transformers: keyerror : mistral (for transformer version = 4.30) and Import Error Using `load_in_8bit=True` requires Accelerate: for transformer version > 4.30

System Info

transformer version : 4.30,4.31,4.34,4.35 python version : 3.11.1, 3.11.5,3.8

Who can help?

No response

Information

• The official example scripts
• My own modified scripts

Tasks

• An officially supported task in the examples folder (such as GLUE/SQuAD, …)
• My own task or dataset (give details below)

Reproduction

1. #loading packges
from torch import cuda, bfloat16
import transformers
from transformers import StoppingCriteria, StoppingCriteriaList
import torch
from langchain.document_loaders import UnstructuredFileLoader
from langchain.chains.summarize import load_summarize_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM
from langchain import PromptTemplate
from langchain.text_splitter import RecursiveCharacterTextSplitter
import accelerate
base_model_id = "mistralai/Mistral-7B-Instruct-v0.1"
baseline = AutoModelForCausalLM.from_pretrained(base_model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
print("loaded all packages")
device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
print("Printing Device...")
print(device)
print("loading model....")
model_id = "mistralai/Mistral-7B-Instruct-v0.1"
model_config = transformers.AutoConfig.from_pretrained(
    model_id,
    use_auth_token=hf_auth#,
#    load_in_8bit=False    
)
bnb_config = transformers.BitsAndBytesConfig(load_in_4bit = True,
bnb_4bit_quant_tyoe = 'nf4',
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=bfloat16
)

model = transformers.AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    config=model_config,
    quantization_config=bnb_config,
#    load_in_8bit=False,
    device_map='auto',
    use_auth_token=hf_auth,
    offload_folder="save_folder"
)
# Load model directly
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id, use_auth_token=hf_auth)
generate_text = transformers.pipeline(
    model=model, tokenizer=tokenizer,
    return_full_text=True, 
    task='text-generation',
    temperature=0.1,  
    max_new_tokens=4096,  
    repetition_penalty=1.1 
)
print("loaded model")
llm = HuggingFacePipeline(pipeline=generate_text)

Run this

Expected behavior

we should get inference