Gemma 7B IT
Your go-to model for enhanced text comprehension and generation.
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.
| License | Gemma |
|---|---|
| Context window(in thousands) | 8192 |
Use cases for Gemma 7B IT
- Sentiment Analysis: Ideal for extracting sentiment from large volumes of text data across various digital platforms.
- Predictive Text Modeling: Great for applications that suggest next words or phrases based on context.
- Automated Report Generation: Perfect for quickly creating detailed reports from complex datasets due to its high throughput.
| Arena Elo | 1038 |
|---|---|
| MMLU | 64.3 |
| MT Bench | N/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.
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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)
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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.