code: def post_patch(model):
      # Patch model
      layers = model.model.layers
  
      # Torch.compile fails on embedding matrix??
      # Workaround randomnly fixes it for torch versions < 2.2
      model.model.embed_tokens = torch.nn.Embedding.from_pretrained(
          model.model.embed_tokens.weight
      )
      model.config.update({"unsloth_version": __version__})
  
      # We also do this for the lm_head
      lm_head = torch.nn.Linear(1, 1, bias=None)
      del lm_head.weight
      lm_head.weight = model.lm_head.weight
      lm_head.in_features = lm_head.weight.shape[1]
      lm_head.out_features = lm_head.weight.shape[0]
      model.lm_head = lm_head
  
      # Also patch all dtypes - BnB seems to not allocate the correct type?
      # BnB default dtype seems to be float16!
      correct_dtype = lm_head.weight.dtype
  
      for name, module in model.named_modules():
          if isinstance(
              module, (bnb.nn.Linear4bit, peft.tuners.lora.Linear4bit)
          ):
              weight = module.weight
              quant_state = weight.quant_state
  
              if type(quant_state) is list:
                  # BnB seems to have float16 as default!
                  module.weight.quant_state[
                      2
                  ] = correct_dtype  # Cast to correct dtype
              else:
                  # https://github.com/TimDettmers/bitsandbytes/pull/763/files
                  quant_state.dtype = correct_dtype
              pass
          pass
      pass
  
      # Clear deleted GPU items
      gc.collect()
      torch.cuda.empty_cache()
      return model
  
imports: import torch
  from typing import Optional, Tuple, List, Union
  from torch.nn.functional import scaled_dot_product_attention
  from transformers.modeling_attn_mask_utils import _prepare_4d_causal_attention_mask
  from transformers.models.llama.modeling_llama import logger, BaseModelOutputWithPast, CausalLMOutputWithPast
  from ..kernels import *
  from ._utils import *
  from ._utils import __version__
  from transformers.models.llama.modeling_llama import LlamaAttention, LlamaDecoderLayer, LlamaModel, LlamaForCausalLM
  from peft import PeftModelForCausalLM
  import gc
  import peft
  import bitsandbytes
  import numpy
  import types
  from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, AutoConfig
  from transformers import set_seed
  from peft import LoraConfig, TaskType, get_peft_model
label_desc: Use image node_id 3 for calling the FastLlamaModel obj's underlying member method code with example usage: obj.post_patch(model) and returns: model