Hey devs 👋
There’s a lot happening in the AI space, and one name that recently caught my attention is Gemma — Google’s open-source family of lightweight, state-of-the-art generative AI models.
As someone who’s been experimenting with AI tools lately (mostly ChatGPT, some Hugging Face models), I wanted to try out what Google brought to the table — and I wasn’t disappointed.
So here’s a quick breakdown of what Gemma is, why it matters, how it compares to other models like Gemini, and how developers like us can get started.
A Quick Recap of Gemma's Success:
Before diving into Gemma let us take a look back and find what's the pressessor was capable of,
Open Source & Accessible: This was a key point. Making the model weights freely available to researchers, developers, and hobbyists to experiment, adapt, and contribute to the Gemma ecosystem.
Competitive Performance: Gemma had a strong performance across various benchmarks, often rivaling larger models in market and closed-source models in specific tasks.
Variety of Sizes: Gemma came in different sizes (e.g., 2B, 7B) allowing users to choose the right balance between performance and computational cost.
Pre-trained and Instruction-tuned Versions: Google provided both pre-trained models and instruction-tuned versions, catering to different use cases. The instruction-tuned models, often named with it were ready for conversational applications right out of the box.
Responsible AI Focus: Google emphasized responsible AI development, incorporating safeguards and transparency around the model's capabilities and limitations.
Gemma Variants:
There are two main sizes of the Gemma model family:
Gemma 2B - This has 2 billion parameters. It's available in both pretrained and instruction-tuned formats. Ideal for lightweight use cases and local development.
Gemma 7B - With 7 billion parameters, this version offers more power while still being fairly lightweight.
Both versions also come in instruction-tuned variants:
Gemma 2B-it - Fine-tuned for tasks like chatbots and QA.
Gemma 7B-it - Fine-tuned for more complex reasoning tasks.
You can access Gemma models via Hugging Face, Kaggle, Google Cloud Vertex AI, or Colab.
Gemini or Gemma
A very and short comparison:
Gemma is open-source and optimized for local or lightweight applications. Great for developers, hobbyists, and researchers.
Gemini is Google's proprietary model suite (formerly Bard). It's more powerful, designed for enterprise-grade performance, and includes tools like Gemini Advanced, Gemini Nano (for Android), and Gemini Ultra.
In short:
If you want full control and custom experiments: Use Gemma.
If you want enterprise-level performance with built-in tools: Use Gemini.
How to use?
You can run Gemma on:
Your local machine (with good enough RAM/GPU)
Google Colab (free-tier is enough to get started)
Hugging Face Spaces or directly via transformers
A simple example from python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2b")
While ChatGPT and Gemini 1.5 are powerful and ready to use, open source models like Gemma are where dev creativity thrives. You can tune it, host it, embed it, and even build something weird (but awesome).
If you're a developer curious about building with AI without relying fully on APIs, give Gemma a shot.
Signing off,Jigin – Always exploring, whether it’s code, coffee, or open AI models.
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