4.2 [SFT] Instruction SFT

import json
import os
import urllib


def download_and_load_file(file_path, url):
    if not os.path.exists(file_path):
        with urllib.request.urlopen(url) as response:
            text_data = response.read().decode("utf-8")
        with open(file_path, "w", encoding="utf-8") as file:
            file.write(text_data)
    with open(file_path, "r", encoding="utf-8") as file:
        data = json.load(file)
    return data


file_path = "instruction-data.json"
url = (
    "https://raw.githubusercontent.com/rasbt/LLMs-from-scratch"
    "/main/ch07/01_main-chapter-code/instruction-data.json"
)

data = download_and_load_file(file_path, url)
print("Number of entries:", len(data))
print("Example entry:\n", data[50])

# 从 JSON 文件中加载的 data 列表包含了1100 个指令数据集样本
>>> type(data)
<class 'list'>
>>> len(data)
1100
>>>
>>> from rich.pretty import pprint
>>> pprint(data[0])
{
│   'instruction': 'Evaluate the following phrase by transforming it into the spelling given.',
│   'input': 'freind --> friend',
│   'output': 'The spelling of the given phrase "freind" is incorrect, the correct spelling is "friend".'
}

train_portion = int(len(data) * 0.85)  # 85% for training
test_portion = int(len(data) * 0.1)    # 10% for testing
val_portion = len(data) - train_portion - test_portion  # Remaining 5% for validation

train_data = data[:train_portion]
test_data = data[train_portion:train_portion + test_portion]
val_data = data[train_portion + test_portion:]

def format_input(entry):
    instruction_text = (
        f"Below is an instruction that describes a task. "
        f"Write a response that appropriately completes the request."
        f"\n\n### Instruction:\n{entry['instruction']}"
    )

    input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""

    return instruction_text + input_text


model_input = format_input(data[50])
desired_response = f"\n\n### Response:\n{data[50]['output']}"

print(model_input + desired_response)

# Below is an instruction that describes a task. Write a response that appropriately completes the request.

# ### Instruction:
# Identify the correct spelling of the following word.

# ### Input:
# Ocassion

# ### Response:
# The correct spelling is 'Occasion.'

import torch
from torch.utils.data import Dataset


class InstructionDataset(Dataset):
    def __init__(self, data, tokenizer):
        self.data = data

        # Pre-tokenize texts
        self.encoded_texts = []
        for entry in data:
            instruction_plus_input = format_input(entry)
            response_text = f"\n\n### Response:\n{entry['output']}"
            full_text = instruction_plus_input + response_text
            self.encoded_texts.append(
                tokenizer.encode(full_text)
            )

    def __getitem__(self, index):
        return self.encoded_texts[index]

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

def custom_collate_draft_1(
    batch,
    pad_token_id=50256,
    device="cpu"
):
    # Find the longest sequence in the batch
    # and increase the max length by +1, which will add one extra
    # padding token below
    batch_max_length = max(len(item)+1 for item in batch)

    # Pad and prepare inputs
    inputs_lst = []

    for item in batch:
        new_item = item.copy()
        # Add an <|endoftext|> token
        new_item += [pad_token_id]
        # Pad sequences to batch_max_length
        padded = (
            new_item + [pad_token_id] *
            (batch_max_length - len(new_item))
        )
        # Via padded[:-1], we remove the extra padded token
        # that has been added via the +1 setting in batch_max_length
        # (the extra padding token will be relevant in later codes)
        inputs = torch.tensor(padded[:-1])
        inputs_lst.append(inputs)

    # Convert list of inputs to tensor and transfer to target device
    inputs_tensor = torch.stack(inputs_lst).to(device)
    return inputs_tensor

def custom_collate_draft_2(
    batch,
    pad_token_id=50256,
    device="cpu"
):
    # Find the longest sequence in the batch
    batch_max_length = max(len(item)+1 for item in batch)

    # Pad and prepare inputs
    inputs_lst, targets_lst = [], []

    for item in batch:
        new_item = item.copy()
        # Add an <|endoftext|> token
        new_item += [pad_token_id]
        # Pad sequences to max_length
        padded = (
            new_item + [pad_token_id] *
            (batch_max_length - len(new_item))
        )
        inputs = torch.tensor(padded[:-1])  # Truncate the last token for inputs
        targets = torch.tensor(padded[1:])  # Shift +1 to the right for targets
        inputs_lst.append(inputs)
        targets_lst.append(targets)

    # Convert list of inputs to tensor and transfer to target device
    inputs_tensor = torch.stack(inputs_lst).to(device)
    targets_tensor = torch.stack(targets_lst).to(device)
    return inputs_tensor, targets_tensor

def custom_collate_fn(
    batch,
    pad_token_id=50256,
    ignore_index=-100,
    allowed_max_length=None,
    device="cpu"
):
    # Find the longest sequence in the batch
    batch_max_length = max(len(item)+1 for item in batch)

    # Pad and prepare inputs and targets
    inputs_lst, targets_lst = [], []

    for item in batch:
        new_item = item.copy()
        # Add an <|endoftext|> token
        new_item += [pad_token_id]
        # Pad sequences to max_length
        padded = (
            new_item + [pad_token_id] *
            (batch_max_length - len(new_item))
        )
        inputs = torch.tensor(padded[:-1])  # Truncate the last token for inputs
        targets = torch.tensor(padded[1:])  # Shift +1 to the right for targets

        # New: Replace all but the first padding tokens in targets by ignore_index
        mask = targets == pad_token_id
        indices = torch.nonzero(mask).squeeze()
        if indices.numel() > 1:
            targets[indices[1:]] = ignore_index

        # New: Optionally truncate to maximum sequence length
        if allowed_max_length is not None:
            inputs = inputs[:allowed_max_length]
            targets = targets[:allowed_max_length]

        inputs_lst.append(inputs)
        targets_lst.append(targets)

    # Convert list of inputs and targets to tensors and transfer to target device
    inputs_tensor = torch.stack(inputs_lst).to(device)
    targets_tensor = torch.stack(targets_lst).to(device)

    return inputs_tensor, targets_tensor

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

from functools import partial

customized_collate_fn = partial(
    custom_collate_fn,
    device=device,
    allowed_max_length=1024
)


from torch.utils.data import DataLoader

num_workers = 0
batch_size = 8
torch.manual_seed(123)

train_dataset = InstructionDataset(train_data, tokenizer)
train_loader = DataLoader(
    train_dataset,
    batch_size=batch_size,
    collate_fn=customized_collate_fn,
    shuffle=True,
    drop_last=True,
    num_workers=num_workers
)

val_dataset = InstructionDataset(val_data, tokenizer)
val_loader = DataLoader(
    val_dataset,
    batch_size=batch_size,
    collate_fn=customized_collate_fn,
    shuffle=False,
    drop_last=False,
    num_workers=num_workers
)

test_dataset = InstructionDataset(test_data, tokenizer)
test_loader = DataLoader(
    test_dataset,
    batch_size=batch_size,
    collate_fn=customized_collate_fn,
    shuffle=False,
    drop_last=False,
    num_workers=num_workers
)

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


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},
}

CHOOSE_MODEL = "gpt2-medium (355M)"

BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

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();

torch.manual_seed(123)


for entry in test_data[:3]:

    input_text = format_input(entry)

    token_ids = generate(
        model=model,
        idx=text_to_token_ids(input_text, tokenizer).to(device),
        max_new_tokens=256,
        context_size=BASE_CONFIG["context_length"],
        eos_id=50256
    )
    generated_text = token_ids_to_text(token_ids, tokenizer)
    response_text = (
        generated_text[len(input_text):]
        .replace("### Response:", "")
        .strip()
)

    print(input_text)
    print(f"\nCorrect response:\n>> {entry['output']}")
    print(f"\nModel response:\n>> {response_text.strip()}")
    print("-------------------------------------")

torch.manual_seed(123)

optimizer = torch.optim.AdamW(model.parameters(), lr=0.00005, weight_decay=0.1)

num_epochs = 2

train_losses, val_losses, tokens_seen = train_model_simple(
    model, train_loader, val_loader, optimizer, device,
    num_epochs=num_epochs, eval_freq=5, eval_iter=5,
    start_context=format_input(val_data[0]), tokenizer=tokenizer
)

from previous_chapters import plot_losses
# Alternatively:
# from llms_from_scratch.ch05 import plot_losses

epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))
plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)

from tqdm import tqdm

for i, entry in tqdm(enumerate(test_data), total=len(test_data)):

    input_text = format_input(entry)

    token_ids = generate(
        model=model,
        idx=text_to_token_ids(input_text, tokenizer).to(device),
        max_new_tokens=256,
        context_size=BASE_CONFIG["context_length"],
        eos_id=50256
    )
    generated_text = token_ids_to_text(token_ids, tokenizer)
    response_text = generated_text[len(input_text):].replace("### Response:", "").strip()

    test_data[i]["model_response"] = response_text


with open("instruction-data-with-response.json", "w") as file:
    json.dump(test_data, file, indent=4)  # "indent" for pretty-printing

print(test_data[0])
# {'instruction': 'Rewrite the sentence using a simile.', 
#  'input': 'The car is very fast.', 
#  'output': 'The car is as fast as lightning.', 
#  'model_response': 'The car is as fast as a bullet.'}

snap install ollama

# 启动 Ollama 应用程序
ollama serve

# Check ollama serve running
import psutil

def check_if_running(process_name):
    running = False
    for proc in psutil.process_iter(["name"]):
        if process_name in proc.info["name"]:
            running = True
            break
    return running

ollama_running = check_if_running("ollama")

if not ollama_running:
    raise RuntimeError("Ollama not running. Launch ollama before proceeding.")
print("Ollama running:", check_if_running("ollama"))

############################ MODEL ############################
ollama pull llama3 # https://ollama.com/library

ollama list
# NAME             ID              SIZE      MODIFIED
# llama3:latest    365c0bd3c000    4.7 GB    13 minutes ago

ollama show llama3
#   Model
#     architecture        llama
#     parameters          8.0B
#     context length      8192
#     embedding length    4096
#     quantization        Q4_0

#   Capabilities
#     completion

#   Parameters
#     num_keep    24
#     stop        "<|start_header_id|>"
#     stop        "<|end_header_id|>"
#     stop        "<|eot_id|>"

#   License
#     META LLAMA 3 COMMUNITY LICENSE AGREEMENT
#     Meta Llama 3 Version Release Date: April 18, 2024


############################ RUN ############################
ollama run llama3 --keepalive 5m

ollama ps
NAME             ID              SIZE      PROCESSOR    UNTIL
llama3:latest    365c0bd3c000    6.2 GB    100% CPU     4 minutes from now

ollama run llama3 # re-enter

# 进入一个可以与模型进行交互的命令行界面
>>> What do llamas eat?
# 
# 结束此次 ollama run llama3 会话
/bye

import urllib.request

def query_model(
    prompt,
    model="llama3",
    url="http://localhost:11434/api/chat"
):
    # Create the data payload as a dictionary
    data = {
        "model": model,
        "messages": [
            {"role": "user", "content": prompt}
        ],
        "options": {     # Settings below are required for deterministic responses
            "seed": 123,
            "temperature": 0,
            "num_ctx": 2048
        }
    }


    # Convert the dictionary to a JSON formatted string and encode it to bytes
    payload = json.dumps(data).encode("utf-8")

    # Create a request object, setting the method to POST and adding necessary headers
    request = urllib.request.Request(
        url,
        data=payload,
        method="POST"
    )
    request.add_header("Content-Type", "application/json")

    # Send the request and capture the response
    response_data = ""
    with urllib.request.urlopen(request) as response:
        # Read and decode the response
        while True:
            line = response.readline().decode("utf-8")
            if not line:
                break
            response_json = json.loads(line)
            response_data += response_json["message"]["content"]

    return response_data


model = "llama3"
result = query_model("What do Llamas eat?", model)
print(result)

def generate_model_scores(json_data, json_key, model="llama3"):
    scores = []
    for entry in tqdm(json_data, desc="Scoring entries"):
        prompt = (
            f"Given the input `{format_input(entry)}` "
            f"and correct output `{entry['output']}`, "
            f"score the model response `{entry[json_key]}`"
            f" on a scale from 0 to 100, where 100 is the best score. "
            f"Respond with the integer number only."
        )
        score = query_model(prompt, model)
        try:
            scores.append(int(score))
        except ValueError:
            print(f"Could not convert score: {score}")
            continue

    return scores


scores = generate_model_scores(test_data, "model_response")
print(f"Number of scores: {len(scores)} of {len(test_data)}")
print(f"Average score: {sum(scores)/len(scores):.2f}\n")

# Copyright (c) Sebastian Raschka under Apache License 2.0 (see LICENSE.txt).
# Source for "Build a Large Language Model From Scratch"
#   - https://www.manning.com/books/build-a-large-language-model-from-scratch
# Code: https://github.com/rasbt/LLMs-from-scratch
#
# A minimal instruction finetuning file based on the code in chapter 7

from functools import partial
from importlib.metadata import version
import json
import os
import re
import time
import urllib

import matplotlib.pyplot as plt
import tiktoken
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm

# Import from local files in this folder
from gpt_download import download_and_load_gpt2
from previous_chapters import (
    calc_loss_loader,
    generate,
    GPTModel,
    load_weights_into_gpt,
    text_to_token_ids,
    train_model_simple,
    token_ids_to_text
)


class InstructionDataset(Dataset):
    def __init__(self, data, tokenizer):
        self.data = data

        # Pre-tokenize texts
        self.encoded_texts = []
        for entry in data:
            instruction_plus_input = format_input(entry)
            response_text = f"\n\n### Response:\n{entry['output']}"
            full_text = instruction_plus_input + response_text
            self.encoded_texts.append(
                tokenizer.encode(full_text)
            )

    def __getitem__(self, index):
        return self.encoded_texts[index]

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


def custom_collate_fn(
    batch,
    pad_token_id=50256,
    ignore_index=-100,
    allowed_max_length=None,
    device="cpu"
):
    # Find the longest sequence in the batch
    batch_max_length = max(len(item)+1 for item in batch)

    # Pad and prepare inputs and targets
    inputs_lst, targets_lst = [], []

    for item in batch:
        new_item = item.copy()
        # Add an <|endoftext|> token
        new_item += [pad_token_id]
        # Pad sequences to max_length
        padded = new_item + [pad_token_id] * (batch_max_length - len(new_item))
        inputs = torch.tensor(padded[:-1])  # Truncate the last token for inputs
        targets = torch.tensor(padded[1:])  # Shift +1 to the right for targets

        # New: Replace all but the first padding tokens in targets by ignore_index
        mask = targets == pad_token_id
        indices = torch.nonzero(mask).squeeze()
        if indices.numel() > 1:
            targets[indices[1:]] = ignore_index

        # New: Optionally truncate to maximum sequence length
        if allowed_max_length is not None:
            inputs = inputs[:allowed_max_length]
            targets = targets[:allowed_max_length]

        inputs_lst.append(inputs)
        targets_lst.append(targets)

    # Convert list of inputs and targets to tensors and transfer to target device
    inputs_tensor = torch.stack(inputs_lst).to(device)
    targets_tensor = torch.stack(targets_lst).to(device)

    return inputs_tensor, targets_tensor


def download_and_load_file(file_path, url):

    if not os.path.exists(file_path):
        with urllib.request.urlopen(url) as response:
            text_data = response.read().decode("utf-8")
        with open(file_path, "w", encoding="utf-8") as file:
            file.write(text_data)

    with open(file_path, "r") as file:
        data = json.load(file)

    return data


def format_input(entry):
    instruction_text = (
        f"Below is an instruction that describes a task. "
        f"Write a response that appropriately completes the request."
        f"\n\n### Instruction:\n{entry['instruction']}"
    )

    input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""

    return instruction_text + input_text


def plot_losses(epochs_seen, tokens_seen, train_losses, val_losses):
    fig, ax1 = plt.subplots(figsize=(12, 6))

    # Plot training and validation loss against epochs
    ax1.plot(epochs_seen, train_losses, label="Training loss")
    ax1.plot(epochs_seen, val_losses, linestyle="-.", label="Validation loss")
    ax1.set_xlabel("Epochs")
    ax1.set_ylabel("Loss")
    ax1.legend(loc="upper right")

    # Create a second x-axis for tokens seen
    ax2 = ax1.twiny()  # Create a second x-axis that shares the same y-axis
    ax2.plot(tokens_seen, train_losses, alpha=0)  # Invisible plot for aligning ticks
    ax2.set_xlabel("Tokens seen")

    fig.tight_layout()  # Adjust layout to make room
    plot_name = "loss-plot-standalone.pdf"
    print(f"Plot saved as {plot_name}")
    plt.savefig(plot_name)
    # plt.show()


def main(test_mode=False):
    #######################################
    # Print package versions
    #######################################
    print()
    pkgs = [
        "matplotlib",  # Plotting library
        "tiktoken",    # Tokenizer
        "torch",       # Deep learning library
        "tqdm",        # Progress bar
        "tensorflow",  # For OpenAI's pretrained weights
    ]
    for p in pkgs:
        print(f"{p} version: {version(p)}")
    print(50*"-")

    #######################################
    # Download and prepare dataset
    #######################################
    file_path = "instruction-data.json"
    url = "https://raw.githubusercontent.com/rasbt/LLMs-from-scratch/main/ch07/01_main-chapter-code/instruction-data.json"
    data = download_and_load_file(file_path, url)

    train_portion = int(len(data) * 0.85)  # 85% for training
    test_portion = int(len(data) * 0.1)    # 10% for testing

    train_data = data[:train_portion]
    test_data = data[train_portion:train_portion + test_portion]
    val_data = data[train_portion + test_portion:]

    # Use very small subset for testing purposes
    if args.test_mode:
        train_data = train_data[:10]
        val_data = val_data[:10]
        test_data = test_data[:10]

    print("Training set length:", len(train_data))
    print("Validation set length:", len(val_data))
    print("Test set length:", len(test_data))
    print(50*"-")

    tokenizer = tiktoken.get_encoding("gpt2")
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    print("Device:", device)
    print(50*"-")

    customized_collate_fn = partial(custom_collate_fn, device=device, allowed_max_length=1024)

    num_workers = 0
    batch_size = 8

    torch.manual_seed(123)

    train_dataset = InstructionDataset(train_data, tokenizer)
    train_loader = DataLoader(
        train_dataset,
        batch_size=batch_size,
        collate_fn=customized_collate_fn,
        shuffle=True,
        drop_last=True,
        num_workers=num_workers
    )

    val_dataset = InstructionDataset(val_data, tokenizer)
    val_loader = DataLoader(
        val_dataset,
        batch_size=batch_size,
        collate_fn=customized_collate_fn,
        shuffle=False,
        drop_last=False,
        num_workers=num_workers
    )

    #######################################
    # Load pretrained model
    #######################################

    # Small GPT model for testing purposes
    if args.test_mode:
        BASE_CONFIG = {
            "vocab_size": 50257,
            "context_length": 120,
            "drop_rate": 0.0,
            "qkv_bias": False,
            "emb_dim": 12,
            "n_layers": 1,
            "n_heads": 2
        }
        model = GPTModel(BASE_CONFIG)
        model.eval()
        device = "cpu"
        CHOOSE_MODEL = "Small test model"

    # Code as it is used in the main chapter
    else:
        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},
        }

        CHOOSE_MODEL = "gpt2-medium (355M)"

        BASE_CONFIG.update(model_configs[CHOOSE_MODEL])

        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()
        model.to(device)

    print("Loaded model:", CHOOSE_MODEL)
    print(50*"-")

    #######################################
    # Finetuning the model
    #######################################
    print("Initial losses")
    with torch.no_grad():
        train_loss = calc_loss_loader(train_loader, model, device, num_batches=5)
        val_loss = calc_loss_loader(val_loader, model, device, num_batches=5)

    print("   Training loss:", train_loss)
    print("   Validation loss:", val_loss)

    start_time = time.time()
    optimizer = torch.optim.AdamW(model.parameters(), lr=0.00005, weight_decay=0.1)

    num_epochs = 2

    torch.manual_seed(123)
    train_losses, val_losses, tokens_seen = train_model_simple(
        model, train_loader, val_loader, optimizer, device,
        num_epochs=num_epochs, eval_freq=5, eval_iter=5,
        start_context=format_input(val_data[0]), tokenizer=tokenizer
    )

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

    epochs_tensor = torch.linspace(0, num_epochs, len(train_losses))
    plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)
    print(50*"-")

    #######################################
    # Saving results
    #######################################
    print("Generating responses")
    for i, entry in tqdm(enumerate(test_data), total=len(test_data)):

        input_text = format_input(entry)

        token_ids = generate(
            model=model,
            idx=text_to_token_ids(input_text, tokenizer).to(device),
            max_new_tokens=256,
            context_size=BASE_CONFIG["context_length"],
            eos_id=50256
        )
        generated_text = token_ids_to_text(token_ids, tokenizer)
        response_text = generated_text[len(input_text):].replace("### Response:", "").strip()

        test_data[i]["model_response"] = response_text

    test_data_path = "instruction-data-with-response-standalone.json"
    with open(test_data_path, "w") as file:
        json.dump(test_data, file, indent=4)  # "indent" for pretty-printing
    print(f"Responses saved as {test_data_path}")

    file_name = f"{re.sub(r'[ ()]', '', CHOOSE_MODEL) }-sft-standalone.pth"
    torch.save(model.state_dict(), file_name)
    print(f"Model saved as {file_name}")


if __name__ == "__main__":

    import argparse

    parser = argparse.ArgumentParser(
        description="Finetune a GPT model for classification"
    )
    parser.add_argument(
        "--test_mode",
        default=False,
        action="store_true",
        help=("This flag runs the model in test mode for internal testing purposes. "
              "Otherwise, it runs the model as it is used in the chapter (recommended).")
    )
    args = parser.parse_args()

    main(args.test_mode)