How to Leverage Open-Source LLMs in Your Project

In the previous deep dive, we explored the leading open-source LLM models: LLaMA, Falcon, Llama 2, and their chatbot counterparts: Falcon-40B-Instruct, Llama 2-Chat, and FreeWilly 2. Now, the question is: how can you integrate these impressive models into your projects?

To get varied insights, we asked the practitioners who work with LLMs daily about how to effectively utilize existing models, fine-tune and deploy them efficiently, and avoid mistakes and obstacles. You will get perspective from:

1. Edward Beeching: Co-creator of the Hugging Face Open LLMs leaderboard, a machine learning research scientist at Hugging Face;

2. Rajiv Shah, a machine learning engineer at Hugging Face;

3. Aniket Maurya, a developer advocate at Lightning AI;

4. Lianmin Zheng, a Ph.D. student at UC Berkeley, one of the contributors to Vicuna;

5. Devis Lucato, a principal architect at Microsoft at Semantic Kernel.

Highlights

Fine-tuning

When you’re thinking about fine-tuning an LLM, the most common advice is to define whether you need to fine-tune it or you can use existing models. Using an existing model may save you much time and resources.

In case fine-tuning is necessary, there are several moments you need to remember:

• It is crucial to have a deep understanding of the dataset you will use for fine-tuning including its nuances and biases.

• The most frequent problem is fitting the model on a single GPU. That’s why it’s important to use parameter-efficient fine-tuning techniques like Low-Rank Adaptation of Large Language Models (LoRA) and LLM Adapters. Also, use low precision like Brain Floating Point Format (bfp16), or 4-bit precision from QLoRA paper. The Parameter-Efficient Fine-Tuning (PEFT) package is a starting point.

• Play around with the LLM configurations like max seq length, temperature and so. Then learn why it affects you in more detail.

Advice for beginners:

• Be aware of what LLM is good for your particular task.

• Start small.

• Engage with the community to stay updated on the latest developments and best practices.

• Experiment with various techniques, hyperparameters, and datasets to gain a deeper understanding of the model's behavior and performance.

• Document your work.

Now, let’s dive deeper into the insights from each expert!