code: def from_pretrained(
      model_name="unsloth/llama-2-7b-bnb-4bit",
      max_seq_length=4096,
      dtype=None,
      load_in_4bit=True,
      token=None,
      device_map="sequential",
      rope_scaling=None,
      fix_tokenizer=True,
  ):
      SUPPORTS_BFLOAT16 = torch.cuda.is_bf16_supported()
      gpu_stats = torch.cuda.get_device_properties(0)
      max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
  
      statistics = (
          f"==((====))==  Unsloth: Fast Llama patching release {__version__}\n"
          f"   \\\   /|    GPU: {gpu_stats.name}. Max memory: {max_memory} GB\n"
          f"O^O/ \_/ \\    CUDA capability = {gpu_stats.major}.{gpu_stats.minor}. Xformers = {xformers_version}. FA = {HAS_FLASH_ATTENTION}.\n"
          f"\        /    Pytorch version: {torch.__version__}. CUDA Toolkit = {torch.version.cuda}\n"
          f' "-____-"     bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. Platform = {platform_system}\n'
      )
      logger.warning_once(statistics)
      FastLlamaModel.pre_patch()
  
      if dtype is None:
          dtype = (
              torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
          )
      elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
          logger.warning_once(
              "Device does not support bfloat16. Will change to float16."
          )
          dtype = torch.float16
  
      assert (
          dtype == torch.float16
          or dtype == torch.bfloat16
          or dtype == torch.float32
      )
  
      # RoPE scaling
      model_max_seq_length = AutoConfig.from_pretrained(
          model_name, token=token
      ).max_position_embeddings
  
      if (rope_scaling is None) and (
          max_seq_length > model_max_seq_length
      ):
          rope_scaling = max_seq_length / model_max_seq_length
          logger.warning_once(
              f"Unsloth: {model_name} can only handle sequence lengths of at most "
              f"{model_max_seq_length}.\nBut with kaiokendev's RoPE scaling of "
              f"{round(rope_scaling, 3)}, it can be magically be extended to "
              f"{max_seq_length}!"
          )
          rope_scaling = {
              "type": "linear",
              "factor": rope_scaling,
          }
      pass
  
      bnb_config = None
      if load_in_4bit:
          bnb_config = BitsAndBytesConfig(
              load_in_4bit=True,
              bnb_4bit_use_double_quant=True,
              bnb_4bit_quant_type="nf4",
              bnb_4bit_compute_dtype=dtype,
          )
  
      # https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/discussions/12
      # RoPE Scaling's max_position_embeddings must be updated
      max_position_embeddings = max(
          max_seq_length, model_max_seq_length
      )
      model = AutoModelForCausalLM.from_pretrained(
          model_name,
          device_map=device_map,
          torch_dtype=dtype,
          quantization_config=bnb_config,
          token=token,
          rope_scaling=rope_scaling,
          max_position_embeddings=max_position_embeddings,
      )
      tokenizer = AutoTokenizer.from_pretrained(
          model_name,
          model_max_length=max_seq_length,
          padding_side="right",
          token=token,
      )
  
      model, tokenizer = patch_tokenizer(model, tokenizer)
      model = FastLlamaModel.post_patch(model)
  
      # Patch up QKV / O and MLP
      for idx, layer in enumerate(model.model.layers):
          layer.self_attn.apply_qkv = original_apply_qkv
          layer.self_attn.apply_o = original_apply_o
      pass
  
      # Save max_seq_length
      model.max_seq_length = max_position_embeddings
      internal_model = model
      while hasattr(internal_model, "model"):
          internal_model.max_seq_length = max_position_embeddings
          internal_model = internal_model.model
      pass
      internal_model.max_seq_length = max_position_embeddings
  
      # We check the tokenizer first for errors
      if fix_tokenizer:
          tokenizer = check_tokenizer(
              model=model,
              tokenizer=tokenizer,
              model_name=model_name,
              model_max_length=max_seq_length,
              padding_side="right",
              token=token,
          )
      pass
  
      # Fix up config for transformers uploading PEFT
      name = model.config._name_or_path
      if name.startswith("unsloth/") and name.endswith("-bnb-4bit"):
          name = name[: len(name) - len("-bnb-4bit")]
          model.config.update({"_name_or_path": name})
      pass
      # Log Unsloth version for future fastpaths for inference
      model.config.update({"unsloth_version": __version__})
  
      return model, tokenizer
  
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 2 for calling the FastLlamaModel obj's underlying member method code with example usage: obj.from_pretrained(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer) and returns: model,  tokenizer