Deploy Your Private Llama 2 Model to Production with RunPod
Interested in Llama 2 but wondering how to deploy one privately behind an API? I’ve got you covered!
In this tutorial, you’ll learn the steps to deploy your very own Llama 2 instance and set it up for private use using the RunPod cloud platform.
You’ll learn how to create an instance, deploy the Llama 2 model, and interact with it using a simple REST API or text generation client library. Let’s get started!
Llama 2 is the latest LLM offering from Meta AI! This cutting-edge language model comes with an expanded context window of 4096 tokens and an impressive 2T token dataset, surpassing its predecessor, Llama 1, in various aspects. The best part? Llama 2 is free for commercial use (with restrictions). Packed with pre-trained and fine-tuned LLMs ranging from 7 billion to 70 billion parameters, these models are set to outperform existing open-source chat models on a wide range of benchmarks. Here’s an overview of the models available in Llama 2: https://huggingface.co/meta-llama
Llama 2 comes in two primary versions — the base model and Llama-2-Chat — optimized for dialogue use cases.
But how good is Llama 2? Looking at the HuggingFace Open LLM Leaderboard, looks like Llama 2 (and modified versions of it) takes the top spots.
While many sources claim that Llama 2 is Open Source, the license is not considered Open Source by the Open Source Definition3. You can read more about it here: https://blog.opensource.org/metas-llama-2-license-is-not-open-source/
Let’s start by installing the required dependencies:
!pip install -Uqqq pip --progress-bar off
!pip install -qqq runpod==0.10.0 --progress-bar off
!pip install -qqq text-generation==0.6.0 --progress-bar off
!pip install -qqq requests==2.31.0 --progress-bar off
And import the required libraries:
from text_generation import Client
Text Generation Inference
The text generation inference library provides Rust, Python, and gRPC server that is behind Hugging Chat, the Inference API, and Inference Endpoint at HuggingFace. It offers an array of features, including Tensor Parallelism for accelerated inference on multiple GPUs, Token streaming with Server-Sent Events (SSE), and continuous batching for enhanced throughput. Furthermore, it boasts optimized transformers code with flash-attention and Paged Attention for efficient inference across popular architectures. With quantization using bitsandbytes and GPT-Q, safetensors weight loading, and logits warping, you have the tools to deploy the most popular Large Language Models as a simple Docker container.
We’ll use the library to deploy Llama 2 on RunPod. It will support a simple REST API that we can use to interact with the model.
Create the Pod
When it comes to deploying an LLM, you have three main options to consider: the DIY approach, hiring someone to do it for you or renting the machine(s) for hosting while retaining some control. RunPod falls into the third category, providing an easy and convenient solution to choose and deploy your LLM. Of course, you can explore other options as well, but I’ve personally tried and found RunPod to be effective for my needs.