Gemma 2.1 27B-it

Overview

Google's Gemma 2.1 27B-it is a high-performance language model from the Gemma family, designed to handle a wide range of text generation tasks, including question answering, summarization, and reasoning.

Scorecard

Built on advanced TPU hardware, this model offers a robust and efficient solution for developers and researchers.

Gemma 2.1 27B-it Free Chat 💬

Test your prompt with Gemma 2.1 27B-it for free! _{3 messages a day}

Architecture 🏗️

The Gemma 2.1 27B-it model uses a state-of- Google's latest TPU hardware. This setup allows the model to handle extensive computations efficiently, making it suitable for various complex tasks. The architecture includes:

• 27 billion parameters: Ensuring high performance and accuracy.
• 200k token context window: Allowing the model to process and generate long-form content effectively.

Performance 🏎️

Gemma 2.1 27B-it excels in various performance benchmarks, particularly in multilingual understanding and reasoning tasks. The model's high MMLU score of 88.7% demonstrates its capability in handling diverse and complex queries with precision.

Pricing 💵

Token Pricing

• Input Cost: $3 per 1M tokens
• Output Cost: $15 per 1M tokens

Example Cost Calculation

For a task requiring 500k input tokens and generating 1M output tokens, the cost would be:

• Input Cost: 500k tokens * $3/1M tokens = $1.50
• Output Cost: 1M tokens * $15/1M tokens = $15.00
• Total Cost: $1.50 (input) + $15.00 (output) = $16.50

Use Cases 🗂️

Gemma 2.1 27B-it is versatile and can be employed in various applications, including:

• Customer Support: Automating responses and providing accurate information.
• Content Creation: Assisting in generating articles, blog posts, and marketing content.
• Data Analysis: Summarizing large datasets and extracting key insights.

Customization

Developers can fine-tune the model on specific datasets to tailor its responses to particular domains or tasks, enhancing its relevance and accuracy for specialized applications.

Comparison 📊

Compared to other models, Gemma 2.1 27B-it offers a competitive edge with its large context window and high parameter count. For instance, GPT-4o Mini offers a 128k context length with input costs at $0.15 and output at $0.60, whereas Gemma 2.1 27B-it provides a 200k context window, making it more suitable for tasks requiring extensive context.

Conclusion

Gemma 2.1 27B-it stands out as a robust and versatile language model, offering high performance in a variety of tasks. Its large context window and extensive parameter count make it a valuable tool for developers and researchers looking for a reliable and powerful LLM.

Excerpt

Gemma 2.1 27B-it features a 200k context length with input costs at $3 and output at $15 per 1M tokens.