In [1]:

from importlib.metadata import version

pkgs = ["matplotlib",  # Plotting library
        "numpy",       # PyTorch & TensorFlow dependency
        "tiktoken",    # Tokenizer
        "torch",       # Deep learning library
        "tensorflow",  # For OpenAI's pretrained weights
        "pandas"       # Dataset loading
       ]
for p in pkgs:
    print(f"{p} version: {version(p)}")

matplotlib version: 3.10.0
numpy version: 2.0.2
tiktoken version: 0.9.0
torch version: 2.6.0
tensorflow version: 2.18.0
pandas version: 2.2.3

6.1 Different categories of finetuning

• No code in this section

• The most common ways to finetune language models are instruction-finetuning and classification finetuning
• Instruction-finetuning, depicted below, is the topic of the next chapter

• Classification finetuning, the topic of this chapter, is a procedure you may already be familiar with if you have a background in machine learning – it’s similar to training a convolutional network to classify handwritten digits, for example
• In classification finetuning, we have a specific number of class labels (for example, “spam” and “not spam”) that the model can output
• A classification finetuned model can only predict classes it has seen during training (for example, “spam” or “not spam”), whereas an instruction-finetuned model can usually perform many tasks
• We can think of a classification-finetuned model as a very specialized model; in practice, it is much easier to create a specialized model than a generalist model that performs well on many different tasks

6.2 Preparing the dataset

• This section prepares the dataset we use for classification finetuning
• We use a dataset consisting of spam and non-spam text messages to finetune the LLM to classify them
• First, we download and unzip the dataset

In [2]:

import urllib.request
import zipfile
import os
from pathlib import Path

url = "https://archive.ics.uci.edu/static/public/228/sms+spam+collection.zip"
zip_path = "sms_spam_collection.zip"
extracted_path = "sms_spam_collection"
data_file_path = Path(extracted_path) / "SMSSpamCollection.tsv"

def download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path):
    if data_file_path.exists():
        print(f"{data_file_path} already exists. Skipping download and extraction.")
        return

    # Downloading the file
    with urllib.request.urlopen(url) as response:
        with open(zip_path, "wb") as out_file:
            out_file.write(response.read())

    # Unzipping the file
    with zipfile.ZipFile(zip_path, "r") as zip_ref:
        zip_ref.extractall(extracted_path)

    # Add .tsv file extension
    original_file_path = Path(extracted_path) / "SMSSpamCollection"
    os.rename(original_file_path, data_file_path)
    print(f"File downloaded and saved as {data_file_path}")

try:
    download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path)
except (urllib.error.HTTPError, urllib.error.URLError, TimeoutError) as e:
    print(f"Primary URL failed: {e}. Trying backup URL...")
    url = "https://f001.backblazeb2.com/file/LLMs-from-scratch/sms%2Bspam%2Bcollection.zip"
    download_and_unzip_spam_data(url, zip_path, extracted_path, data_file_path) 

File downloaded and saved as sms_spam_collection/SMSSpamCollection.tsv

• The dataset is saved as a tab-separated text file, which we can load into a pandas DataFrame

In [4]:

import pandas as pd

df = pd.read_csv(data_file_path, sep="\t", header=None, names=["Label", "Text"])
df

	Label	Text
0	ham	Go until jurong point, crazy.. Available only ...
1	ham	Ok lar... Joking wif u oni...
2	spam	Free entry in 2 a wkly comp to win FA Cup fina...
3	ham	U dun say so early hor... U c already then say...
4	ham	Nah I don't think he goes to usf, he lives aro...
...	...	...
5567	spam	This is the 2nd time we have tried 2 contact u...
5568	ham	Will ü b going to esplanade fr home?
5569	ham	Pity, * was in mood for that. So...any other s...
5570	ham	The guy did some bitching but I acted like i'd...
5571	ham	Rofl. Its true to its name

5572 rows × 2 columns

• When we check the class distribution, we see that the data contains “ham” (i.e., “not spam”) much more frequently than “spam”

In [5]:

print(df["Label"].value_counts())

Label
ham     4825
spam     747
Name: count, dtype: int64

• For simplicity, and because we prefer a small dataset for educational purposes anyway (it will make it possible to finetune the LLM faster), we subsample (undersample) the dataset so that it contains 747 instances from each class
• (Next to undersampling, there are several other ways to deal with class balances, but they are out of the scope of a book on LLMs; you can find examples and more information in the imbalanced-learn user guide)

In [6]:

def create_balanced_dataset(df):
    
    # Count the instances of "spam"
    num_spam = df[df["Label"] == "spam"].shape[0]
    
    # Randomly sample "ham" instances to match the number of "spam" instances
    ham_subset = df[df["Label"] == "ham"].sample(num_spam, random_state=123)
    
    # Combine ham "subset" with "spam"
    balanced_df = pd.concat([ham_subset, df[df["Label"] == "spam"]])

    return balanced_df


balanced_df = create_balanced_dataset(df)
print(balanced_df["Label"].value_counts())

Label
ham     747
spam    747
Name: count, dtype: int64

• Next, we change the string class labels “ham” and “spam” into integer class labels 0 and 1:

In [7]:

balanced_df["Label"] = balanced_df["Label"].map({"ham": 0, "spam": 1})    

In [8]:

balanced_df

	Label	Text
4307	0	Awww dat is sweet! We can think of something t...
4138	0	Just got to <#>
4831	0	The word "Checkmate" in chess comes from the P...
4461	0	This is wishing you a great day. Moji told me ...
5440	0	Thank you. do you generally date the brothas?
...	...	...
5537	1	Want explicit SEX in 30 secs? Ring 02073162414...
5540	1	ASKED 3MOBILE IF 0870 CHATLINES INCLU IN FREE ...
5547	1	Had your contract mobile 11 Mnths? Latest Moto...
5566	1	REMINDER FROM O2: To get 2.50 pounds free call...
5567	1	This is the 2nd time we have tried 2 contact u...

1494 rows × 2 columns

• Let’s now define a function that randomly divides the dataset into training, validation, and test subsets

In [9]:

def random_split(df, train_frac, validation_frac):
    # Shuffle the entire DataFrame
    df = df.sample(frac=1, random_state=123).reset_index(drop=True)

    # Calculate split indices
    train_end = int(len(df) * train_frac)
    validation_end = train_end + int(len(df) * validation_frac)

    # Split the DataFrame
    train_df = df[:train_end]
    validation_df = df[train_end:validation_end]
    test_df = df[validation_end:]

    return train_df, validation_df, test_df

train_df, validation_df, test_df = random_split(balanced_df, 0.7, 0.1)
# Test size is implied to be 0.2 as the remainder

train_df.to_csv("train.csv", index=None)
validation_df.to_csv("validation.csv", index=None)
test_df.to_csv("test.csv", index=None)

6.3 Creating data loaders

• Note that the text messages have different lengths; if we want to combine multiple training examples in a batch, we have to either
  1. truncate all messages to the length of the shortest message in the dataset or batch
  2. pad all messages to the length of the longest message in the dataset or batch
• We choose option 2 and pad all messages to the longest message in the dataset
• For that, we use <|endoftext|> as a padding token, as discussed in chapter 2

In [10]:

import tiktoken

tokenizer = tiktoken.get_encoding("gpt2")
print(tokenizer.encode("<|endoftext|>", allowed_special={"<|endoftext|>"}))

[50256]

• The SpamDataset class below identifies the longest sequence in the training dataset and adds the padding token to the others to match that sequence length

In [11]:

import torch
from torch.utils.data import Dataset


class SpamDataset(Dataset):
    def __init__(self, csv_file, tokenizer, max_length=None, pad_token_id=50256):
        self.data = pd.read_csv(csv_file)

        # Pre-tokenize texts
        self.encoded_texts = [
            tokenizer.encode(text) for text in self.data["Text"]
        ]

        if max_length is None:
            self.max_length = self._longest_encoded_length()
        else:
            self.max_length = max_length
            # Truncate sequences if they are longer than max_length
            self.encoded_texts = [
                encoded_text[:self.max_length]
                for encoded_text in self.encoded_texts
            ]

        # Pad sequences to the longest sequence
        self.encoded_texts = [
            encoded_text + [pad_token_id] * (self.max_length - len(encoded_text))
            for encoded_text in self.encoded_texts
        ]

    def __getitem__(self, index):
        encoded = self.encoded_texts[index]
        label = self.data.iloc[index]["Label"]
        return (
            torch.tensor(encoded, dtype=torch.long),
            torch.tensor(label, dtype=torch.long)
        )

    def __len__(self):
        return len(self.data)

    def _longest_encoded_length(self):
        max_length = 0
        for encoded_text in self.encoded_texts:
            encoded_length = len(encoded_text)
            if encoded_length > max_length:
                max_length = encoded_length
        return max_length
        # Note: A more pythonic version to implement this method
        # is the following, which is also used in the next chapter:
        # return max(len(encoded_text) for encoded_text in self.encoded_texts)

In [12]:

train_dataset = SpamDataset(
    csv_file="train.csv",
    max_length=None,
    tokenizer=tokenizer
)

print(train_dataset.max_length)

120

• We also pad the validation and test set to the longest training sequence
• Note that validation and test set samples that are longer than the longest training example are being truncated via encoded_text[:self.max_length] in the SpamDataset code
• This behavior is entirely optional, and it would also work well if we set max_length=None in both the validation and test set cases

In [13]:

val_dataset = SpamDataset(
    csv_file="validation.csv",
    max_length=train_dataset.max_length,
    tokenizer=tokenizer
)
test_dataset = SpamDataset(
    csv_file="test.csv",
    max_length=train_dataset.max_length,
    tokenizer=tokenizer
)

• Next, we use the dataset to instantiate the data loaders, which is similar to creating the data loaders in previous chapters

In [14]:

from torch.utils.data import DataLoader

num_workers = 0
batch_size = 8

torch.manual_seed(123)

train_loader = DataLoader(
    dataset=train_dataset,
    batch_size=batch_size,
    shuffle=True,
    num_workers=num_workers,
    drop_last=True,
)

val_loader = DataLoader(
    dataset=val_dataset,
    batch_size=batch_size,
    num_workers=num_workers,
    drop_last=False,
)

test_loader = DataLoader(
    dataset=test_dataset,
    batch_size=batch_size,
    num_workers=num_workers,
    drop_last=False,
)

• As a verification step, we iterate through the data loaders and ensure that the batches contain 8 training examples each, where each training example consists of 120 tokens

In [15]:

print("Train loader:")
for input_batch, target_batch in train_loader:
    pass

print("Input batch dimensions:", input_batch.shape)
print("Label batch dimensions", target_batch.shape)

Train loader:
Input batch dimensions: torch.Size([8, 120])
Label batch dimensions torch.Size([8])

• Lastly, let’s print the total number of batches in each dataset

In [16]:

print(f"{len(train_loader)} training batches")
print(f"{len(val_loader)} validation batches")
print(f"{len(test_loader)} test batches")

130 training batches
19 validation batches
38 test batches

6.4 Initializing a model with pretrained weights

• In this section, we initialize the pretrained model we worked with in the previous chapter

In [17]:

CHOOSE_MODEL = "gpt2-small (124M)"
INPUT_PROMPT = "Every effort moves"

BASE_CONFIG = {
    "vocab_size": 50257,     # Vocabulary size
    "context_length": 1024,  # Context length
    "drop_rate": 0.0,        # Dropout rate
    "qkv_bias": True         # Query-key-value bias
}

model_configs = {
    "gpt2-small (124M)": {"emb_dim": 768, "n_layers": 12, "n_heads": 12},
    "gpt2-medium (355M)": {"emb_dim": 1024, "n_layers": 24, "n_heads": 16},
    "gpt2-large (774M)": {"emb_dim": 1280, "n_layers": 36, "n_heads": 20},
    "gpt2-xl (1558M)": {"emb_dim": 1600, "n_layers": 48, "n_heads": 25},
}

BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

assert train_dataset.max_length <= BASE_CONFIG["context_length"], (
    f"Dataset length {train_dataset.max_length} exceeds model's context "
    f"length {BASE_CONFIG['context_length']}. Reinitialize data sets with "
    f"`max_length={BASE_CONFIG['context_length']}`"
)

In [18]:

from gpt_download import download_and_load_gpt2
from previous_chapters import GPTModel, load_weights_into_gpt
# If the `previous_chapters.py` file is not available locally,
# you can import it from the `llms-from-scratch` PyPI package.
# For details, see: https://github.com/rasbt/LLMs-from-scratch/tree/main/pkg
# E.g.,
# from llms_from_scratch.ch04 import GPTModel
# from llms_from_scratch.ch05 import download_and_load_gpt2, load_weights_into_gpt

model_size = CHOOSE_MODEL.split(" ")[-1].lstrip("(").rstrip(")")
settings, params = download_and_load_gpt2(model_size=model_size, models_dir="gpt2")

model = GPTModel(BASE_CONFIG)
load_weights_into_gpt(model, params)
model.eval();

File already exists and is up-to-date: gpt2/124M/checkpoint
File already exists and is up-to-date: gpt2/124M/encoder.json
File already exists and is up-to-date: gpt2/124M/hparams.json
File already exists and is up-to-date: gpt2/124M/model.ckpt.data-00000-of-00001
File already exists and is up-to-date: gpt2/124M/model.ckpt.index
File already exists and is up-to-date: gpt2/124M/model.ckpt.meta
File already exists and is up-to-date: gpt2/124M/vocab.bpe

• To ensure that the model was loaded correctly, let’s double-check that it generates coherent text

In [19]:

from previous_chapters import (
    generate_text_simple,
    text_to_token_ids,
    token_ids_to_text
)

# Alternatively:
# from llms_from_scratch.ch05 import (
#    generate_text_simple,
#    text_to_token_ids,
#    token_ids_to_text
# )


text_1 = "Every effort moves you"

token_ids = generate_text_simple(
    model=model,
    idx=text_to_token_ids(text_1, tokenizer),
    max_new_tokens=15,
    context_size=BASE_CONFIG["context_length"]
)

print(token_ids_to_text(token_ids, tokenizer))

Every effort moves you forward.

The first step is to understand the importance of your work

• Before we finetune the model as a classifier, let’s see if the model can perhaps already classify spam messages via prompting

In [20]:

text_2 = (
    "Is the following text 'spam'? Answer with 'yes' or 'no':"
    " 'You are a winner you have been specially"
    " selected to receive $1000 cash or a $2000 award.'"
)

token_ids = generate_text_simple(
    model=model,
    idx=text_to_token_ids(text_2, tokenizer),
    max_new_tokens=23,
    context_size=BASE_CONFIG["context_length"]
)

print(token_ids_to_text(token_ids, tokenizer))

Is the following text 'spam'? Answer with 'yes' or 'no': 'You are a winner you have been specially selected to receive $1000 cash or a $2000 award.'

The following text 'spam'? Answer with 'yes' or 'no': 'You are a winner

• As we can see, the model is not very good at following instructions
• This is expected, since it has only been pretrained and not instruction-finetuned (instruction finetuning will be covered in the next chapter)

6.5 Adding a classification head

• In this section, we are modifying the pretrained LLM to make it ready for classification finetuning
• Let’s take a look at the model architecture first

In [21]:

print(model)

GPTModel(
  (tok_emb): Embedding(50257, 768)
  (pos_emb): Embedding(1024, 768)
  (drop_emb): Dropout(p=0.0, inplace=False)
  (trf_blocks): Sequential(
    (0): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (1): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (2): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (3): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (4): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (5): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (6): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (7): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (8): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (9): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (10): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
    (11): TransformerBlock(
      (att): MultiHeadAttention(
        (W_query): Linear(in_features=768, out_features=768, bias=True)
        (W_key): Linear(in_features=768, out_features=768, bias=True)
        (W_value): Linear(in_features=768, out_features=768, bias=True)
        (out_proj): Linear(in_features=768, out_features=768, bias=True)
        (dropout): Dropout(p=0.0, inplace=False)
      )
      (ff): FeedForward(
        (layers): Sequential(
          (0): Linear(in_features=768, out_features=3072, bias=True)
          (1): GELU()
          (2): Linear(in_features=3072, out_features=768, bias=True)
        )
      )
      (norm1): LayerNorm()
      (norm2): LayerNorm()
      (drop_resid): Dropout(p=0.0, inplace=False)
    )
  )
  (final_norm): LayerNorm()
  (out_head): Linear(in_features=768, out_features=50257, bias=False)
)

• Above, we can see the architecture we implemented in chapter 4 neatly laid out
• The goal is to replace and finetune the output layer
• To achieve this, we first freeze the model, meaning that we make all layers non-trainable

In [22]:

for param in model.parameters():
    param.requires_grad = False

• Then, we replace the output layer (model.out_head), which originally maps the layer inputs to 50,257 dimensions (the size of the vocabulary)
• Since we finetune the model for binary classification (predicting 2 classes, “spam” and “not spam”), we can replace the output layer as shown below, which will be trainable by default
• Note that we use BASE_CONFIG["emb_dim"] (which is equal to 768 in the "gpt2-small (124M)" model) to keep the code below more general

In [23]:

torch.manual_seed(123)

num_classes = 2
model.out_head = torch.nn.Linear(in_features=BASE_CONFIG["emb_dim"], out_features=num_classes)

• Technically, it’s sufficient to only train the output layer
• However, as I found in Finetuning Large Language Models, experiments show that finetuning additional layers can noticeably improve the performance
• So, we are also making the last transformer block and the final LayerNorm module connecting the last transformer block to the output layer trainable

In [24]:

for param in model.trf_blocks[-1].parameters():
    param.requires_grad = True

for param in model.final_norm.parameters():
    param.requires_grad = True

• We can still use this model similar to before in previous chapters
• For example, let’s feed it some text input

In [25]:

inputs = tokenizer.encode("Do you have time")
inputs = torch.tensor(inputs).unsqueeze(0)
print("Inputs:", inputs)
print("Inputs dimensions:", inputs.shape) # shape: (batch_size, num_tokens)

Inputs: tensor([[5211,  345,  423,  640]])
Inputs dimensions: torch.Size([1, 4])

• What’s different compared to previous chapters is that it now has two output dimensions instead of 50,257

In [26]:

with torch.no_grad():
    outputs = model(inputs)

print("Outputs:\n", outputs)
print("Outputs dimensions:", outputs.shape) # shape: (batch_size, num_tokens, num_classes)

Outputs:
 tensor([[[-1.5854,  0.9904],
         [-3.7235,  7.4548],
         [-2.2661,  6.6049],
         [-3.5983,  3.9902]]])
Outputs dimensions: torch.Size([1, 4, 2])

• As discussed in previous chapters, for each input token, there’s one output vector
• Since we fed the model a text sample with 4 input tokens, the output consists of 4 2-dimensional output vectors above

• In chapter 3, we discussed the attention mechanism, which connects each input token to each other input token
• In chapter 3, we then also introduced the causal attention mask that is used in GPT-like models; this causal mask lets a current token only attend to the current and previous token positions
• Based on this causal attention mechanism, the 4th (last) token contains the most information among all tokens because it’s the only token that includes information about all other tokens
• Hence, we are particularly interested in this last token, which we will finetune for the spam classification task

In [27]:

print("Last output token:", outputs[:, -1, :])

Last output token: tensor([[-3.5983,  3.9902]])

6.6 Calculating the classification loss and accuracy

• Before explaining the loss calculation, let’s have a brief look at how the model outputs are turned into class labels

In [28]:

print("Last output token:", outputs[:, -1, :])

Last output token: tensor([[-3.5983,  3.9902]])

• Similar to chapter 5, we convert the outputs (logits) into probability scores via the softmax function and then obtain the index position of the largest probability value via the argmax function

In [29]:

probas = torch.softmax(outputs[:, -1, :], dim=-1)
label = torch.argmax(probas)
print("Class label:", label.item())

Class label: 1

• Note that the softmax function is optional here, as explained in chapter 5, because the largest outputs correspond to the largest probability scores

In [30]:

logits = outputs[:, -1, :]
label = torch.argmax(logits)
print("Class label:", label.item())

Class label: 1

• We can apply this concept to calculate the so-called classification accuracy, which computes the percentage of correct predictions in a given dataset
• To calculate the classification accuracy, we can apply the preceding argmax-based prediction code to all examples in a dataset and calculate the fraction of correct predictions as follows:

In [31]:

def calc_accuracy_loader(data_loader, model, device, num_batches=None):
    model.eval()
    correct_predictions, num_examples = 0, 0

    if num_batches is None:
        num_batches = len(data_loader)
    else:
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            input_batch, target_batch = input_batch.to(device), target_batch.to(device)

            with torch.no_grad():
                logits = model(input_batch)[:, -1, :]  # Logits of last output token
            predicted_labels = torch.argmax(logits, dim=-1)

            num_examples += predicted_labels.shape[0]
            correct_predictions += (predicted_labels == target_batch).sum().item()
        else:
            break
    return correct_predictions / num_examples

• Let’s apply the function to calculate the classification accuracies for the different datasets:

In [32]:

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Note:
# Uncommenting the following lines will allow the code to run on Apple Silicon chips, if applicable,
# which is approximately 2x faster than on an Apple CPU (as measured on an M3 MacBook Air).
# As of this writing, in PyTorch 2.4, the results obtained via CPU and MPS were identical.
# However, in earlier versions of PyTorch, you may observe different results when using MPS.

#if torch.cuda.is_available():
#    device = torch.device("cuda")
#elif torch.backends.mps.is_available():
#    device = torch.device("mps")
#else:
#    device = torch.device("cpu")
#print(f"Running on {device} device.")

model.to(device) # no assignment model = model.to(device) necessary for nn.Module classes

torch.manual_seed(123) # For reproducibility due to the shuffling in the training data loader

train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=10)
val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=10)
test_accuracy = calc_accuracy_loader(test_loader, model, device, num_batches=10)

print(f"Training accuracy: {train_accuracy*100:.2f}%")
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
print(f"Test accuracy: {test_accuracy*100:.2f}%")

Training accuracy: 46.25%
Validation accuracy: 45.00%
Test accuracy: 48.75%

• As we can see, the prediction accuracies are not very good, since we haven’t finetuned the model, yet

• Before we can start finetuning (/training), we first have to define the loss function we want to optimize during training

• The goal is to maximize the spam classification accuracy of the model; however, classification accuracy is not a differentiable function

• Hence, instead, we minimize the cross-entropy loss as a proxy for maximizing the classification accuracy (you can learn more about this topic in lecture 8 of my freely available Introduction to Deep Learning class)

• The calc_loss_batch function is the same here as in chapter 5, except that we are only interested in optimizing the last token model(input_batch)[:, -1, :] instead of all tokens model(input_batch)

In [33]:

def calc_loss_batch(input_batch, target_batch, model, device):
    input_batch, target_batch = input_batch.to(device), target_batch.to(device)
    logits = model(input_batch)[:, -1, :]  # Logits of last output token
    loss = torch.nn.functional.cross_entropy(logits, target_batch)
    return loss

The calc_loss_loader is exactly the same as in chapter 5

In [34]:

# Same as in chapter 5
def calc_loss_loader(data_loader, model, device, num_batches=None):
    total_loss = 0.
    if len(data_loader) == 0:
        return float("nan")
    elif num_batches is None:
        num_batches = len(data_loader)
    else:
        # Reduce the number of batches to match the total number of batches in the data loader
        # if num_batches exceeds the number of batches in the data loader
        num_batches = min(num_batches, len(data_loader))
    for i, (input_batch, target_batch) in enumerate(data_loader):
        if i < num_batches:
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            total_loss += loss.item()
        else:
            break
    return total_loss / num_batches

• Using the calc_closs_loader, we compute the initial training, validation, and test set losses before we start training

In [35]:

with torch.no_grad(): # Disable gradient tracking for efficiency because we are not training, yet
    train_loss = calc_loss_loader(train_loader, model, device, num_batches=5)
    val_loss = calc_loss_loader(val_loader, model, device, num_batches=5)
    test_loss = calc_loss_loader(test_loader, model, device, num_batches=5)

print(f"Training loss: {train_loss:.3f}")
print(f"Validation loss: {val_loss:.3f}")
print(f"Test loss: {test_loss:.3f}")

Training loss: 2.453
Validation loss: 2.583
Test loss: 2.322

• In the next section, we train the model to improve the loss values and consequently the classification accuracy

6.7 Finetuning the model on supervised data

• In this section, we define and use the training function to improve the classification accuracy of the model
• The train_classifier_simple function below is practically the same as the train_model_simple function we used for pretraining the model in chapter 5
• The only two differences are that we now
  1. track the number of training examples seen (examples_seen) instead of the number of tokens seen
  2. calculate the accuracy after each epoch instead of printing a sample text after each epoch

In [36]:

# Overall the same as `train_model_simple` in chapter 5
def train_classifier_simple(model, train_loader, val_loader, optimizer, device, num_epochs,
                            eval_freq, eval_iter):
    # Initialize lists to track losses and examples seen
    train_losses, val_losses, train_accs, val_accs = [], [], [], []
    examples_seen, global_step = 0, -1

    # Main training loop
    for epoch in range(num_epochs):
        model.train()  # Set model to training mode

        for input_batch, target_batch in train_loader:
            optimizer.zero_grad() # Reset loss gradients from previous batch iteration
            loss = calc_loss_batch(input_batch, target_batch, model, device)
            loss.backward() # Calculate loss gradients
            optimizer.step() # Update model weights using loss gradients
            examples_seen += input_batch.shape[0] # New: track examples instead of tokens
            global_step += 1

            # Optional evaluation step
            if global_step % eval_freq == 0:
                train_loss, val_loss = evaluate_model(
                    model, train_loader, val_loader, device, eval_iter)
                train_losses.append(train_loss)
                val_losses.append(val_loss)
                print(f"Ep {epoch+1} (Step {global_step:06d}): "
                      f"Train loss {train_loss:.3f}, Val loss {val_loss:.3f}")

        # Calculate accuracy after each epoch
        train_accuracy = calc_accuracy_loader(train_loader, model, device, num_batches=eval_iter)
        val_accuracy = calc_accuracy_loader(val_loader, model, device, num_batches=eval_iter)
        print(f"Training accuracy: {train_accuracy*100:.2f}% | ", end="")
        print(f"Validation accuracy: {val_accuracy*100:.2f}%")
        train_accs.append(train_accuracy)
        val_accs.append(val_accuracy)

    return train_losses, val_losses, train_accs, val_accs, examples_seen

• The evaluate_model function used in the train_classifier_simple is the same as the one we used in chapter 5

In [37]:

# Same as chapter 5
def evaluate_model(model, train_loader, val_loader, device, eval_iter):
    model.eval()
    with torch.no_grad():
        train_loss = calc_loss_loader(train_loader, model, device, num_batches=eval_iter)
        val_loss = calc_loss_loader(val_loader, model, device, num_batches=eval_iter)
    model.train()
    return train_loss, val_loss

• The training takes about 5 minutes on a M3 MacBook Air laptop computer and less than half a minute on a V100 or A100 GPU

In [38]:

import time

start_time = time.time()

torch.manual_seed(123)

optimizer = torch.optim.AdamW(model.parameters(), lr=5e-5, weight_decay=0.1)

num_epochs = 5
train_losses, val_losses, train_accs, val_accs, examples_seen = train_classifier_simple(
    model, train_loader, val_loader, optimizer, device,
    num_epochs=num_epochs, eval_freq=50, eval_iter=5,
)

end_time = time.time()
execution_time_minutes = (end_time - start_time) / 60
print(f"Training completed in {execution_time_minutes:.2f} minutes.")

Ep 1 (Step 000000): Train loss 2.153, Val loss 2.392
Ep 1 (Step 000050): Train loss 0.617, Val loss 0.637
Ep 1 (Step 000100): Train loss 0.523, Val loss 0.557
Training accuracy: 70.00% | Validation accuracy: 72.50%
Ep 2 (Step 000150): Train loss 0.561, Val loss 0.489
Ep 2 (Step 000200): Train loss 0.419, Val loss 0.397
Ep 2 (Step 000250): Train loss 0.409, Val loss 0.353
Training accuracy: 82.50% | Validation accuracy: 85.00%
Ep 3 (Step 000300): Train loss 0.333, Val loss 0.320
Ep 3 (Step 000350): Train loss 0.340, Val loss 0.306
Training accuracy: 90.00% | Validation accuracy: 90.00%
Ep 4 (Step 000400): Train loss 0.136, Val loss 0.200
Ep 4 (Step 000450): Train loss 0.153, Val loss 0.132
Ep 4 (Step 000500): Train loss 0.222, Val loss 0.137
Training accuracy: 100.00% | Validation accuracy: 97.50%
Ep 5 (Step 000550): Train loss 0.207, Val loss 0.143
Ep 5 (Step 000600): Train loss 0.083, Val loss 0.074
Training accuracy: 100.00% | Validation accuracy: 97.50%
Training completed in 5.31 minutes.

• Similar to chapter 5, we use matplotlib to plot the loss function for the training and validation set

In [39]:

import matplotlib.pyplot as plt

def plot_values(epochs_seen, examples_seen, train_values, val_values, label="loss"):
    fig, ax1 = plt.subplots(figsize=(5, 3))

    # Plot training and validation loss against epochs
    ax1.plot(epochs_seen, train_values, label=f"Training {label}")
    ax1.plot(epochs_seen, val_values, linestyle="-.", label=f"Validation {label}")
    ax1.set_xlabel("Epochs")
    ax1.set_ylabel(label.capitalize())
    ax1.legend()

    # Create a second x-axis for examples seen
    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
    ax2.plot(examples_seen, train_values, alpha=0)  # Invisible plot for aligning ticks
    ax2.set_xlabel("Examples seen")

    fig.tight_layout()  # Adjust layout to make room
    plt.savefig(f"{label}-plot.pdf")
    plt.show()

In [40]:

epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))
examples_seen_tensor = torch.linspace(0, examples_seen, len(train_losses))

plot_values(epochs_tensor, examples_seen_tensor, train_losses, val_losses)

• Above, based on the downward slope, we see that the model learns well
• Furthermore, the fact that the training and validation loss are very close indicates that the model does not tend to overfit the training data
• Similarly, we can plot the accuracy below

In [41]:

epochs_tensor = torch.linspace(0, num_epochs, len(train_accs))
examples_seen_tensor = torch.linspace(0, examples_seen, len(train_accs))

plot_values(epochs_tensor, examples_seen_tensor, train_accs, val_accs, label="accuracy")

• Based on the accuracy plot above, we can see that the model achieves a relatively high training and validation accuracy after epochs 4 and 5
• However, we have to keep in mind that we specified eval_iter=5 in the training function earlier, which means that we only estimated the training and validation set performances
• We can compute the training, validation, and test set performances over the complete dataset as follows below

In [42]:

train_accuracy = calc_accuracy_loader(train_loader, model, device)
val_accuracy = calc_accuracy_loader(val_loader, model, device)
test_accuracy = calc_accuracy_loader(test_loader, model, device)

print(f"Training accuracy: {train_accuracy*100:.2f}%")
print(f"Validation accuracy: {val_accuracy*100:.2f}%")
print(f"Test accuracy: {test_accuracy*100:.2f}%")

Training accuracy: 97.21%
Validation accuracy: 97.32%
Test accuracy: 95.67%

• We can see that the training and validation set performances are practically identical
• However, based on the slightly lower test set performance, we can see that the model overfits the training data to a very small degree, as well as the validation data that has been used for tweaking some of the hyperparameters, such as the learning rate
• This is normal, however, and this gap could potentially be further reduced by increasing the model’s dropout rate (drop_rate) or the weight_decay in the optimizer setting

6.8 Using the LLM as a spam classifier

• Finally, let’s use the finetuned GPT model in action
• The classify_review function below implements the data preprocessing steps similar to the SpamDataset we implemented earlier
• Then, the function returns the predicted integer class label from the model and returns the corresponding class name

In [43]:

def classify_review(text, model, tokenizer, device, max_length=None, pad_token_id=50256):
    model.eval()

    # Prepare inputs to the model
    input_ids = tokenizer.encode(text)
    supported_context_length = model.pos_emb.weight.shape[0]
    # Note: In the book, this was originally written as pos_emb.weight.shape[1] by mistake
    # It didn't break the code but would have caused unnecessary truncation (to 768 instead of 1024)

    # Truncate sequences if they too long
    input_ids = input_ids[:min(max_length, supported_context_length)]
    assert max_length is not None, (
        "max_length must be specified. If you want to use the full model context, "
        "pass max_length=model.pos_emb.weight.shape[0]."
    )
    assert max_length <= supported_context_length, (
        f"max_length ({max_length}) exceeds model's supported context length ({supported_context_length})."
    )    
    # Alternatively, a more robust version is the following one, which handles the max_length=None case better
    # max_len = min(max_length,supported_context_length) if max_length else supported_context_length
    # input_ids = input_ids[:max_len]
    
    # Pad sequences to the longest sequence
    input_ids += [pad_token_id] * (max_length - len(input_ids))
    input_tensor = torch.tensor(input_ids, device=device).unsqueeze(0) # add batch dimension

    # Model inference
    with torch.no_grad():
        logits = model(input_tensor)[:, -1, :]  # Logits of the last output token
    predicted_label = torch.argmax(logits, dim=-1).item()

    # Return the classified result
    return "spam" if predicted_label == 1 else "not spam"

• Let’s try it out on a few examples below

In [44]:

text_1 = (
    "You are a winner you have been specially"
    " selected to receive $1000 cash or a $2000 award."
)

print(classify_review(
    text_1, model, tokenizer, device, max_length=train_dataset.max_length
))

spam

In [45]:

text_2 = (
    "Hey, just wanted to check if we're still on"
    " for dinner tonight? Let me know!"
)

print(classify_review(
    text_2, model, tokenizer, device, max_length=train_dataset.max_length
))

not spam

• Finally, let’s save the model in case we want to reuse the model later without having to train it again

In [46]:

torch.save(model.state_dict(), "review_classifier.pth")

• Then, in a new session, we could load the model as follows

In [47]:

model_state_dict = torch.load("review_classifier.pth", map_location=device, weights_only=True)
model.load_state_dict(model_state_dict)

<All keys matched successfully>

Summary and takeaways

• See the ./gpt_class_finetune.py script, a self-contained script for classification finetuning
• You can find the exercise solutions in ./exercise-solutions.ipynb
• In addition, interested readers can find an introduction to parameter-efficient training with low-rank adaptation (LoRA) in appendix E