Google Gemini is a large language model (LLM) developed by Google. It is Google’s answer to popular, competing LLM technologies like OpenAI GPT-4 and Anthropic Claude. Gemini performs well on LLM benchmarks and incorporates novel technologies to improve computational speed, accuracy, and ability to process multi-modal inputs.
Google Gemini Pro is a full-scale version of the model, providing high performance on LLM benchmarks while offering improved computational efficiency. Google also offers Gemini Flash, a lightweight model for constrained environments, and has announced Gemini Ultra, a more advanced version of the model. This is part of an extensive series of guides about machine learning.
Here are the key features offered by Google Gemini Pro:
The platform leverages a Mixture-of-Experts (MoE) architecture, which enhances performance by activating the most relevant neural network pathways based on the input type. This design improves efficiency and allows the model to process complex tasks with greater speed and accuracy.
The architecture is rooted in Google's leading research on Transformer and MoE models, which involve dividing a large neural network into smaller "expert" networks. These experts specialize in different types of data, such as text, images, or code, and are selectively activated to handle specific inputs. This specialization enables the model to maintain high performance while being more resource-efficient during training and operation.
Gemini 1.5 Pro, the most current model in the Gemini Pro suite, features a standard 128,000 token context window, with capabilities to extend up to 1 million tokens for specific use cases. This large context window allows the model to handle extensive datasets, including lengthy text documents, large codebases, and long video or audio files.
Here are the technical details of models offered in the Gemini Pro family, as of the time of this writing. Rate limits of the models are expressed in these terms:
Gemini 1.5 Pro is a mid-size multimodal model optimized for a range of reasoning tasks. It supports code and text generation, text editing, problem-solving, recommendations, information extraction, data generation, and the creation of AI agents. It can process large datasets, including extensive video, audio, and codebases.
Model details:
Rate limits:
Gemini 1.0 Pro is an LLM designed to handle tasks such as multi-turn text and code chat, as well as code generation. It supports zero-shot, one-shot, and few-shot learning, making it versatile for various applications.
Model details:
Rate limits:
Gemini 1.0 Pro Vision is a performance-optimized multimodal model capable of handling visual-related tasks. It can generate image descriptions, identify objects in images, and provide information about places or objects depicted in images. Similar to 1.0 Pro, it supports zero-shot, one-shot, and few-shot learning.
Model details:
Rate limits:
For end-users and organizations, Gemini is available in two versions:
The standard version of Gemini is free, providing access to the 1.0 Pro model. It aids in writing, learning, and planning and is integrated with Google applications.
This version costs $19.99 per month. It uses 1.5 Pro, with a context window of 1 million tokens. It comes with 2 TB of Google One storage and can be integrated into Gmail and Google Docs. It can also run and edit Python code.
For users of the Gemini 1.0 Pro model, there are two pricing tiers available: Free and Pay-as-you-go. Please note rate limits listed in the model technical specifications above.
Free Tier:
Pay-as-you-go Tier:
1.5 Pro is also available with Free and Pay-as-you-go tiers.
Free Tier:
Pay-as-you-go Tier:
To integrate Google Gemini Pro with your applications, you need to set up your development environment. You can run the setup in Google Colab, which allows you to execute the notebook directly in your browser without additional configuration.
Alternatively, you can set up your local environment to meet the following requirements:
Once you have the basic requirements, install the Python SDK for the Gemini API, which is included in the
google-generativeaipip install -q -U google-generativeai
Next, import the necessary packages for your project:
import pathlib import textwrap import google.generativeai as genai from IPython.display import display, Markdown
Before using the Gemini API, obtain an API key from Google AI Studio. In Google Colab, add the key to the secrets manager and name it:
GOOGLE_API_KEYPass this key to the SDK using one of the following methods:
from google.colab import userdata GOOGLE_API_KEY = userdata.get('GOOGLE_API_KEY') genai.configure(api_key=GOOGLE_API_KEY)
With the API key set up, you can now list the available Gemini models using the
list_modelsfor model in genai.list_models(): if 'generateContent' in model.supported_generation_methods: print(model.name)
To generate text responses from text inputs, use this model:
gemini-proGenerativeModel generate_content model = genai.GenerativeModel('gemini-pro') response = model.generate_content("What is generative AI?")
A simple way to handle the response is converting it to Markdown:
to_markdown(response.text)
The Gemini API supports many other options, including multi-turn chat and multimodal input, depending on the model's capabilities. The Gemini models support text and images as input, with text as the output. For more details, see the official documentation.
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