text: Assistant:{  "action": "Final Answer",  "action_input": "The capital of Pennsylvania is Harrisburg."}Human: "What 2 + 5?"AI Assistant:{  "action": "Final Answer",  "action_input": "2 + 5 = 7."}Human: 'What's the capital of Maryland?'AI Assistant:"""from transformers import pipelinefrom langchain.llms import HuggingFacePipelinehf_model = pipeline(    "text-generation", model="cerebras/Cerebras-GPT-590M", max_new_tokens=200)original_model = HuggingFacePipeline(pipeline=hf_model)generated = original_model.generate([prompt], stop=["Human:"])print(generated)    Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.    generations=[[Generation(text=' "What\'s the capital of Maryland?"\n', generation_info=None)]] llm_output=NoneThat's not so impressive, is it? It didn't answer the question and it didn't follow the JSON format at all! Let's try with the structured decoder.RELLM LLM Wrapperâ€‹Let's try that again, now providing a regex to match the JSON structured format.import regex  # Note this is the regex library NOT python's re stdlib module# We'll choose a regex that matches to a structured json string that looks like:# {#  "action": "Final Answer",# "action_input": string or dict# }pattern = regex.compile(    r'\{\s*"action":\s*"Final Answer",\s*"action_input":\s*(\{.*\}|"[^"]*")\s*\}\nHuman:')from langchain.experimental.llms import RELLMmodel = RELLM(pipeline=hf_model, regex=pattern, max_new_tokens=200)generated = model.predict(prompt,