OSError When Trying to Load Model from Local Disk (Offline)

So I want to load the hugging face from my local folder and train my model with it.

However, I get this error:

OSError: Incorrect path_or_model_id: '/distilgpt2'. Please provide either the path to a local folder or the repo_id of a model on the Hub.
# https://www.linkedin.com/advice/1/how-do-you-use-hugging-face-natural-language-processing-q4gve


# Models
# microsoft/phi-1_5
# distilbert/distilbert-base-uncased
# google-t5/t5-base
# deberta-v3-base

import pandas as pd
import torch
import transformers as tm #import BertLMHeadModel, AutoModelForMaskedLM, AutoModelForSeq2SeqLM, AutoModelForCausalLM, AutoModel, AutoModelForSequenceClassification, GPT2LMHeadModel,PhiForCausalLM, GPT2Tokenizer, AutoTokenizer,  GPT2TokenizerFast
import os
import datasets as ds
from trl import SFTTrainer
import json

parent = os.path.dirname(os.getcwd())
fileName=parent+'\\amazon-kdd-cup-2024-starter-kit\data\development.json'
# fileName="./dev/data.json"
file=open(fileName)
# myData = ds.load_dataset("json", data_files=fileName, split="train")
myData=pd.read_json(fileName, lines=True)



testInput=myData["input_field"]
testMCQ=myData["is_multiple_choice"]


for i in range(len(myData)):
    tuple=list(zip(testInput, testMCQ))
    testData=pd.DataFrame(tuple, columns=['input_field', 'is_multiple_choice'])

 
testDataSet=ds.Dataset.from_pandas(testData)
print(testDataSet["input_field"])
print(testDataSet["is_multiple_choice"])

model_name="/distilgpt2"
model = tm.AutoModelForCausalLM.from_pretrained(model_name) # for phi-1_5
tokenizer = tm.AutoModelForCausalLM.from_pretrained(model_name, use_fast=False)

training_args = tm.TrainingArguments(
            output_dir="./",
            per_device_train_batch_size=16,
            learning_rate=2e-4,
            lr_scheduler_type="cosine",
            num_train_epochs=3,
            gradient_accumulation_steps=2, # simulate larger batch sizes
)


trainer = SFTTrainer(
    model,
    train_dataset=testDataSet,
    dataset_text_field="input_field",
    max_seq_length=3,
)



trainer.train()



How exactly should I specify the local folder name?

The answer to this depends on where the folder in question is.

The folder is called distilgpt2, and it’s stored in the same folder as the code above, and my app cannot detect it.

take the / off the front.

With Filepaths, / at the start means the root of the filesystem. . means “current directory” and .. means “go up one directory”

So if your working directory is at /my/drv/file/folder:
. refers to /my/drv/file/folder
./something refers to /my/drv/file/folder/something
/ refers to /
/something refers to /something
../something refers to /my/drv/file/something
something refers implicitly to /my/drv/file/folder/something