text: hours of serverless GPU compute to test different models.os.environ["PIPELINE_API_KEY"] = "YOUR_API_KEY_HERE"Create the PipelineAI instanceâ€‹When instantiating PipelineAI, you need to specify the id or tag of the pipeline you want to use, e.g. pipeline_key = "public/gpt-j:base". You then have the option of passing additional pipeline-specific keyword arguments:llm = PipelineAI(pipeline_key="YOUR_PIPELINE_KEY", pipeline_kwargs={...})Create a Prompt Templateâ€‹We will create a prompt template for Question and Answer.template = """Question: {question}Answer: Let's think step by step."""prompt = PromptTemplate(template=template, input_variables=["question"])Initiate the LLMChainâ€‹llm_chain = LLMChain(prompt=prompt, llm=llm)Run the LLMChainâ€‹Provide a question and run the LLMChain.question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"llm_chain.run(question)PreviousPetalsNextPredibaseInstall pipeline-aiImportsSet the Environment API KeyCreate the PipelineAI instanceCreate a Prompt TemplateInitiate the LLMChainRun the LLMChainCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.