Do you want to breathe domain-specific knowledge into large language models (LLMs)? If your answer is yes, then you’re in the right place! This blog will guide you through the process of adapting LLMs like LLaMA using domain-specific data via continual pre-training.

Understanding the Concept

First, let’s break it down with an analogy. Imagine teaching a child (representing our model) about different hobbies (our domains). Initially, the child knows only the basics of everything. Now, if you want the child to excel in painting (biomedicine, finance, law), instead of just handing them a general art book (generic data), you hand them specialized art books about various painting styles and famous artists (domain-specific corpora). This process deepens their understanding in a way that mirrors human learning, enhancing their ability to engage in a conversation about painting. Similarly, adapting LLMs involves transforming a broad knowledge base into domain-specific expertise.

Steps to Adapt Your LLM

• Access the Base Model: Start with the LLaMA-1-7B base model.
• Continual Pre-Training: Perform pre-training on your selected domain-specific corpora.
• Transform the Data: Convert your pre-training corpora into a format resembling reading comprehension texts.
• Evaluate Performance: Compare your domain-adapted model’s performance to existing large models like BloombergGPT-50B.

Testing and Running the Model

To run the adapted LLM, you’ll need to use the Hugging Face Transformers library to pull your model and tokenizer. Here’s a simple code snippet to help you initiate a conversation with your biomedicine base model:

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-LLM")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-LLM", use_fast=False)

user_input = "Question: Which of the following is an example of monosomy? Options: - 46,XX - 47,XXX - 69,XYY - 45,X. Please provide your choice first and then provide explanations if possible."

prompt = user_input
inputs = tokenizer(prompt, return_tensors='pt', add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=2048)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)

print(f"### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}")

Troubleshooting

If you encounter issues while adapting LLMs, consider the following tips:

• Ensure your data is well-formatted for reading comprehension.
• Check the compatibility of your libraries with the current model version.
• If your model is not generating expected results, try adjusting the prompts or input sequences.
• For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Adapting large language models to specific domains via continual pre-training can significantly improve their performance and relevance in specialized tasks. By following the steps outlined above, you can transform your LLMs into powerful tools tailored for particular areas like biomedicine, finance, or law.

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.