text: There is a rigid response structure.# Return the results as a dictionaryresult = {'output': result}An example inference function would be:def inference(model_inputs:dict) -> dict:    global model    global tokenizer    # Parse out your arguments    prompt = model_inputs.get('prompt', None)    if prompt == None:        return {'message': "No prompt provided"}    # Run the model    input_ids = tokenizer.encode(prompt, return_tensors='pt').cuda()    output = model.generate(        input_ids,        max_length=100,        do_sample=True,        top_k=50,        top_p=0.95,        num_return_sequences=1,        temperature=0.9,        early_stopping=True,        no_repeat_ngram_size=3,        num_beams=5,        length_penalty=1.5,        repetition_penalty=1.5,        bad_words_ids=[[tokenizer.encode(' ', add_prefix_space=True)[0]]]        )    result = tokenizer.decode(output[0], skip_special_tokens=True)    # Return the results as a dictionary    result = {'output': result}    return resultYou can find a full example of a Banana app here.Wrappersâ€‹LLMâ€‹There exists an Banana LLM wrapper, which you can access withfrom langchain.llms import BananaYou need to provide a model key located in the dashboard:llm = Banana(model_key="YOUR_MODEL_KEY")PreviousAzure OpenAINextBasetenInstallation and SetupDefine your Banana TemplateBuild the Banana appWrappersLLMCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.