In today’s rapidly advancing AI landscape, the need for specialized language models is more crucial than ever. This blog post will guide you through the process of training a finance model using the **Llama3-8B** architecture based on the paper Instruction Pre-Training: Language Models are Supervised Multitask Learners. We will explore the framework of Instruction Pre-Training that scales the pre-training of language models using instruction-response pairs generated by an efficient instruction synthesizer. Let’s dive in!

Understanding the Concept

Imagine you’re training a parrot to not just repeat words, but to understand and respond based on specific requests. In this analogy, the parrot is our language model, and the instructions we provide act like the commands that guide it to learn effectively. Instruction Pre-Training helps refine this learning process by offering diverse contexts (or instructions) under which the model can reply (response). This structured approach makes it possible for the Llama3-8B model to outperform other pre-training methods in adapting to specific domains such as finance.

Steps to Train Your Finance Model

• Clone the Repository: First, obtain the relevant code files needed for the training process by cloning the LMOps repository.

  git clone https://github.com/microsoft/LMOps

  Then, navigate into the directory:

  cd LMOps/adaptllm

• Install Dependencies: You’ll need to install the required packages. Use:

  pip install -r requirements.txt

• Run the Model: Now, let’s prepare the model for input. Use the following Python script to set up and run the finance model appropriately. This example showcases querying the model for specific financial data.

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("instruction-pretrain/finance-Llama3-8B")
tokenizer = AutoTokenizer.from_pretrained("instruction-pretrain/finance-Llama3-8B")

user_input = '''Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered
Common Stock, Par Value $.01 Per Share MMM New York Stock Exchange
MMM Chicago Stock Exchange, Inc.
1.500% Notes due 2026 MMM26 New York Stock Exchange
1.750% Notes due 2030 MMM30 New York Stock Exchange
1.500% Notes due 2031 MMM31 New York Stock Exchange
Which debt securities are registered to trade on a national securities exchange under 3M's name as of Q2 of 2023?'''
inputs = tokenizer(user_input, return_tensors="pt", add_special_tokens=True).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_new_tokens=400)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(pred)

Troubleshooting Tips

If you encounter issues during the model training or querying process, consider the following troubleshooting ideas:

• Check if all dependencies are correctly installed. Sometimes, missing packages can lead to errors.
• Ensure you are using a compatible version of the model and tokenizer. Outdated models may not work with the latest libraries.
• If the model runs out of memory, consider using fewer GPUs or adjusting the batch size.
• Review the input data structure to ensure it’s formatted correctly for the model.

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Conclusion

By leveraging the Instruction Pre-Training technique, the finance model can be fine-tuned to meet specific requirements in financial datasets. This structured learning methodology not only increases the model’s response accuracy but also its versatility across various applications.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.