What is Google Gemma?
Google revealed the open AI model which has the same technology and research as Gemini AI models, a previous Google’s AI model.
The name Gemma is the Latin word that represents precious stone. Google launched Gemma on February 21, 2024, and Gamma is an open AI for developers and can be used without charge in Kaggle and Colab, an as-a-service Jupyter Notebook version.
Features of Google Gemma
Featuring two models: Gemma 2B and Gemma 7B. They are text-to-text, decoder large language models (LLMs) featuring both pretrained and instruction-tuned versions. Gemma 2B has a neural network with two billion parameters, whereas Gemma 7B has seven billion parameters.
Difference between Google Gemma and Gemini
Google Gemma and Gemini are open AI models. Google’s AI model Gemma isn’t as vast or strong as popular AI models like ChatGPT-4 which is the chat AI from open AI or Google’s Gemini Ultra and Pro which are chatbots, which include billions of parameters. Google Gemma open models are based on small, lightweight models, and can operate on laptops and desktop PCs because of higher inference speeds and reduced computing loads.
Gemma Benefits
1. Lightweight Architecture
Google Gemma open models are significantly smaller than her bigger cousins and come in two different sizes, 2B and 7B, respectively. It is therefore ideal for personal computers and even mobile devices, resulting in quicker inference times and lower processing burdens.
2. One of the best open source large language models
Google Gemma, unlike many advanced LLMs, is open source. But Gamma is the best AI for developers and researchers to experiment with it, make improvements, and contribute to its growth.
3. Instruction-tuned Variants
Despite pre-trained AI models, Google Gemma open models deliver “instruction-tuned” versions that are geared to specific tasks like summarizing and responding to inquiries. Quality and flexibility are increased for real-world applications.
Gemma capabilities
- Google Gemma open models provide support to low-latency generative AI apps such as text streaming. Running Gemma on GKE allows developers to create custom fine-tuned models in portable containers.
- Google Gemma open models are designed to operate on common AI hardware, like NVIDIA GPUs and Google Cloud TPUs.
- Gemma was trained on a variety of English-language Web text pages to expose her to different linguistic styles, themes, and terminology.
- Lastly, Gemma can learn programming language code and mathematical texts to produce code and solve code-related and mathematical queries.
Gemma with Vertex AI
Google Cloud’s Vertex AI and Google Kubernetes Engine (GKE) could both be used to run Gemma models. App developers may leverage Vertex AI to enhance Gemma for certain use cases, such as Q&A or text generation.
How are Gemma AI models different from other AI models?
1. Gemma differs significantly from other prominent open AI language models, like Google’s Gemini. Gemma is situated out for her openness and lightness. Gemini and ChatGPT are both closed models that are too heavy to run on laptops. ChatGPT and Gemini are closed, meaning developers cannot change their code in the same way as open-source Gemma does.
2. Gemma is not Google’s first AI’s OpenAI model, but it is far superior in aspects of training and functionality to previous models Bert and T5. OpenAI chat app, ChatGPT uses an advanced open AI algorithm, yet they have not revealed any open source AI language model for ChatGPT.
3. Decoders create new texts, but encoder models receive inputs and interpret context, which is mostly utilized in generative AI. Encoder models handle tasks such as text categorization and emotional tone analysis.
4. Google Gemini employs a decoder and encoder architecture to provide its multimodal functionality, which includes voice, graphics, and text in user prompts and replies.
Conclusion
After going through Google Gamma’s model, Gemma benefits, Gemma capabilities, and a comparison of Gemma with Vertex AI and Gemini.
We can say that Google Gemma’s lightweight design and open-source nature set it apart for its accessibility and adaptability, providing developers with fresh possibilities for experimentation and creativity in AI applications.
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