code: def concat_weights(models, device=Device.DEFAULT):
      def convert(name) -> Tensor:
          disk_tensors: List[Tensor] = [model[name] for model in models]
          if len(disk_tensors) == 1 or len(disk_tensors[0].shape) == 1:
              return disk_tensors[0].to(device=device)
          axis = (
              1
              if name.startswith("tok_embeddings.")
              or name.endswith(".attention.wo.weight")
              or name.endswith(".feed_forward.w2.weight")
              else 0
          )
          lazy_tensors = [
              data.to(device=device) for data in disk_tensors
          ]
          return lazy_tensors[0].cat(*lazy_tensors[1:], dim=axis)
  
      return {
          name: convert(name)
          for name in {name: None for model in models for name in model}
      }
  
file_path: tinygrad/examples/llama.py
imports: from pathlib import Path
  from typing import List
  import sys, argparse, json
  import numpy
  from tinygrad.helpers import Timing, Profiling, getenv, DEBUG, colored
  from tinygrad import Device, GlobalCounters, dtypes, nn
  from tinygrad.tensor import Tensor
  from tinygrad.nn.state import safe_load, torch_load, load_state_dict, get_parameters
  from extra.models.llama import Transformer, convert_from_huggingface
  from sentencepiece import SentencePieceProcessor
label_desc: Use image node_id 1 for calling a global function with example usage: concat_weights(models, device) and returns: unknown, lazy_tensors, disk_tensors