The following log details an agent's completion of a task which involved using a language model to generate an answer for a given question using a Python script. If the agent encountered an error, the user had the option to provide feedback to correct it. To improve the process, the correct operation steps needed to be extracted as executable shell or Python scripts to ensure that the agent could successfully complete the task without the need for human feedback in the future.

SYSTEM: Human feedback: the large language model is in local path, /NAME_1/NAME_2/llm_model/llama-65b-hf
NEXT ACTION: COMMAND = write_to_file ARGUMENTS = {'filename': '/NAME_1/Auto-GPT/autogpt/auto_gpt_workspace/question_answer.py', 'text': "import torch\nfrom transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n\nmodel_path = '/NAME_1/NAME_2/llm_model/llama-65b-hf'\ntokenizer = AutoTokenizer.from_pretrained(model_path)\nmodel = AutoModelForSeq2SeqLM.from_pretrained(model_path)\n\nquestion = 'who are you?'\n\ninputs = tokenizer.encode_plus(question, return_tensors='pt')\noutputs = model.generate(**inputs)\nanswer = tokenizer.decode(outputs[0], skip_special_tokens=True)\n\nwith open('answer.txt', 'w') as f:\n f.write(answer)\n"}
SYSTEM: Command write_to_file returned: File written to successfully.
NEXT ACTION: COMMAND = execute_python_file ARGUMENTS = {'filename': '/NAME_1/Auto-GPT/autogpt/auto_gpt_workspace/question_answer.py'}
SYSTEM: Human feedback: please use AutoModelForCausalLM instead of AutoModelForSeq2SeqLM, and you can use pipeline for "text-generation"
NEXT ACTION: COMMAND = write_to_file ARGUMENTS = {'filename': '/NAME_1/Auto-GPT/autogpt/auto_gpt_workspace/question_answer.py', 'text': "import torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n\nmodel_path = '/NAME_1/NAME_2/llm_model/llama-65b-hf'\ntokenizer = AutoTokenizer.from_pretrained(model_path)\nmodel = AutoModelForCausalLM.from_pretrained(model_path)\n\ntext_generator = pipeline('text-generation', model=model, tokenizer=tokenizer)\n\nquestion = 'who are you?'\n\nanswer = text_generator(question, max_length=50, do_sample=True, num_return_sequences=1)[0]['generated_text']\n\nwith open('answer.txt', 'w') as f:\n f.write(answer)\n"}
SYSTEM: Command write_to_file returned: File written to successfully.
NEXT ACTION: COMMAND = execute_python_file ARGUMENTS = {'filename': '/NAME_1/Auto-GPT/autogpt/auto_gpt_workspace/question_answer.py'}
SYSTEM: Command execute_python_file returned:
NEXT ACTION: COMMAND = read_file ARGUMENTS = {'filename': '/NAME_1/Auto-GPT/autogpt/auto_g