Gemma 2B IT
Utilize efficient text generation for resource-constrained environment.
Developed by Google, Gemma 2B IT is a versatile language model excelling in various text-related tasks. With impressive benchmark scores like RealToxicity and BOLD, its compact size allows for easy deployment in resource-constrained settings.
| License | Gemma |
|---|---|
| Context window(in thousands) | 8192 |
Use cases for Gemma 2B IT
- Technical Documentation: Generate detailed and accurate technical documents using the model's extensive context capabilities.
- Language Translation: Translate content across multiple languages with its robust context processing.
- Educational Tools: Create intelligent tutoring systems for personalized learning experiences.
| Arena Elo | 990 |
|---|---|
| MMLU | 42.3 |
| MT Bench | N/A |
Gemma 2B IT has an Arena Elo score of 989 on the Chatbot Arena Leaderboard, ranking above DeepSeek Coder 33B Instruct but below GPT-3.5 Turbo-0613.
1038
1037
1010
1008
990
| Throughput(output tokens per second) | N/A |
|---|---|
| Latency(seconds to first tokens chunk received) | N/A |
| Total Response Time(seconds to output 100 tokens) | N/A |
As of June 19, 2024 there is no available performance data for this LLM.
What's Twitter saying?
- Better Performance than phi-2.7b: Ivan Fioravanti compared Gemma 2B with phi-2.7b and found that Gemma 2B performs better in various logic tests using Apple MLX, showcasing its superior performance and versatility. (Source: @ivanfioravanti)
- Inference Cost Reduction by 66%: Anthony Goldbloom noted that using the Gemma-2B model cut their inference costs by 66% compared to Mistral-7B, while also delivering better performance, highlighting its cost-effectiveness and efficiency. (Source: @antgoldbloom)
- Surpassing Larger Models in Math Reasoning: Gaurav Vij observed that Gemma 2B outperforms the larger Llama 13B model in math reasoning tasks due to domain-specific fine-tuning using LoRA, demonstrating its capability in specialized tasks. (Source: @Gaurav_vij137)
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What is the Gemma model by Google?
The Gemma model is a lightweight, state-of-the-art, open model developed by Google, designed for a variety of text generation tasks. It is built from the research and technology behind the Gemini models and is available in English. The Gemma family includes text-to-text, decoder-only large language models with open weights, pre-trained variants, and instruction-tuned variants. They are ideal for tasks like question answering, summarization, and reasoning, with the flexibility to be deployed on limited resources like laptops and desktops.
Can I use the Gemma model for conversational AI applications?
Yes, the Gemma model is well-suited for conversational AI applications. It includes instruction-tuned variants that can be used for creating chatbots and conversational interfaces. The model's documentation provides examples and guidelines for implementing a chat template to facilitate conversational use.
What training data was used for the Gemma model?
The Gemma models were trained on a dataset comprising 6 trillion tokens from diverse sources, including web documents, code, and mathematical text. This broad range of linguistic styles, topics, and vocabulary helps ensure the model's versatility across various text generation tasks.
How do I fine-tune the Gemma model for a specific task?
The Gemma model can be fine-tuned on custom datasets for specific tasks. The Hugging Face page provides links to example fine-tuning scripts and detailed instructions for fine-tuning on datasets like the UltraChat dataset or the English quotes dataset. These resources can help you adapt the model to your specific requirements.
Where can I find technical documentation and support for the Gemma model?
Technical documentation, including usage guidelines, code snippets for different computational setups, and links to resources like the Responsible Generative AI Toolkit and the Vertex Model Garden, are available on the Gemma model's Hugging Face page. For further support and community discussions, users can engage with the Community section or explore the provided examples and tutorials.