ComponentsLLMsPredibaseOn this pagePredibasePredibase allows you to train, finetune, and deploy any ML model—from linear regression to large language model. This example demonstrates using Langchain with models deployed on PredibaseSetupTo run this notebook, you'll need a Predibase account and an API key.You'll also need to install the Predibase Python package:pip install predibaseimport osos.environ["PREDIBASE_API_TOKEN"] = "{PREDIBASE_API_TOKEN}"Initial Call​from langchain.llms import Predibasemodel = Predibase(    model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))response = model("Can you recommend me a nice dry wine?")print(response)Chain Call Setup​llm = Predibase(    model="vicuna-13b", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))SequentialChain​from langchain.chains import LLMChainfrom langchain.prompts import PromptTemplate# This is an LLMChain to write a synopsis given a title of a play.template = """You are a playwright. Given the title of play, it is your job to write a synopsis for that title.Title: {title}Playwright: This is a synopsis for the above play:"""prompt_template = PromptTemplate(input_variables=["title"], template=template)synopsis_chain = LLMChain(llm=llm, prompt=prompt_template)# This is an LLMChain to write a review of a play given a synopsis.template = """You are a play critic from the New York Times. Given the synopsis of play, it is your job to write a review for that play.Play Synopsis:{synopsis}Review from a New York Times play critic of the above play:"""prompt_template = PromptTemplate(input_variables=["synopsis"], template=template)review_chain = LLMChain(llm=llm, prompt=prompt_template)# This is the overall chain where we run these two chains in sequence.from langchain.chains import SimpleSequentialChainoverall_chain = SimpleSequentialChain(    chains=[synopsis_chain, review_chain], verbose=True)review = overall_chain.run("Tragedy at sunset on the beach")Fine-tuned LLM (Use your own fine-tuned LLM from Predibase)​from langchain.llms import Predibasemodel = Predibase(    model="my-finetuned-LLM", predibase_api_key=os.environ.get("PREDIBASE_API_TOKEN"))# replace my-finetuned-LLM with the name of your model in Predibase# response = model("Can you help categorize the following emails into positive, negative, and neutral?")PreviousPipelineAINextPrediction GuardInitial CallChain Call SetupSequentialChainFine-tuned LLM (Use your own fine-tuned LLM from Predibase)