How to Use the Instruction Pre-Training Framework for Language Models

Welcome to this guide on leveraging the power of the Instruction Pre-Training framework for language models, specifically focusing on the finance model developed from Llama3-8B. This framework significantly enhances the capabilities of language models by using instruction-response pairs, setting a new standard in AI application. Let’s explore how to use this framework step by step!

Understanding Instruction Pre-Training

Think of Instruction Pre-Training as teaching a child how to answer questions about various subjects. Instead of making the child memorize facts (Vanilla Pre-training), you provide them with a story that illustrates an answer (Instruction Pre-Training). This method not only helps the child to understand better but also makes it easier for them to recall information when asked a question later. This is how instruction-response pairs augment raw data for more effective language model training.

Getting Started with the Finance-Llama3-8B Model

Below are the steps to chat with the finance-Llama3-8B model:

• Step 1: Begin by importing the necessary libraries.
• Step 2: Load the model and tokenizer:

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

• Step 3: Input your question and tokenize it:

user_input = "Use this fact to answer the question: Title of each class Trading Symbol(s) Name of each exchange on which registered..."
inputs = tokenizer(user_input, return_tensors='pt', add_special_tokens=True).input_ids.to(model.device)

• Step 4: Generate a response:

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)

Evaluating Any Huggingface Language Models

You can also evaluate various Huggingface Language Models (LMs) through domain-specific tasks using the following process:

Step-by-Step Evaluation

• Step 1: Clone the repository:

git clone https://github.com/microsoft/LMOps
cd LMOps/adaptllm
pip install -r requirements.txt

• Step 2: Set the model domain:

DOMAIN=finance

• Step 3: Specify the model name:

MODEL=instruction-pretrain/finance-Llama3-8B

• Step 4: Choose the suitable options for model parallelization:

MODEL_PARALLEL=False
N_GPU=1
add_bos_token=True

• Step 5: Execute the evaluation script:

bash scripts/inference.sh $DOMAIN $MODEL $add_bos_token $MODEL_PARALLEL $N_GPU

Troubleshooting Tips

If you encounter any challenges during implementation, consider the following solutions:

• If the model doesn’t load correctly, make sure you have the correct huggingface model path.
• Ensure all dependencies are correctly installed.
• If you face out-of-memory errors, try reducing the batch size or use model parallelization.
• Refer to the FAQ section for detailed responses regarding specific instructions.

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Conclusion

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.

By following the steps outlined above, you will be well on your way to utilizing the Instruction Pre-Training framework for your finance-related AI projects. Happy coding!