LLaMA 2 New Open Source Large Language Model with 32K Context Window

Step-by-Step Tutorial: Extending Context Window of Large Language Models with LLaMA 2

1. Introduction to LLaMA 2:

• LLaMA 2 is an open-source large language model with a 32K context window released by together.ai.
• The model, LLaMA 27b 32k, is designed for tasks like multi-document understanding, summarization, and question answering.

2. Understanding the Model:

• LLaMA 27b 32k is built using position interpolation and data optimization techniques by together AI.
• The model allows for up to three times faster inference and fine-tuning with a 32K context window.

3. Evaluation and Comparison:

• Evaluate the model's performance by comparing LLaMA 27b with LLaMA 27b 32k using metrics like average score.
• The evaluation chart shows that LLaMA 27b 32k achieves comparable quality to the original model.

4. Fine-Tuning Techniques:

• Fine-tune the model for specific tasks like long context QA and book summarization.
• Access the fine-tuning techniques on their GitHub repository for applying on custom datasets.

5. Implementing Long Context QA:

• Input questions, answers, and key documents for the model to improve accuracy by up to 15 points.
• Fine-tune LLaMA 27b 32k using tasks like book summarization to produce highly informative summaries.

6. Efficiency Improvements:

• Update the inference and training stack for greater efficiency using flash attention 2 and other optimizations.
• Achieve up to three times improvement in inference and training throughput with the updated stack.

7. Accessing the Model:

• Visit the hugging face page for LLaMA 2 to access the model card for inferencing.
• Use the provided code for inferencing, ensuring you have sufficient GPU resources based on the selected model.

8. Positional Interpolation Technique:

• Learn about positional interpolation, a method used to extend the context window of large language models.
• Positional interpolation allows for increasing the context window size without extensive fine-tuning, leading to stable and reliable results.

9. Future Developments:

• Stay updated on advancements in the open-source AI ecosystem for models capable of handling larger context windows.
• Keep an eye on further developments in the space over the next few months.

By following these steps, you can understand, evaluate, fine-tune, and implement the LLaMA 2 model with a 32K context window for various language processing tasks.