gemma-1.1-7b-it
Maintainer: google
198
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The gemma-1.1-7b-it is an instruction-tuned version of the Gemma 7B large language model from Google. It is part of the Gemma family of models, which also includes the gemma-1.1-2b-it, gemma-7b, and gemma-2b models. The Gemma models are lightweight, state-of-the-art open models built using the same research and technology as Google's Gemini models. They are text-to-text, decoder-only language models available in English with open weights, pre-trained variants, and instruction-tuned variants.
Model inputs and outputs
Inputs
- Text string: This could be a question, prompt, or document that the model will generate text in response to.
Outputs
- Generated text: The model will output English-language text in response to the input, such as an answer to a question or a summary of a document.
Capabilities
The gemma-1.1-7b-it model is well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. Its relatively small size compared to other large language models makes it possible to deploy it in environments with limited resources like a laptop or desktop.
What can I use it for?
The Gemma family of models can be used for a wide range of applications across different industries and domains. Some potential use cases include:
- Content Creation: Generate creative text formats like poems, scripts, code, marketing copy, and email drafts.
- Chatbots and Conversational AI: Power conversational interfaces for customer service, virtual assistants, or interactive applications.
- Text Summarization: Create concise summaries of text corpora, research papers, or reports.
- NLP Research: Serve as a foundation for researchers to experiment with NLP techniques, develop algorithms, and contribute to the advancement of the field.
- Language Learning Tools: Support interactive language learning experiences, aiding in grammar correction or providing writing practice.
- Knowledge Exploration: Assist researchers in exploring large bodies of text by generating summaries or answering questions about specific topics.
Things to try
One interesting aspect of the gemma-1.1-7b-it model is its use of a chat template for conversational use cases. The model expects the input to be formatted with specific delimiters, such as <start_of_turn> and <end_of_turn>, to indicate the different parts of a conversation. This can help maintain a coherent flow and context when interacting with the model over multiple turns.
Another notable feature is the model's ability to handle different precision levels, including torch.float16, torch.bfloat16, and quantized versions using bitsandbytes. This flexibility allows users to balance performance and efficiency based on their hardware and resource constraints.
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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