gemma-2b-it

Maintainer: google-deepmind

Total Score

78

Last updated 5/17/2024
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Model overview

The gemma-2b-it is a 2 billion parameter instruct-tuned version of Google's Gemma language model, developed by google-deepmind. It is part of a suite of Gemma models which also includes the gemma-7b-it, gemma-7b, and other variants. These models are designed to perform a variety of natural language processing tasks with high capabilities.

Model inputs and outputs

The gemma-2b-it model takes in a text prompt and generates relevant text in response. The key input parameters include the prompt text, temperature to control randomness, top-k and top-p settings to control the diversity of the output, and maximum and minimum new tokens to generate. The output is an array of generated text.

Inputs

  • Prompt: The text prompt to generate a response from.
  • Temperature: Adjusts the randomness of the output, with higher values producing more diverse and creative text.
  • Top K: Samples from the top K most likely tokens during decoding.
  • Top P: Samples from the top P percentage of most likely tokens during decoding.
  • Max New Tokens: The maximum number of new tokens to generate.
  • Min New Tokens: The minimum number of new tokens to generate.
  • Repetition Penalty: Controls how repetitive the generated text can be.

Outputs

  • Generated Text: An array of generated text in response to the input prompt.

Capabilities

The gemma-2b-it model demonstrates strong natural language understanding and generation capabilities, allowing it to perform a wide variety of tasks such as question answering, text summarization, creative writing, and open-ended dialogue. It can generate coherent and contextually relevant text, exhibiting an understanding of language, tone, and structure.

What can I use it for?

The gemma-2b-it model could be utilized in a range of applications, such as chatbots, content generation, language translation, and task-oriented dialogue systems. Its versatility makes it suitable for use in both consumer-facing and enterprise-level applications. For example, a company could use gemma-2b-it to generate personalized product descriptions, marketing copy, or to power a customer service chatbot.

Things to try

Some interesting things to explore with the gemma-2b-it model include prompting it to write stories or poems, engaging in open-ended conversations, and experimenting with different parameter settings to see how they affect the output. The model's strong language understanding and generation capabilities make it a versatile tool for a variety of creative and practical applications.



This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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