Using Google Gemini Pro AI
- Currently, Gemini Pro is available through Google AI Studio for free with up to 60 requests per minute, making it ideal for most app development needs.
- You can also access it through the Vertex AI Gemini API for more advanced use cases, but that requires paid resources on Google Cloud.
Using the API:
- Check out the official documentation for the Vertex AI Gemini API: https://deepmind.google/technologies/gemini/
- Several helpful tutorials exist online, including:
- Getting Started with Gemini Pro on Google AI Studio: https://www.youtube.com/watch?v=HN96QDFBD0g
- Google’s Gemini Pro Model API in 8 Minutes: https://www.youtube.com/watch?v=2DyL1KP-gEc
Input and Output:
- Gemini Pro accepts multimodal prompts, meaning you can combine text and images as input.
- It outputs text or code, making it versatile for various tasks.
Here’s how you can get started with Gemini Pro:
How to Access Gemini Pro Free with Google AI Studio
For higher usage requirements, paid plans are available on Vertex AI. These offer increased quotas and advanced features like fine-tuning the model for specific tasks.
There are two main ways to access Gemini Pro:
1. Google AI Studio:
- Free access: You can use Gemini Pro for free (up to 60 requests per minute) in Google AI Studio. This is a good option for trying out the model and exploring its capabilities.
- Steps:
- Create a Google AI Studio account or sign in if you already have one.
- Select “Create Notebook” from the main menu.
- Choose “Python 3” as the runtime environment.
- Copy and paste the following code snippet into the notebook:
Python
!pip install transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
# Set the model name and path
model_name = "google/dialog-t5-xl"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
# Input your text here
text = "What is the meaning of life?"
# Encode the text
encoded_text = tokenizer(text, return_tensors="pt")
# Generate the response
generated_ids = model.generate(**encoded_text, beam_search=True)
# Decode the response
decoded_text = tokenizer.decode(generated_ids[0])
print(decoded_text)
Use code with caution. Learn more
* Replace "What is the meaning of life?" with your own text prompt.
* Run the code. This will download the necessary files and generate a response to your prompt.
Google Cloud Vertex AI (for enterprises):
- Paid access: If you need more than 60 requests per minute or want to use Gemini Pro in a production environment, you can access it through Google Cloud Vertex AI. This requires a paid account.
- Steps:
- Create a Google Cloud account or sign in if you already have one.
- Go to Vertex AI in the Cloud Console.
- Create a containerized deployment of Gemini Pro.
- Use the API to send your text prompts to the model and receive responses.
Additional resources:
- Getting Started with Gemini Pro API on Google AI Studio: https://www.youtube.com/watch?v=kvuxXL6muxw
- Google Gemini API documentation: https://huggingface.co/google/long-t5-local-large
Here’s a general workflow for using Gemini Pro:
- Define your prompt: Clearly state what you want the model to do or what information you’re looking for. Be specific and concise.
- Choose the desired output format: Gemini Pro can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Specify the format you prefer for the output.
- Submit your request: Once you’ve got your prompt and format ready, submit your request to the API.
- Receive the response: Gemini Pro will process your request and provide you with the generated output.
Gemini Pro vs. ChatGPT:
Comparing these two large language models is interesting, as both have their strengths and weaknesses:
Similarities:
- Capabilities: Both are capable of generating text, translating languages, writing different creative formats, and answering questions in an informative way.
- Accessibility: Both offer free trial versions or tiered access options.
- Multilingual: Both support several languages, although Gemini Pro boasts a wider range (38 vs. 26).
Differences:
- Focus: Gemini Pro is trained on both text and code, making it adept at code generation and understanding technical prompts. ChatGPT, on the other hand, excels in creative writing and conversational dialogue.
- Output options: While both offer text output, Gemini Pro can also generate Python code based on your prompts.
- Transparency: Gemini Pro is relatively new and information about its inner workings is limited. ChatGPT has been around longer and has a more established community with various resources available.
Which one is better Gemini AI or Chat GPT4?
The “better” model depends on your specific needs:
- Choose Gemini Pro if:
- You need both text and code generation capabilities.
- You have technical tasks involving Python code.
- You prefer a wider range of supported languages.
- Choose ChatGPT if:
- You prioritize creative writing and engaging dialogue.
- You value established community resources and transparency.
- You prefer a model familiar with casual conversational language.
Ultimately, the best way to decide is to try both and see which one suits your needs better.
