Introduction

In today’s fast-paced financial world, the ability to quickly and accurately answer complex financial questions is more crucial than ever. Machine learning models, particularly large language models (LLMs), have shown tremendous potential in enhancing financial analysis and decision-making processes. This article explores how we can leverage the power of LLaMA 3, a state-of-the-art language model, by fine-tuning it on the Finance Alpaca dataset to create a specialized financial Q&A system. We’ll also delve into how Neptune AI can be used to monitor and optimize this process, ensuring the highest quality results.

Understanding Financial Q&A Systems

The Basics of Financial Q&A

Financial Q&A systems are designed to provide accurate and timely answers to a wide range of finance-related questions. These can range from simple queries about stock prices to complex inquiries about market trends, risk assessment, and investment strategies. Traditional systems often rely on rule-based approaches or basic machine learning techniques, which can struggle with the nuances and complexity of financial language.

Traditional Approaches and Their Limitations

Historically, financial Q&A systems have relied on a combination of rule-based approaches and basic machine-learning techniques. While these methods have served the industry well for many years, they often struggle with several key challenges:

1. Complexity of Financial Language: Financial terminology and concepts can be highly nuanced and context-dependent, making it difficult for traditional systems to accurately interpret and respond to queries.

2. Rapid Market Changes: The financial world is dynamic, with market conditions and regulations constantly evolving. Traditional systems may struggle to keep pace with these changes and provide up-to-date information.

3. Handling Unstructured Data: A significant portion of financial information comes in the form of unstructured data, such as news articles, reports, and social media. Traditional systems often have difficulty extracting meaningful insights from these sources.

4. Limited Contextual Understanding: Many traditional Q&A systems operate on a simple input-output basis, lacking the ability to understand the broader context of a query or to engage in more nuanced, multi-turn conversations.

These limitations highlight the need for more advanced, AI-driven approaches to financial Q&A systems. This is where large language models like LLaMA 3 come into play, offering the potential for more sophisticated, context-aware financial analysis.

Introducing LLaMA 3

LLaMA 3 (Large Language Model Meta AI 3) represents the latest advancement in Meta AI’s series of powerful language models. Building upon the successes of its predecessors, LLaMA 3 offers several key improvements that make it particularly well-suited for financial applications:

Enhanced Natural Language Understanding

• LLaMA 3 demonstrates a remarkable ability to understand and generate human-like text across a wide range of topics and styles. This capability is crucial for interpreting complex financial queries and providing coherent, contextually appropriate responses.

Improved Context Retention

• One of the standout features of LLaMA 3 is its enhanced ability to maintain context over longer sequences of text. This is particularly valuable in financial Q&A, where understanding the broader context of a query can significantly impact the accuracy and relevance of the response.

Efficient Processing

• Despite its advanced capabilities, LLaMA 3 is designed with efficiency in mind. This allows for quicker response times, which is crucial in the fast-paced world of finance where timely information can make a significant difference.

Adaptability through Fine-Tuning

• While LLaMA 3 comes pre-trained on a vast corpus of general knowledge, its architecture allows for effective fine-tuning on specialized datasets. This adaptability is key to creating a financial Q&A system that can handle industry-specific terminology and concepts with high accuracy.

These features make LLaMA 3 an excellent candidate for creating advanced financial Q&A systems when fine-tuned on specialized datasets.

The Finance Alpaca Dataset

The Finance Alpaca dataset is a comprehensive collection of financial questions and answers, covering a wide range of topics including:

• Stock market analysis
• Corporate finance
• Investment strategies
• Economic indicators
• Financial regulations

Benefits of Using Finance Alpaca for Fine-Tuning

Fine-tuning LLaMA 3 on the Finance Alpaca dataset offers several advantages:

1. Domain-Specific Knowledge: By training on Finance Alpaca, LLaMA 3 can acquire deep, specialized knowledge of financial concepts and terminology.

2. Improved Accuracy: The model learns to provide more accurate and relevant responses to finance-related queries.

3. Enhanced Contextual Understanding: Finance Alpaca helps the model understand the specific contexts in which certain financial terms and concepts are used.

4. Realistic Q&A Patterns: The dataset exposes the model to typical patterns of financial questions and answers, improving its ability to engage in natural, fluent conversations about finance.

This dataset provides an ideal foundation for fine-tuning LLaMA 3 to create a specialized financial assistant capable of handling complex financial queries with high accuracy.

Neptune AI: Monitoring and Optimization

Neptune AI is a powerful metadata store and experiment tracking tool designed to help data scientists and machine learning engineers manage their experiments effectively. Key features include:

• Real-time experiment tracking
• Performance monitoring and visualization
• Version control for datasets and models
• Collaboration tools for team-based projects

In the context of our LLaMA 3 fine-tuning project, Neptune AI will play a crucial role in:

• Tracking the progress of our fine-tuning experiments
• Monitoring key performance metrics

Fine-Tuning LLaMA 3 with Neptune AI Monitoring

Let’s walk through the process of fine-tuning LLaMA 3 on the Finance Alpaca dataset while using Neptune AI for monitoring:

Step 1: Setting the Stage

First, we’re setting up our workspace by installing necessary packages like Unsloth (for optimized training), Xformers (for improved attention mechanisms), and Neptune (for monitoring). We’re also importing required libraries and setting up our Neptune project. For newcomers, Log in to Neptune AI -> go to the dashboard -> click “Create Project” and follow the prompts -> navigate to account settings to generate and copy your API token -> find your Project ID in the project overview or settings.

# Install necessary packages
%%capture
# Installs Unsloth, Xformers (Flash Attention) and all other packages!
!pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
!pip install --no-deps xformers "trl<0.9.0" peft accelerate bitsandbytes
!pip install neptune

from unsloth import FastLanguageModel
import torch
import neptune



import os
os.environ["NEPTUNE_API_TOKEN"] = "YOUR_TOKEN"
os.environ["NEPTUNE_PROJECT"] = "WORKSPACE/PROJECT"

# Set up Neptune
run = neptune.init_run()
# Trainer setup
from trl import SFTTrainer
from transformers import TrainingArguments
from unsloth import is_bfloat16_supported

Step 2: Preparing for Training

Next, we’re configuring our training parameters. We’re using the SFTTrainer from the trl library, which is great for fine-tuning language models. We’re setting batch sizes, learning rates, and other hyperparameters to optimize our training process.

training_args = TrainingArguments(
    per_device_train_batch_size=2,
    gradient_accumulation_steps=8,
    warmup_steps=20,
    max_steps=120,
    learning_rate=5e-5,
    fp16=not is_bfloat16_supported(),
    bf16=is_bfloat16_supported(),
    logging_steps=1,
    optim="adamw_8bit",
    weight_decay=0.01,
    lr_scheduler_type="linear",
    seed=3407,
    output_dir="outputs",
)

Step 3: Loading and Preparing the Model

We’re using the llama3 model as our starting point. We’re loading it with some optimizations for memory efficiency and speed, and then applying some Parameter-Efficient Fine-Tuning (PEFT) techniques to make our training more efficient.

# Load and prepare the model
max_seq_length = 2048
dtype = None
load_in_4bit = True

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="unsloth/llama-3-8b-bnb-4bit",
    max_seq_length=max_seq_length,
    dtype=dtype,
    load_in_4bit=load_in_4bit,
)

model = FastLanguageModel.get_peft_model(
    model,
    r=16,
    target_modules=[
        "q_proj", "k_proj", "v_proj", "o_proj",
        "gate_proj", "up_proj", "down_proj"
    ],
    lora_alpha=16,
    lora_dropout=0,
    bias="none",
    use_gradient_checkpointing="unsloth",
    random_state=3407,
    use_rslora=False,
    loftq_config=None,
)

Step 4: Preparing the Dataset

We’re using the Finance Alpaca dataset, which is perfect for our financial Q&A system. We’re formatting the data to match our model’s input requirements and splitting it into training and evaluation sets.

 Define the prompt format and function to format dataset
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{}

### Input:
{}


### Response:
{}"""

EOS_TOKEN = tokenizer.eos_token
def formatting_prompts_func(examples):
    instructions = examples["instruction"]
    inputs = examples["input"]
    outputs = examples["output"]
    texts = [alpaca_prompt.format(instruction, input, output) + EOS_TOKEN for instruction, input, output in zip(instructions, inputs, outputs)]
    return {"text": texts}



# Load and format the dataset
from datasets import load_dataset
dataset = load_dataset("gbharti/finance-alpaca", split="train")
dataset = dataset.map(formatting_prompts_func, batched=True)

# Split dataset into training and validation
train_dataset = dataset.select(range(int(0.9 * len(dataset))))

Step 5: Training with Neptune Monitoring

Now for the exciting part! We’re training our model while logging key metrics to Neptune. This allows us to keep track of our model’s progress in real time.

# Initialize the trainer
trainer = SFTTrainer(
    model=model,
    tokenizer=tokenizer,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    dataset_text_field="text",
    max_seq_length=max_seq_length,
    dataset_num_proc=2,
    args=training_args,
)

# Training with Neptune logging
for step in range(training_args.max_steps):
    trainer_stats = trainer.train(resume_from_checkpoint=False)


    # Log training metrics and losses to Neptune
    run["train/loss"].append(trainer_stats.training_loss)
    learning_rate = trainer.optimizer.param_groups[0]['lr']
    run["train/learning_rate"].append(learning_rate)

# Stop Neptune run
run.stop()

Step 6: Testing Our Fine-Tuned Model

Finally, we’re putting our newly trained model to the test! We’re using it to generate a response to a financial question, demonstrating how it can now handle specialized financial queries.

FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
    alpaca_prompt.format(
        "Does bull/bear market actually make a difference?", # instruction
        "", # input
        "", # output - leave this blank for generation!
    )
], return_tensors = "pt").to("cuda")

from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)

Output:

By following this process and using Neptune AI for monitoring, we’ve successfully fine-tuned LLaMA 3 on financial data, creating a powerful tool for answering finance-related questions. The real-time monitoring allows us to track the model’s progress, make necessary adjustments, and ensure we’re getting the best possible results from our fine-tuning process.

Visualizing Results with Neptune AI

Neptune AI provides powerful visualization tools to help us understand our model’s performance:

1. Gradient Norm (left chart):

• The graph shows the gradient norm stabilizing over time, indicating the model is converging.
• Initial spikes suggest large parameter updates early in training.
• The curve flattens out around step 50, suggesting the model is reaching a more stable state.

2. Learning Rate (middle chart):

• We see a linear decay in the learning rate from about 5e-5 to 0.
• This learning rate schedule helps fine-tune the model more precisely as training progresses.
• The gradual decrease allows for larger updates early on and more refined adjustments later.

3. Training Loss (right chart):

• The loss curve shows a general downward trend, indicating the model is improving.
• There’s some noise in the loss, which is normal during training.
• The purple and brown lines represent different runs or model variations, allowing for easy comparison.

These visualizations offer valuable insights:

• We can confirm the model is learning effectively.
• The stability of later training steps suggests we might be approaching convergence.
• Comparing multiple runs helps identify the best-performing model configuration.

Conclusion

The fusion of LLaMA 3’s advanced language understanding capabilities, the specialized knowledge from the Finance Alpaca dataset, and the robust monitoring provided by Neptune AI represents a significant leap forward in the realm of financial question-answering systems. Through this innovative approach, we’ve demonstrated how cutting-edge AI technologies can be harnessed to create more accurate, efficient, and context-aware tools for financial analysis and decision-making. Happy Fine Tuning!