code: def restore_falcon_weight(
      ckpt: dict, n_head: int, n_head_kv: int, head_dim: int
  ):
      # see https://github.com/ggerganov/llama.cpp/blob/
      # master/convert-hf-to-gguf.py#L666
      import numpy as np
  
      for name, weight in ckpt.items():
          if name.endswith("attn_qkv.weight"):
              part1, part2, part3 = np.split(
                  weight.reshape(-1, head_dim * n_head),
                  [n_head * head_dim, (n_head + n_head_kv) * head_dim],
                  axis=0,
              )
              part1 = part1.reshape(
                  (
                      n_head_kv,
                      n_head // n_head_kv,
                      head_dim,
                      head_dim * n_head,
                  )
              )
              part2 = part2.reshape(
                  (n_head_kv, 1, head_dim, head_dim * n_head)
              )
              part3 = part3.reshape(
                  (n_head_kv, 1, head_dim, head_dim * n_head)
              )
              data = torch.cat([part1, part2, part3], dim=1)
              ckpt[name] = data.reshape(-1, head_dim * n_head)
      return ckpt
  
imports: import os
  import torch
  from accelerate import init_empty_weights
  from accelerate.utils import set_module_tensor_to_device
  from transformers import FalconConfig, FalconForCausalLM, PreTrainedTokenizerFast
  from ..gguf import GGUFFileLoader
label_desc: Use image node_id 2 for calling a global function with example usage: restore_falcon_weight(ckpt, n_head, n_head_kv, head_dim) and returns: ckpt