code: def get_llm_gpt4all(
      model_name=None,
      model=None,
      max_new_tokens=256,
      temperature=0.1,
      repetition_penalty=1.0,
      top_k=40,
      top_p=0.7,
      streaming=False,
      callbacks=None,
      prompter=None,
      context="",
      iinput="",
      n_jobs=None,
      n_gpus=None,
      main_gpu=0,
      verbose=False,
      inner_class=False,
      max_seq_len=None,
      llamacpp_path=None,
      llamacpp_dict=None,
  ):
      redo = False
      if not inner_class:
          assert prompter is not None
  
      default_kwargs = get_gpt4all_default_kwargs(
          max_new_tokens=max_new_tokens,
          temperature=temperature,
          repetition_penalty=repetition_penalty,
          top_k=top_k,
          top_p=top_p,
          n_jobs=n_jobs,
          verbose=verbose,
          max_seq_len=max_seq_len,
          main_gpu=main_gpu,
      )
      if model_name == "llama":
          # FIXME: streaming not thread safe due to:
          # llama_cpp/utils.py:        sys.stdout = self.outnull_file
          # llama_cpp/utils.py:        sys.stdout = self.old_stdout
          cls = H2OLlamaCpp
          if model is None:
              llamacpp_dict = llamacpp_dict.copy()
              model_path = llamacpp_dict.pop("model_path_llama")
              llamacpp_path = (
                  os.getenv("LLAMACPP_PATH", llamacpp_path) or "./"
              )
              if os.path.isfile(os.path.basename(model_path)):
                  # e.g. if offline but previously downloaded
                  model_path = os.path.basename(model_path)
              elif os.path.isfile(
                  os.path.join(
                      llamacpp_path, os.path.basename(model_path)
                  )
              ):
                  # e.g. so don't have to point to full previously-downloaded path
                  model_path = os.path.join(
                      llamacpp_path, os.path.basename(model_path)
                  )
              elif url_alive(model_path):
                  # online
                  dest = (
                      os.path.join(
                          llamacpp_path, os.path.basename(model_path)
                      )
                      if llamacpp_path
                      else None
                  )
                  if dest.endswith("?download=true"):
                      dest = dest.replace("?download=true", "")
                  model_path = download_simple(model_path, dest=dest)
          else:
              model_path = model
          model_kwargs = get_model_kwargs(
              llamacpp_dict,
              default_kwargs,
              cls,
              exclude_list=["lc_kwargs"],
          )
          model_kwargs.update(
              dict(
                  model_path=model_path,
                  callbacks=callbacks,
                  streaming=streaming,
                  prompter=prompter,
                  context=context,
                  iinput=iinput,
                  n_gpus=n_gpus,
              )
          )
  
          # migration to  new langchain fix:
          odd_keys = ["model_kwargs", "grammar_path", "grammar"]
          for key in odd_keys:
              model_kwargs.pop(key, None)
  
          llm = cls(**model_kwargs)
          llm.client.verbose = verbose
          inner_model = llm.client
  
          if max_seq_len is None:
              redo = True
              max_seq_len = llm.client.n_embd()
              print(
                  "Auto-detected LLaMa n_ctx=%s, will unload then reload with this setting."
                  % max_seq_len
              )
  
          # with multiple GPUs, something goes wrong unless generation occurs early before other imports
          # CUDA error 704 at /tmp/pip-install-khkugdmy/llama-cpp-python_8c0a9782b7604a5aaf95ec79856eac97/vendor/llama.cpp/ggml-cuda.cu:6408: peer access is already enabled
          inner_model("Say exactly one word", max_tokens=1)
          inner_tokenizer = FakeTokenizer(
              tokenizer=llm.client,
              is_llama_cpp=True,
              model_max_length=max_seq_len,
          )
      elif model_name == "gpt4all_llama":
          # FIXME: streaming not thread safe due to:
          # gpt4all/pyllmodel.py:        sys.stdout = stream_processor
          # gpt4all/pyllmodel.py:        sys.stdout = old_stdout
  
          cls = H2OGPT4All
          if model is None:
              llamacpp_dict = llamacpp_dict.copy()
              model_path = llamacpp_dict.pop("model_name_gpt4all_llama")
              if url_alive(model_path):
                  # online
                  llamacpp_path = (
                      os.getenv("LLAMACPP_PATH", llamacpp_path) or "./"
                  )
                  dest = (
                      os.path.join(
                          llamacpp_path, os.path.basename(model_path)
                      )
                      if llamacpp_path
                      else None
                  )
                  model_path = download_simple(model_path, dest=dest)
          else:
              model_path = model
          model_kwargs = get_model_kwargs(
              llamacpp_dict,
              default_kwargs,
              cls,
              exclude_list=["lc_kwargs"],
          )
          model_kwargs.update(
              dict(
                  model=model_path,
                  backend="llama",
                  callbacks=callbacks,
                  streaming=streaming,
                  prompter=prompter,
                  context=context,
                  iinput=iinput,
              )
          )
          llm = cls(**model_kwargs)
          inner_model = llm.client
          inner_tokenizer = FakeTokenizer(model_max_length=max_seq_len)
      elif model_name == "gptj":
          # FIXME: streaming not thread safe due to:
          # gpt4all/pyllmodel.py:        sys.stdout = stream_processor
          # gpt4all/pyllmodel.py:        sys.stdout = old_stdout
  
          cls = H2OGPT4All
          if model is None:
              llamacpp_dict = llamacpp_dict.copy()
              model_path = (
                  llamacpp_dict.pop("model_name_gptj")
                  if model is None
                  else model
              )
              if url_alive(model_path):
                  llamacpp_path = (
                      os.getenv("LLAMACPP_PATH", llamacpp_path) or "./"
                  )
                  dest = (
                      os.path.join(
                          llamacpp_path, os.path.basename(model_path)
                      )
                      if llamacpp_path
                      else None
                  )
                  model_path = download_simple(model_path, dest=dest)
          else:
              model_path = model
          model_kwargs = get_model_kwargs(
              llamacpp_dict,
              default_kwargs,
              cls,
              exclude_list=["lc_kwargs"],
          )
          model_kwargs.update(
              dict(
                  model=model_path,
                  backend="gptj",
                  callbacks=callbacks,
                  streaming=streaming,
                  prompter=prompter,
                  context=context,
                  iinput=iinput,
              )
          )
          llm = cls(**model_kwargs)
          inner_model = llm.client
          inner_tokenizer = FakeTokenizer(model_max_length=max_seq_len)
      else:
          raise RuntimeError("No such model_name %s" % model_name)
      if inner_class:
          return inner_model, inner_tokenizer, redo, max_seq_len
      else:
          return llm
  
imports: import inspect
  import os
  from typing import Dict, Any, Optional, List, Iterator
  from langchain.callbacks.manager import CallbackManagerForLLMRun
  from langchain.schema.output import GenerationChunk
  from langchain.llms import gpt4all
  from pydantic.v1 import root_validator
  from utils import FakeTokenizer, url_alive, download_simple, clear_torch_cache, n_gpus_global
  from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
  from langchain.llms import LlamaCpp
label_desc: Use image node_id 4 for calling a global function with example usage: get_llm_gpt4all(model_name, model, max_new_tokens, temperature, repetition_penalty, top_k, top_p, streaming, callbacks, prompter, context, iinput, n_jobs, n_gpus, main_gpu, verbose, inner_class, max_seq_len, llamacpp_path, llamacpp_dict) and returns: inner_model,  inner_tokenizer,  redo,  max_seq_len, llm