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"from smolagents import LiteLLMModel\n",
"from dotenv import load_dotenv\n",
"import os\n",
"\n",
"load_dotenv()\n",
"\n",
"# Replace all calls to HfApiModel\n",
"llm_model = LiteLLMModel(\n",
" model_id=\"gemini/gemini-2.0-flash\", # you can see other model names here: https://cloud.google.com/vertex-ai/generative-ai/docs/learn/models. It is important to prefix the name with \"gemini/\"\n",
" api_key=os.getenv(\"GEMINI_API_KEY\"),\n",
" max_tokens=8192\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "38e0f80a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The capital of France is **Paris**.\n",
"\n"
]
}
],
"source": [
"response = llm_model([\n",
" {\"role\": \"user\", \"content\": \"What is the capital of France?\"}\n",
"])\n",
"print(response.content)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "939309dd",
"metadata": {},
"outputs": [
{
"data": {
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"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"font-weight: bold\">Call echo with 'hello'</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">╰─ HfApiModel - Qwen/Qwen2.5-Coder-32B-Instruct ──────────────────────────────────────────────────────────────────╯</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m╭─\u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[1;38;2;212;183;2mNew run\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╮\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mCall echo with 'hello'\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m╰─\u001b[0m\u001b[38;2;212;183;2m HfApiModel - Qwen/Qwen2.5-Coder-32B-Instruct \u001b[0m\u001b[38;2;212;183;2m─────────────────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d4b702; text-decoration-color: #d4b702\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ </span><span style=\"font-weight: bold\">Step </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #d4b702; text-decoration-color: #d4b702\"> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>\n",
"</pre>\n"
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"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Error in generating model output:</span>\n",
"<span style=\"color: #800080; text-decoration-color: #800080; font-weight: bold\">InferenceClient.chat_completion</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">() got an unexpected keyword argument </span><span style=\"color: #008000; text-decoration-color: #008000\">'repo_id'</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;31mError in generating model output:\u001b[0m\n",
"\u001b[1;35mInferenceClient.chat_completion\u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31m)\u001b[0m\u001b[1;31m got an unexpected keyword argument \u001b[0m\u001b[32m'repo_id'\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">[Step 1: Duration 0.01 seconds]</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[2m[Step 1: Duration 0.01 seconds]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "AgentGenerationError",
"evalue": "Error in generating model output:\nInferenceClient.chat_completion() got an unexpected keyword argument 'repo_id'",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mTypeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:1266\u001b[39m, in \u001b[36mCodeAgent.step\u001b[39m\u001b[34m(self, memory_step)\u001b[39m\n\u001b[32m 1265\u001b[39m additional_args = {\u001b[33m\"\u001b[39m\u001b[33mgrammar\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m.grammar} \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.grammar \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[32m-> \u001b[39m\u001b[32m1266\u001b[39m chat_message: ChatMessage = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1267\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minput_messages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1268\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m<end_code>\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mObservation:\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCalling tools:\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1269\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43madditional_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1270\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1271\u001b[39m memory_step.model_output_message = chat_message\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/models.py:1090\u001b[39m, in \u001b[36mInferenceClientModel.__call__\u001b[39m\u001b[34m(self, messages, stop_sequences, grammar, tools_to_call_from, **kwargs)\u001b[39m\n\u001b[32m 1081\u001b[39m completion_kwargs = \u001b[38;5;28mself\u001b[39m._prepare_completion_kwargs(\n\u001b[32m 1082\u001b[39m messages=messages,\n\u001b[32m 1083\u001b[39m stop_sequences=stop_sequences,\n\u001b[32m (...)\u001b[39m\u001b[32m 1088\u001b[39m **kwargs,\n\u001b[32m 1089\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1090\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat_completion\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mcompletion_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28mself\u001b[39m.last_input_token_count = response.usage.prompt_tokens\n",
"\u001b[31mTypeError\u001b[39m: InferenceClient.chat_completion() got an unexpected keyword argument 'repo_id'",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[31mAgentGenerationError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 28\u001b[39m\n\u001b[32m 25\u001b[39m agent = CodeAgent(tools=[echo], model=model)\n\u001b[32m 27\u001b[39m \u001b[38;5;66;03m# Run the agent with a prompt\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m28\u001b[39m response = \u001b[43magent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCall echo with \u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mhello\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 29\u001b[39m \u001b[38;5;28mprint\u001b[39m(response)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:341\u001b[39m, in \u001b[36mMultiStepAgent.run\u001b[39m\u001b[34m(self, task, stream, reset, images, additional_args, max_steps)\u001b[39m\n\u001b[32m 339\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._run(task=\u001b[38;5;28mself\u001b[39m.task, max_steps=max_steps, images=images)\n\u001b[32m 340\u001b[39m \u001b[38;5;66;03m# Outputs are returned only at the end. We only look at the last step.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m341\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdeque\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxlen\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[32m0\u001b[39m].final_answer\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:365\u001b[39m, in \u001b[36mMultiStepAgent._run\u001b[39m\u001b[34m(self, task, max_steps, images)\u001b[39m\n\u001b[32m 362\u001b[39m final_answer = \u001b[38;5;28mself\u001b[39m._execute_step(task, action_step)\n\u001b[32m 363\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentGenerationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 364\u001b[39m \u001b[38;5;66;03m# Agent generation errors are not caused by a Model error but an implementation error: so we should raise them and exit.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m365\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[32m 366\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 367\u001b[39m \u001b[38;5;66;03m# Other AgentError types are caused by the Model, so we should log them and iterate.\u001b[39;00m\n\u001b[32m 368\u001b[39m action_step.error = e\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:362\u001b[39m, in \u001b[36mMultiStepAgent._run\u001b[39m\u001b[34m(self, task, max_steps, images)\u001b[39m\n\u001b[32m 360\u001b[39m action_step = \u001b[38;5;28mself\u001b[39m._create_action_step(step_start_time, images)\n\u001b[32m 361\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m362\u001b[39m final_answer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_execute_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction_step\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 363\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentGenerationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 364\u001b[39m \u001b[38;5;66;03m# Agent generation errors are not caused by a Model error but an implementation error: so we should raise them and exit.\u001b[39;00m\n\u001b[32m 365\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:385\u001b[39m, in \u001b[36mMultiStepAgent._execute_step\u001b[39m\u001b[34m(self, task, memory_step)\u001b[39m\n\u001b[32m 383\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_execute_step\u001b[39m(\u001b[38;5;28mself\u001b[39m, task: \u001b[38;5;28mstr\u001b[39m, memory_step: ActionStep) -> Union[\u001b[38;5;28;01mNone\u001b[39;00m, Any]:\n\u001b[32m 384\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.log_rule(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.step_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, level=LogLevel.INFO)\n\u001b[32m--> \u001b[39m\u001b[32m385\u001b[39m final_answer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemory_step\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m final_answer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.final_answer_checks:\n\u001b[32m 387\u001b[39m \u001b[38;5;28mself\u001b[39m._validate_final_answer(final_answer)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:1282\u001b[39m, in \u001b[36mCodeAgent.step\u001b[39m\u001b[34m(self, memory_step)\u001b[39m\n\u001b[32m 1280\u001b[39m memory_step.model_output = model_output\n\u001b[32m 1281\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m-> \u001b[39m\u001b[32m1282\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m AgentGenerationError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError in generating model output:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m.logger) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 1284\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.log_markdown(\n\u001b[32m 1285\u001b[39m content=model_output,\n\u001b[32m 1286\u001b[39m title=\u001b[33m\"\u001b[39m\u001b[33mOutput message of the LLM:\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 1287\u001b[39m level=LogLevel.DEBUG,\n\u001b[32m 1288\u001b[39m )\n\u001b[32m 1290\u001b[39m \u001b[38;5;66;03m# Parse\u001b[39;00m\n",
"\u001b[31mAgentGenerationError\u001b[39m: Error in generating model output:\nInferenceClient.chat_completion() got an unexpected keyword argument 'repo_id'"
]
}
],
"source": [
"import os\n",
"from smolagents import CodeAgent, HfApiModel, tool\n",
"\n",
"# Set your Hugging Face API token\n",
"os.environ[\"HF_API_TOKEN\"] = \"<your-huggingface-token>\"\n",
"\n",
"# Define a simple tool\n",
"@tool\n",
"def echo(text: str) -> str:\n",
" \"\"\"\n",
" Echo the input text.\n",
"\n",
" Args:\n",
" text (str): The text to echo.\n",
"\n",
" Returns:\n",
" str: The same text that was input.\n",
" \"\"\"\n",
" return text\n",
"\n",
"# Initialize the model\n",
"model = HfApiModel(repo_id=\"HuggingFaceH4/zephyr-7b-beta\") # or another supported model\n",
"\n",
"# Create the agent with the tool\n",
"agent = CodeAgent(tools=[echo], model=model)\n",
"\n",
"# Run the agent with a prompt\n",
"response = agent.run(\"Call echo with 'hello'\")\n",
"print(response)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9f94c824",
"metadata": {},
"outputs": [
{
"data": {
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"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"font-weight: bold\">How many seconds would it take for a leopard at full speed to run through Pont des Arts?</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span> <span style=\"color: #d4b702; text-decoration-color: #d4b702\">│</span>\n",
"<span style=\"color: #d4b702; text-decoration-color: #d4b702\">╰─ InferenceClientModel - Qwen/Qwen2.5-Coder-32B-Instruct ────────────────────────────────────────────────────────╯</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m╭─\u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[1;38;2;212;183;2mNew run\u001b[0m\u001b[38;2;212;183;2m \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╮\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[1mHow many seconds would it take for a leopard at full speed to run through Pont des Arts?\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m│\u001b[0m \u001b[38;2;212;183;2m│\u001b[0m\n",
"\u001b[38;2;212;183;2m╰─\u001b[0m\u001b[38;2;212;183;2m InferenceClientModel - Qwen/Qwen2.5-Coder-32B-Instruct \u001b[0m\u001b[38;2;212;183;2m───────────────────────────────────────────────────────\u001b[0m\u001b[38;2;212;183;2m─╯\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #d4b702; text-decoration-color: #d4b702\">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ </span><span style=\"font-weight: bold\">Step </span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #d4b702; text-decoration-color: #d4b702\"> ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[38;2;212;183;2m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ \u001b[0m\u001b[1mStep \u001b[0m\u001b[1;36m1\u001b[0m\u001b[38;2;212;183;2m ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">Error in generating model output:</span>\n",
"<span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">404</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> Client Error: Not Found for url: </span>\n",
"<span style=\"color: #0000ff; text-decoration-color: #0000ff; text-decoration: underline\">https://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\"> (Request ID: </span>\n",
"<span style=\"color: #808000; text-decoration-color: #808000; font-weight: bold\">Root</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">=</span><span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">-681045d9-038c834f3e57cf572c3f9c02;</span><span style=\"color: #ffff00; text-decoration-color: #ffff00\">195ac9a0-e49e-48f8-a238-84c03d4c9606</span><span style=\"color: #800000; text-decoration-color: #800000; font-weight: bold\">)</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1;31mError in generating model output:\u001b[0m\n",
"\u001b[1;36m404\u001b[0m\u001b[1;31m Client Error: Not Found for url: \u001b[0m\n",
"\u001b[4;94mhttps://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions\u001b[0m\u001b[1;31m \u001b[0m\u001b[1;31m(\u001b[0m\u001b[1;31mRequest ID: \u001b[0m\n",
"\u001b[1;33mRoot\u001b[0m\u001b[1;31m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;31m-681045d9-038c834f3e57cf572c3f9c02;\u001b[0m\u001b[93m195ac9a0-e49e-48f8-a238-84c03d4c9606\u001b[0m\u001b[1;31m)\u001b[0m\n"
]
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"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"color: #7f7f7f; text-decoration-color: #7f7f7f\">[Step 1: Duration 0.57 seconds]</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[2m[Step 1: Duration 0.57 seconds]\u001b[0m\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "AgentGenerationError",
"evalue": "Error in generating model output:\n404 Client Error: Not Found for url: https://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions (Request ID: Root=1-681045d9-038c834f3e57cf572c3f9c02;195ac9a0-e49e-48f8-a238-84c03d4c9606)",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mHTTPError\u001b[39m Traceback (most recent call last)",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_http.py:409\u001b[39m, in \u001b[36mhf_raise_for_status\u001b[39m\u001b[34m(response, endpoint_name)\u001b[39m\n\u001b[32m 408\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m409\u001b[39m \u001b[43mresponse\u001b[49m\u001b[43m.\u001b[49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 410\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/requests/models.py:1024\u001b[39m, in \u001b[36mResponse.raise_for_status\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m 1023\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[32m-> \u001b[39m\u001b[32m1024\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response=\u001b[38;5;28mself\u001b[39m)\n",
"\u001b[31mHTTPError\u001b[39m: 404 Client Error: Not Found for url: https://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[31mHfHubHTTPError\u001b[39m Traceback (most recent call last)",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:1266\u001b[39m, in \u001b[36mCodeAgent.step\u001b[39m\u001b[34m(self, memory_step)\u001b[39m\n\u001b[32m 1265\u001b[39m additional_args = {\u001b[33m\"\u001b[39m\u001b[33mgrammar\u001b[39m\u001b[33m\"\u001b[39m: \u001b[38;5;28mself\u001b[39m.grammar} \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.grammar \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m {}\n\u001b[32m-> \u001b[39m\u001b[32m1266\u001b[39m chat_message: ChatMessage = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1267\u001b[39m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43minput_messages\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1268\u001b[39m \u001b[43m \u001b[49m\u001b[43mstop_sequences\u001b[49m\u001b[43m=\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43m<end_code>\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mObservation:\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mCalling tools:\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1269\u001b[39m \u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43madditional_args\u001b[49m\u001b[43m,\u001b[49m\n\u001b[32m 1270\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1271\u001b[39m memory_step.model_output_message = chat_message\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/models.py:1090\u001b[39m, in \u001b[36mInferenceClientModel.__call__\u001b[39m\u001b[34m(self, messages, stop_sequences, grammar, tools_to_call_from, **kwargs)\u001b[39m\n\u001b[32m 1081\u001b[39m completion_kwargs = \u001b[38;5;28mself\u001b[39m._prepare_completion_kwargs(\n\u001b[32m 1082\u001b[39m messages=messages,\n\u001b[32m 1083\u001b[39m stop_sequences=stop_sequences,\n\u001b[32m (...)\u001b[39m\u001b[32m 1088\u001b[39m **kwargs,\n\u001b[32m 1089\u001b[39m )\n\u001b[32m-> \u001b[39m\u001b[32m1090\u001b[39m response = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mclient\u001b[49m\u001b[43m.\u001b[49m\u001b[43mchat_completion\u001b[49m\u001b[43m(\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[43mcompletion_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1092\u001b[39m \u001b[38;5;28mself\u001b[39m.last_input_token_count = response.usage.prompt_tokens\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/huggingface_hub/inference/_client.py:992\u001b[39m, in \u001b[36mInferenceClient.chat_completion\u001b[39m\u001b[34m(self, messages, model, stream, frequency_penalty, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream_options, temperature, tool_choice, tool_prompt, tools, top_logprobs, top_p, extra_body)\u001b[39m\n\u001b[32m 985\u001b[39m request_parameters = provider_helper.prepare_request(\n\u001b[32m 986\u001b[39m inputs=messages,\n\u001b[32m 987\u001b[39m parameters=parameters,\n\u001b[32m (...)\u001b[39m\u001b[32m 990\u001b[39m api_key=\u001b[38;5;28mself\u001b[39m.token,\n\u001b[32m 991\u001b[39m )\n\u001b[32m--> \u001b[39m\u001b[32m992\u001b[39m data = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_inner_post\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrequest_parameters\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 994\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m stream:\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/huggingface_hub/inference/_client.py:357\u001b[39m, in \u001b[36mInferenceClient._inner_post\u001b[39m\u001b[34m(self, request_parameters, stream)\u001b[39m\n\u001b[32m 356\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m357\u001b[39m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 358\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m response.iter_lines() \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;28;01melse\u001b[39;00m response.content\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/huggingface_hub/utils/_http.py:482\u001b[39m, in \u001b[36mhf_raise_for_status\u001b[39m\u001b[34m(response, endpoint_name)\u001b[39m\n\u001b[32m 480\u001b[39m \u001b[38;5;66;03m# Convert `HTTPError` into a `HfHubHTTPError` to display request information\u001b[39;00m\n\u001b[32m 481\u001b[39m \u001b[38;5;66;03m# as well (request id and/or server error message)\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m482\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, \u001b[38;5;28mstr\u001b[39m(e), response) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n",
"\u001b[31mHfHubHTTPError\u001b[39m: 404 Client Error: Not Found for url: https://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions (Request ID: Root=1-681045d9-038c834f3e57cf572c3f9c02;195ac9a0-e49e-48f8-a238-84c03d4c9606)",
"\nThe above exception was the direct cause of the following exception:\n",
"\u001b[31mAgentGenerationError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[8]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m 3\u001b[39m model = InferenceClientModel()\n\u001b[32m 4\u001b[39m agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m \u001b[43magent\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mHow many seconds would it take for a leopard at full speed to run through Pont des Arts?\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:341\u001b[39m, in \u001b[36mMultiStepAgent.run\u001b[39m\u001b[34m(self, task, stream, reset, images, additional_args, max_steps)\u001b[39m\n\u001b[32m 339\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m._run(task=\u001b[38;5;28mself\u001b[39m.task, max_steps=max_steps, images=images)\n\u001b[32m 340\u001b[39m \u001b[38;5;66;03m# Outputs are returned only at the end. We only look at the last step.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m341\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mdeque\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_run\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[43mmax_steps\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimages\u001b[49m\u001b[43m=\u001b[49m\u001b[43mimages\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmaxlen\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[32m0\u001b[39m].final_answer\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:365\u001b[39m, in \u001b[36mMultiStepAgent._run\u001b[39m\u001b[34m(self, task, max_steps, images)\u001b[39m\n\u001b[32m 362\u001b[39m final_answer = \u001b[38;5;28mself\u001b[39m._execute_step(task, action_step)\n\u001b[32m 363\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentGenerationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 364\u001b[39m \u001b[38;5;66;03m# Agent generation errors are not caused by a Model error but an implementation error: so we should raise them and exit.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m365\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[32m 366\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 367\u001b[39m \u001b[38;5;66;03m# Other AgentError types are caused by the Model, so we should log them and iterate.\u001b[39;00m\n\u001b[32m 368\u001b[39m action_step.error = e\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:362\u001b[39m, in \u001b[36mMultiStepAgent._run\u001b[39m\u001b[34m(self, task, max_steps, images)\u001b[39m\n\u001b[32m 360\u001b[39m action_step = \u001b[38;5;28mself\u001b[39m._create_action_step(step_start_time, images)\n\u001b[32m 361\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m362\u001b[39m final_answer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_execute_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtask\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maction_step\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 363\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m AgentGenerationError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m 364\u001b[39m \u001b[38;5;66;03m# Agent generation errors are not caused by a Model error but an implementation error: so we should raise them and exit.\u001b[39;00m\n\u001b[32m 365\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m e\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:385\u001b[39m, in \u001b[36mMultiStepAgent._execute_step\u001b[39m\u001b[34m(self, task, memory_step)\u001b[39m\n\u001b[32m 383\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_execute_step\u001b[39m(\u001b[38;5;28mself\u001b[39m, task: \u001b[38;5;28mstr\u001b[39m, memory_step: ActionStep) -> Union[\u001b[38;5;28;01mNone\u001b[39;00m, Any]:\n\u001b[32m 384\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.log_rule(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStep \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.step_number\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, level=LogLevel.INFO)\n\u001b[32m--> \u001b[39m\u001b[32m385\u001b[39m final_answer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mstep\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmemory_step\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 386\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m final_answer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m.final_answer_checks:\n\u001b[32m 387\u001b[39m \u001b[38;5;28mself\u001b[39m._validate_final_answer(final_answer)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/p/software/Final_Assignment_Template/.venv/lib/python3.13/site-packages/smolagents/agents.py:1282\u001b[39m, in \u001b[36mCodeAgent.step\u001b[39m\u001b[34m(self, memory_step)\u001b[39m\n\u001b[32m 1280\u001b[39m memory_step.model_output = model_output\n\u001b[32m 1281\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[32m-> \u001b[39m\u001b[32m1282\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m AgentGenerationError(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mError in generating model output:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m.logger) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01me\u001b[39;00m\n\u001b[32m 1284\u001b[39m \u001b[38;5;28mself\u001b[39m.logger.log_markdown(\n\u001b[32m 1285\u001b[39m content=model_output,\n\u001b[32m 1286\u001b[39m title=\u001b[33m\"\u001b[39m\u001b[33mOutput message of the LLM:\u001b[39m\u001b[33m\"\u001b[39m,\n\u001b[32m 1287\u001b[39m level=LogLevel.DEBUG,\n\u001b[32m 1288\u001b[39m )\n\u001b[32m 1290\u001b[39m \u001b[38;5;66;03m# Parse\u001b[39;00m\n",
"\u001b[31mAgentGenerationError\u001b[39m: Error in generating model output:\n404 Client Error: Not Found for url: https://router.huggingface.co/hf-inference/models/Qwen/Qwen2.5-Coder-32B-Instruct/v1/chat/completions (Request ID: Root=1-681045d9-038c834f3e57cf572c3f9c02;195ac9a0-e49e-48f8-a238-84c03d4c9606)"
]
}
],
"source": [
"from smolagents import CodeAgent, DuckDuckGoSearchTool, InferenceClientModel\n",
"\n",
"model = InferenceClientModel()\n",
"agent = CodeAgent(tools=[DuckDuckGoSearchTool()], model=model)\n",
"\n",
"agent.run(\"How many seconds would it take for a leopard at full speed to run through Pont des Arts?\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9b9fc943",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": ".venv",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.1"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|