text: It is broken into two parts: installation and setup, and then references to specific Beam wrappers.Installation and Setupâ€‹Create an accountInstall the Beam CLI with curl https://raw.githubusercontent.com/slai-labs/get-beam/main/get-beam.sh -sSfL | shRegister API keys with beam configureSet environment variables (BEAM_CLIENT_ID) and (BEAM_CLIENT_SECRET)Install the Beam SDK pip install beam-sdkWrappersâ€‹LLMâ€‹There exists a Beam LLM wrapper, which you can access withfrom langchain.llms.beam import BeamDefine your Beam app.â€‹This is the environment youâ€™ll be developing against once you start the app.
  It's also used to define the maximum response length from the model.llm = Beam(model_name="gpt2",           name="langchain-gpt2-test",           cpu=8,           memory="32Gi",           gpu="A10G",           python_version="python3.8",           python_packages=[               "diffusers[torch]>=0.10",               "transformers",               "torch",               "pillow",               "accelerate",               "safetensors",               "xformers",],           max_length="50",           verbose=False)Deploy your Beam appâ€‹Once defined, you can deploy your Beam app by calling your model's _deploy() method.llm._deploy()Call your Beam appâ€‹Once a beam model is deployed, it can be called by callying your model's _call() method.