Gemma 7B IT

Your go-to model for enhanced text comprehension and generation.

Choose from hundreds of open-source LLMs in our model directory.
about

Created by Google, Gemma 7B IT is a standout in the Gemma family of language models. It excels in benchmarks like MMLU and HellaSwag, making it perfect for a wide range of text generation applications.

LicenseGemma
Context window(in thousands)8192

Use cases for Gemma 7B IT

  1. Sentiment Analysis: Ideal for extracting sentiment from large volumes of text data across various digital platforms.
  2. Predictive Text Modeling: Great for applications that suggest next words or phrases based on context.
  3. Automated Report Generation: Perfect for quickly creating detailed reports from complex datasets due to its high throughput.
Quality
Arena Elo1037
MMLU64.3
MT BenchN/A

Gemma 7B IT holds a score of 1,037 on the Chatbot Arena Leaderboard, ranking above Nous Hermes 2 Mixtral 7B, which has a score of 1,010.

Zephyr 7B beta

1053

Code Llama 70B Instruct

1042

Gemma 7B IT

1037

Llama 2 Chat (7B)

1037

Nous Hermes 2 Mistral 7B

1010

Performance
Throughput(output tokens per second)233
Latency(seconds to first tokens chunk received)0.26
Total Response Time(seconds to output 100 tokens)0.7

This model boasts fast throughput and low latency, making it suitable for time-sensitive tasks, though it may not be the best choice for data-heavy operations.

What's Twitter saying?

  • Prompting Guide for LLM: The new Gemma 7B Instruct prompting guide is live, showcasing how to effectively prompt the model for tasks like chain-of-thought reasoning. More prompt ideas and tasks, including few-shot, mathematics, and code generation, will be added soon. (Source: @omarsar0)
  • Google's New LLM: Google's lightweight LLM, Gemma, outperforms both Llama-2 and Mistral at the 7 billion parameter level. Detailed steps for deploying it using the Hugging Face API are available via the provided link. (Source: @dify_ai)
  • Performance Improvements: Gemma's performance on Ollama 0.1.27 shows significant improvements, with the Gemma 2B and 7B models delivering faster output rates compared to the previous version, especially on the M3 Max. (Source: @ivanfioravanti)

Explore Our LLM Library

Discover the power and diversity of large language models available with Telnyx. Explore the options below to find the perfect model for your project.

TRY IT OUT

Chat with an LLM

Powered by our own GPU infrastructure, select a large language model, add a prompt, and chat away. For unlimited chats, sign up for a free account on our Mission Control Portal here.

HOW IT WORKS
Sign-up to get started with the Telnyx model library
1/4
RESOURCES

Get started

Check out our helpful tools to help get you started.

  • Icon Resources EBook

    Test in the portal

    Easily browse and select your preferred model in the AI Playground.

  • Icon Resources Docs

    Explore the docs

    Don’t wait to scale, start today with our public API endpoints.

  • Icon Resources Article

    Stay up to date

    Keep an eye on our AI changelog so you don't miss a beat.

Start building your future with Telnyx AI
faqs

What is the Gemma model by Google?

Gemma is a family of lightweight, state-of-the-art open models developed by Google, designed for various text generation tasks like question answering, summarization, and reasoning. They are text-to-text, decoder-only large language models available in English. For more information, visit the Gemma model page on Hugging Face.

What are the key features of the Gemma 2B model?

The Gemma 2B model is designed for efficiency and versatility in text generation tasks, trained on a context length of 8192 tokens. It offers open weights, pre-trained variants, and instruction-tuned variants, making it suitable for deployment in environments with limited resources. For detailed features, visit the Gemma 2B model page.

Can I fine-tune the Gemma 2B model for my specific needs?

Yes, you can fine-tune Gemma 2B on your dataset. Fine-tuning scripts and notebooks are available under the examples directory of the google/gemma-7b repository. Adapt these resources for Gemma 2B by changing the model-id to google/gemma-2b. For the original resources, visit the google/gemma-7b repository.

What datasets were used to train the Gemma models?

Gemma models were trained on a dataset totaling 6 trillion tokens, comprising web documents, code, and mathematical text to ensure a broad understanding of language, logic, and information. This diverse dataset enables Gemma models to perform a wide range of text generation tasks effectively.

What are the limitations of the Gemma 2B model?

The Gemma 2B model, while state-of-the-art, has limitations related to the quality and diversity of its training data, complexity of tasks, language ambiguity, factual accuracy, and ethical considerations. Users should be aware of these limitations and consider them when using the model for specific applications.

How can I contribute to the Gemma model project?

While the Gemma model project is developed by Google, the community can contribute by providing feedback, reporting issues, and sharing insights on the model's performance and applications through the Hugging Face community platform. Engage with the Gemma model community here.

Where can I find technical documentation and further resources on the Gemma models?

For in-depth technical documentation, usage examples, and further resources on the Gemma models, visit the Gemma model page on Hugging Face. Additionally, you can explore the Gemma Technical Report, the Responsible Generative AI Toolkit, and the Gemma models on Vertex Model Garden for more detailed information.

How does Google ensure the ethical use of the Gemma models?

Google has conducted structured evaluations, internal red-teaming, and implemented CSAM and sensitive data filtering to ensure the Gemma models meet internal policies for ethics and safety. Additionally, Google provides guidelines for responsible use and encourages developers to implement content safety safeguards. For more information, refer to the Responsible Generative AI Toolkit.