( tools: Union llm_engine: Callable = <transformers.agents.llm_engine.HfEngine object at 0x7f149c7d3f10> system_prompt = 'You will be given a task to solve as best you can. You have access to the following tools:\n<<tool_descriptions>>\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (name of the tool to use) and a `action_input` key (input to the tool).\n\nThe $ACTION_JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. It should be formatted in json. Do not try to escape special characters. Here is the template of a valid $ACTION_JSON_BLOB:\nAction:\n{\n "action": $TOOL_NAME,\n "action_input": $INPUT\n}\n\nMake sure to have the $INPUT as a dictionnary in the right format for the tool you are using, and do not put variable names as input if you can find the right values.\n\nYou will be given:\n\nTask: the task you are given.\n\nYou should ALWAYS use the following format:\n\nThought: you should always think about one action to take. Then use the action as follows:\nAction:\n$ACTION_JSON_BLOB\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times, you should take several steps when needed. The $ACTION_JSON_BLOB must only use a SINGLE action at a time.)\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like "image_1.jpg".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: "image_1.jpg"\n\nThought: I need to transform the image that I received in the previous observation to make it green.\nAction:\n{\n "action": "image_transformer",\n "action_input": {"image": "image_1.jpg"}\n}\n\nTo provide the final answer to the task, use an action blob with "action": "final_answer" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n "action": "final_answer",\n "action_input": {"answer": "insert your final answer here"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nAction:\n{\n "action": "document_qa",\n "action_input": {"document": "document.pdf", "question": "Who is the oldest person mentioned?"}\n}\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\n\nThought: I will now generate an image showcasing the oldest person.\nAction:\n{\n "action": "image_generator",\n "action_input": {"text": ""A portrait of John Doe, a 55-year-old man living in Canada.""}\n}\nObservation: "image.png"\n\nThought: I will now return the generated image.\nAction:\n{\n "action": "final_answer",\n "action_input": "image.png"\n}\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code evaluator to compute the result of the operation and then return the final answer using the `final_answer` tool\nAction:\n{\n "action": "python_interpreter",\n "action_input": {"code": "5 + 3 + 1294.678"}\n}\nObservation: 1302.678\n\nThought: Now that I know the result, I will now return it.\nAction:\n{\n "action": "final_answer",\n "action_input": "1302.678"\n}\n\n---\nTask: "Which city has the highest population , Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nAction:\n{\n "action": "search",\n "action_input": "Population Guangzhou"\n}\nObservation: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\n\n\nThought: Now let\'s get the population of Shanghai using the tool \'search\'.\nAction:\n{\n "action": "search",\n "action_input": "Population Shanghai"\n}\nObservation: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has a larger population. Let\'s return the result.\nAction:\n{\n "action": "final_answer",\n "action_input": "Shanghai"\n}\n\n\nAbove example were using notional tools that might not exist for you. You only have acces to those tools:\n<<tool_names>>\nALWAYS provide a \'Thought:\' and an \'Action:\' sequence. You MUST provide at least the \'Action:\' sequence to move forward.\n\nNow begin!\n' tool_description_template = None additional_args = {} max_iterations: int = 6 tool_parser = <function parse_json_tool_call at 0x7f149c7695a0> add_base_tools: bool = False verbose: int = 0 memory_verbose: bool = False )

( tools: List llm_engine: Callable = <transformers.agents.llm_engine.HfEngine object at 0x7f149c784190> system_prompt: str = 'You will be given a task to solve, your job is to come up with a series of simple commands in Python that will perform the task.\nTo help you, I will give you access to a set of tools that you can use. Each tool is a Python function and has a description explaining the task it performs, the inputs it expects and the outputs it returns.\nYou should first explain which tool you will use to perform the task and for what reason, then write the code in Python.\nEach instruction in Python should be a simple assignment. You can print intermediate results if it makes sense to do so.\nBe sure to provide a \'Code:\' token, else the system will be stuck in a loop.\n\nTools:\n<<tool_descriptions>>\n\nExamples:\n---\nTask: "Answer the question in the variable `question` about the image stored in the variable `image`. The question is in French."\n\nI will use the following tools: `translator` to translate the question into English and then `image_qa` to answer the question on the input image.\nCode:\n```py\ntranslated_question = translator(question=question, src_lang="French", tgt_lang="English")\nprint(f"The translated question is {translated_question}.")\nanswer = image_qa(image=image, question=translated_question)\nprint(f"The answer is {answer}")\n```<end_code>\n\n---\nTask: "Identify the oldest person in the `document` and create an image showcasing the result."\n\nI will use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document, question="What is the oldest person?")\nprint(f"The answer is {answer}.")\nimage = image_generator(answer)\n```<end_code>\n\n---\nTask: "Generate an image using the text given in the variable `caption`."\n\nI will use the following tool: `image_generator` to generate an image.\nCode:\n```py\nimage = image_generator(prompt=caption)\n```<end_code>\n\n---\nTask: "Summarize the text given in the variable `text` and read it out loud."\n\nI will use the following tools: `summarizer` to create a summary of the input text, then `text_reader` to read it out loud.\nCode:\n```py\nsummarized_text = summarizer(text)\nprint(f"Summary: {summarized_text}")\naudio_summary = text_reader(summarized_text)\n```<end_code>\n\n---\nTask: "Answer the question in the variable `question` about the text in the variable `text`. Use the answer to generate an image."\n\nI will use the following tools: `text_qa` to create the answer, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = text_qa(text=text, question=question)\nprint(f"The answer is {answer}.")\nimage = image_generator(answer)\n```<end_code>\n\n---\nTask: "Caption the following `image`."\n\nI will use the following tool: `image_captioner` to generate a caption for the image.\nCode:\n```py\ncaption = image_captioner(image)\n```<end_code>\n\n---\nAbove example were using tools that might not exist for you. You only have acces to those Tools:\n<<tool_names>>\n\nRemember to make sure that variables you use are all defined.\nBe sure to provide a \'Code:\n```\' sequence before the code and \'```<end_code>\' after, else you will get an error.\nDO NOT pass the arguments as a dict as in \'answer = ask_search_agent({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = ask_search_agent(query="What is the place where James Bond lives?")\'.\n\nNow Begin!\n' tool_description_template: str = '\n- {{ tool.name }}: {{ tool.description }}\n Takes inputs: {{tool.inputs}}\n' **kwargs )

( tools: List llm_engine: Callable = <transformers.agents.llm_engine.HfEngine object at 0x7f149c784340> system_prompt: str = 'You will be given a task to solve as best you can.\nYou have access to the following tools:\n<<tool_descriptions>>\n\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of \'Thought:\', \'Code:\', and \'Observation:\' sequences.\n\nAt each step, in the \'Thought:\' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use.\nThen in the \'Code:\' sequence, you shold write the code in simple Python. The code sequence must end with \'/End code\' sequence.\nDuring each intermediate step, you can use \'print()\' to save whatever important information you will then need.\nThese print outputs will then be available in the \'Observation:\' field, for using this information as input for the next step.\n\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document=document, question="Who is the oldest person mentioned?")\nprint(answer)\n```<end_code>\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\nThought: I will now generate an image showcasing the oldest person.\n\nCode:\n```py\nimage = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")\nfinal_answer(image)\n```<end_code>\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\n\nCode:\n```py\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n```<end_code>\n\n---\nTask: "Which city has the highest population , Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nCode:\n```py\npopulation_guangzhou = search("Guangzhou population")\nprint("Population Guangzhou:", population_guangzhou)\npopulation_shanghai = search("Shanghai population")\nprint("Population Shanghai:", population_shanghai)\n```<end_code>\nObservation:\nPopulation Guangzhou: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\nPopulation Shanghai: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has the highest population.\nCode:\n```py\nfinal_answer("Shanghai")\n```<end_code>\n\n---\nTask: "What is the current age of the pope, raised to the power 0.36?"\n\nThought: I will use the tool `search` to get the age of the pope, then raise it to the power 0.36.\nCode:\n```py\npope_age = search(query="current pope age")\nprint("Pope age:", pope_age)\n```<end_code>\nObservation:\nPope age: "The pope Francis is currently 85 years old."\n\nThought: I know that the pope is 85 years old. Let\'s compute the result using python code.\nCode:\n```py\npope_current_age = 85 ** 0.36\nfinal_answer(pope_current_age)\n```<end_code>\n\n\nAbove example were using notional tools that might not exist for you. You only have acces to those tools:\n<<tool_names>>\nYou also can perform computations in the python code you generate.\n\nAlways provide a \'Thought:\' and a \'Code:\n```py\' sequence ending with \'```<end_code>\' sequence. You MUST provide at least the \'Code:\' sequence to move forward.\n\nRemember to not perform too many operations in a single code block! You should split the task into intermediate code blocks.\nPrint results at the end of each step to save the intermediate results. Then use final_answer() to return the final result.\n\nRemember to make sure that variables you use are all defined.\nDO NOT pass the arguments as a dict as in \'answer = ask_search_agent({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = ask_search_agent(query="What is the place where James Bond lives?")\'.\n\nNow Begin!\n' tool_description_template: str = '\n- {{ tool.name }}: {{ tool.description }}\n Takes inputs: {{tool.inputs}}\n' **kwargs )

( tools: List llm_engine: Callable = <transformers.agents.llm_engine.HfEngine object at 0x7f149c7844f0> system_prompt: str = 'You will be given a task to solve as best you can. You have access to the following tools:\n<<tool_descriptions>>\n\nThe way you use the tools is by specifying a json blob.\nSpecifically, this json should have a `action` key (name of the tool to use) and a `action_input` key (input to the tool).\n\nThe $ACTION_JSON_BLOB should only contain a SINGLE action, do NOT return a list of multiple actions. It should be formatted in json. Do not try to escape special characters. Here is the template of a valid $ACTION_JSON_BLOB:\nAction:\n{\n "action": $TOOL_NAME,\n "action_input": $INPUT\n}\n\nMake sure to have the $INPUT as a dictionnary in the right format for the tool you are using, and do not put variable names as input if you can find the right values.\n\nYou will be given:\n\nTask: the task you are given.\n\nYou should ALWAYS use the following format:\n\nThought: you should always think about one action to take. Then use the action as follows:\nAction:\n$ACTION_JSON_BLOB\nObservation: the result of the action\n... (this Thought/Action/Observation can repeat N times, you should take several steps when needed. The $ACTION_JSON_BLOB must only use a SINGLE action at a time.)\n\nYou can use the result of the previous action as input for the next action.\nThe observation will always be a string: it can represent a file, like "image_1.jpg".\nThen you can use it as input for the next action. You can do it for instance as follows:\n\nObservation: "image_1.jpg"\n\nThought: I need to transform the image that I received in the previous observation to make it green.\nAction:\n{\n "action": "image_transformer",\n "action_input": {"image": "image_1.jpg"}\n}\n\nTo provide the final answer to the task, use an action blob with "action": "final_answer" tool. It is the only way to complete the task, else you will be stuck on a loop. So your final output should look like this:\nAction:\n{\n "action": "final_answer",\n "action_input": {"answer": "insert your final answer here"}\n}\n\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nAction:\n{\n "action": "document_qa",\n "action_input": {"document": "document.pdf", "question": "Who is the oldest person mentioned?"}\n}\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\n\nThought: I will now generate an image showcasing the oldest person.\nAction:\n{\n "action": "image_generator",\n "action_input": {"text": ""A portrait of John Doe, a 55-year-old man living in Canada.""}\n}\nObservation: "image.png"\n\nThought: I will now return the generated image.\nAction:\n{\n "action": "final_answer",\n "action_input": "image.png"\n}\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code evaluator to compute the result of the operation and then return the final answer using the `final_answer` tool\nAction:\n{\n "action": "python_interpreter",\n "action_input": {"code": "5 + 3 + 1294.678"}\n}\nObservation: 1302.678\n\nThought: Now that I know the result, I will now return it.\nAction:\n{\n "action": "final_answer",\n "action_input": "1302.678"\n}\n\n---\nTask: "Which city has the highest population , Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nAction:\n{\n "action": "search",\n "action_input": "Population Guangzhou"\n}\nObservation: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\n\n\nThought: Now let\'s get the population of Shanghai using the tool \'search\'.\nAction:\n{\n "action": "search",\n "action_input": "Population Shanghai"\n}\nObservation: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has a larger population. Let\'s return the result.\nAction:\n{\n "action": "final_answer",\n "action_input": "Shanghai"\n}\n\n\nAbove example were using notional tools that might not exist for you. You only have acces to those tools:\n<<tool_names>>\nALWAYS provide a \'Thought:\' and an \'Action:\' sequence. You MUST provide at least the \'Action:\' sequence to move forward.\n\nNow begin!\n' tool_description_template: str = '\n- {{ tool.name }}: {{ tool.description }}\n Takes inputs: {{tool.inputs}}\n' **kwargs )

( tools: List llm_engine: Callable = <transformers.agents.llm_engine.HfEngine object at 0x7f149c7846a0> system_prompt: str = 'You will be given a task to solve as best you can.\nYou have access to the following tools:\n<<tool_descriptions>>\n\nTo solve the task, you must plan forward to proceed in a series of steps, in a cycle of \'Thought:\', \'Code:\', and \'Observation:\' sequences.\n\nAt each step, in the \'Thought:\' sequence, you should first explain your reasoning towards solving the task, then the tools that you want to use.\nThen in the \'Code:\' sequence, you shold write the code in simple Python. The code sequence must end with \'/End code\' sequence.\nDuring each intermediate step, you can use \'print()\' to save whatever important information you will then need.\nThese print outputs will then be available in the \'Observation:\' field, for using this information as input for the next step.\n\nIn the end you have to return a final answer using the `final_answer` tool.\n\nHere are a few examples using notional tools:\n---\nTask: "Generate an image of the oldest person in this document."\n\nThought: I will proceed step by step and use the following tools: `document_qa` to find the oldest person in the document, then `image_generator` to generate an image according to the answer.\nCode:\n```py\nanswer = document_qa(document=document, question="Who is the oldest person mentioned?")\nprint(answer)\n```<end_code>\nObservation: "The oldest person in the document is John Doe, a 55 year old lumberjack living in Newfoundland."\n\nThought: I will now generate an image showcasing the oldest person.\n\nCode:\n```py\nimage = image_generator("A portrait of John Doe, a 55-year-old man living in Canada.")\nfinal_answer(image)\n```<end_code>\n\n---\nTask: "What is the result of the following operation: 5 + 3 + 1294.678?"\n\nThought: I will use python code to compute the result of the operation and then return the final answer using the `final_answer` tool\n\nCode:\n```py\nresult = 5 + 3 + 1294.678\nfinal_answer(result)\n```<end_code>\n\n---\nTask: "Which city has the highest population , Guangzhou or Shanghai?"\n\nThought: I need to get the populations for both cities and compare them: I will use the tool `search` to get the population of both cities.\nCode:\n```py\npopulation_guangzhou = search("Guangzhou population")\nprint("Population Guangzhou:", population_guangzhou)\npopulation_shanghai = search("Shanghai population")\nprint("Population Shanghai:", population_shanghai)\n```<end_code>\nObservation:\nPopulation Guangzhou: [\'Guangzhou has a population of 15 million inhabitants as of 2021.\']\nPopulation Shanghai: \'26 million (2019)\'\n\nThought: Now I know that Shanghai has the highest population.\nCode:\n```py\nfinal_answer("Shanghai")\n```<end_code>\n\n---\nTask: "What is the current age of the pope, raised to the power 0.36?"\n\nThought: I will use the tool `search` to get the age of the pope, then raise it to the power 0.36.\nCode:\n```py\npope_age = search(query="current pope age")\nprint("Pope age:", pope_age)\n```<end_code>\nObservation:\nPope age: "The pope Francis is currently 85 years old."\n\nThought: I know that the pope is 85 years old. Let\'s compute the result using python code.\nCode:\n```py\npope_current_age = 85 ** 0.36\nfinal_answer(pope_current_age)\n```<end_code>\n\n\nAbove example were using notional tools that might not exist for you. You only have acces to those tools:\n<<tool_names>>\nYou also can perform computations in the python code you generate.\n\nAlways provide a \'Thought:\' and a \'Code:\n```py\' sequence ending with \'```<end_code>\' sequence. You MUST provide at least the \'Code:\' sequence to move forward.\n\nRemember to not perform too many operations in a single code block! You should split the task into intermediate code blocks.\nPrint results at the end of each step to save the intermediate results. Then use final_answer() to return the final result.\n\nRemember to make sure that variables you use are all defined.\nDO NOT pass the arguments as a dict as in \'answer = ask_search_agent({\'query\': "What is the place where James Bond lives?"})\', but use the arguments directly as in \'answer = ask_search_agent(query="What is the place where James Bond lives?")\'.\n\nNow Begin!\n' tool_description_template: str = '\n- {{ tool.name }}: {{ tool.description }}\n Takes inputs: {{tool.inputs}}\n' **kwargs )

( task_or_repo_id model_repo_id = None token = None **kwargs )

( *args **kwargs )

( tools: List add_base_tools: bool = False )

( model = None pre_processor = None post_processor = None device = None device_map = None model_kwargs = None token = None **hub_kwargs )

( tool_class: Tool )

( collection_slug: str token: Optional = None )

( model: str = 'meta-llama/Meta-Llama-3-8B-Instruct' )

( value )

( value )

( value samplerate = 16000 )