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#This file was our attempt at training the model, which ultimately failed.
!pip install datasets peft transformers
from google.colab import userdata
my_secret_key = userdata.get('Cli2')
from huggingface_hub import login
login(my_secret_key)
# Name for finetuned model and folder.
model_output = "./BudgetAdvisor"
# Dataset loading and manipulation.
from datasets import load_dataset
dataset = load_dataset("gbharti/finance-alpaca") # features: ['text', 'instruction', 'input', 'output']
# Remove empty columns from dataset.
dataset = dataset.remove_columns(["text", "input"])
# Splits dataset to test and train sets, 90 % for train and 10 % for test.
dataset = dataset["train"].train_test_split(test_size=0.1)
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
#Tokenizer and model settings.
from transformers import AutoTokenizer
from transformers import Trainer, TrainingArguments, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.2-1B", use_fast=True)
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B")
# Make arrays of token the same size.
if tokenizer.pad_token is None:
tokenizer.add_special_tokens({'pad_token': tokenizer.eos_token})
model.resize_token_embeddings(len(tokenizer))
# For memory efficiency.
model.gradient_checkpointing_enable()
# Parameter-Efficient Fine-Tuning
from peft import LoraConfig, get_peft_model
# Define a PEFT configuration for LoRA
lora_config = LoraConfig(
r=8, # Reduced rank for faster training
lora_alpha=16,
target_modules=["q_proj", "v_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
)
# Check if cuda is available.
import torch
model = get_peft_model(model, lora_config)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Preprocessing function
def preprocess_data(examples):
# Combine instruction and input as the prompt
inputs = [f"Instruction: {instr}\nInput: {inp}\n" for instr, inp in zip(examples['instruction'], examples['output'])]
targets = [output for output in examples['output']]
return {'input_text': inputs, 'target_text': targets}
train_dataset = train_dataset.map(preprocess_data, batched=True)
eval_dataset = eval_dataset.map(preprocess_data, batched=True)
# Tokenization function
def tokenize_data(examples):
model_inputs = tokenizer(
examples['input_text'],
max_length=128, # Reduced max_length for faster processing
truncation=True,
padding="max_length"
)
labels = tokenizer(
examples['target_text'],
max_length=128,
truncation=True,
padding="max_length"
)["input_ids"]
model_inputs["labels"] = labels
return model_inputs
# Tokenize the datasets
train_dataset = train_dataset.map(tokenize_data, batched=True, remove_columns=train_dataset.column_names)
eval_dataset = eval_dataset.map(tokenize_data, batched=True, remove_columns=eval_dataset.column_names)
# Set the format for PyTorch tensors
train_dataset.set_format(type="torch")
eval_dataset.set_format(type="torch")
# Training arguments and trainer.
training_args = TrainingArguments(
output_dir=model_output, # "./BudgetAdvisor"
per_device_train_batch_size=8, # Increase if GPU memory allows
per_device_eval_batch_size=8,
evaluation_strategy="steps",
eval_steps=500,
save_steps=500,
num_train_epochs=3, # Increased epochs for better training
learning_rate=5e-5,
fp16=True, # Enable mixed precision for faster training
logging_steps=100,
save_total_limit=2,
load_best_model_at_end=True,
report_to="none", # Disable reporting to third-party services
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer
)
# Message for testing.
print("Trainer is set up!")
# Trains the model and saves it.
trainer.train()
print("Model trained!")
trainer.save_model(model_output) # "./BudgetAdvisor"
tokenizer.save_pretrained(model_output)# "./BudgetAdvisor"
!zip -r BudgetAdvisor.zip ./BudgetAdvisor |