Leverage open models like Gemma 2 on GKE with LangChain

AI Chatbots Dev Ops

Hugging Face Landing Page

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Hugging Face

The AI community building the future. The platform where the machine learning community collaborates on models, datasets, and applications.

To be able to use the Gemma 2 model, you first need a Hugging Face account. Start by creating one if you don't already have one, and create a token key with read permissions from your settings page. Make sure to note down the token value, which we'll need in a bit.

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Google Cloud Monitoring Landing Page

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Google Cloud Monitoring

Gain visibility into the performance, uptime, and overall health of cloud-powered apps on Google Cloud and other cloud or on-premises environments.

Monitoring: Use Cloud Monitoring and Cloud Logging to track the performance of both Gemma and your LangChain application. Look for error rates, latency, and resource utilization.

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Google Gemma Landing Page
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Google Gemma

Introducing Gemma, a family of open-source, lightweight language models. Discover quickstart guides, benchmarks, train and deploy on Google Cloud, and join the community to advance AI research.
Pricing:
- Open Source
In my previous posts, we explored how LangChain simplifies AI application development and how to deploy Gemini-powered LangChain applications on GKE. Now, let's take a look at a slightly different approach: running your own instance of Gemma, Google's open large language model, directly within your GKE cluster and integrating it with LangChain.

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