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Google DeepMind Advanced

Gemma 2 27B IT

Gemma 2 27B IT is Google's instruction-tuned 27B text checkpoint with open weights under the Gemma terms.

General Purpose Language ModelText Free
In plain English

What is this model and why does it matter?

Gemma 2 27B IT is Google's instruction-tuned 27B Gemma 2 text model for question answering, summarization and reasoning.

Self-hosted assistantsSummarizationQuestion answeringReasoningFine-tuningResearch
Model overview

Gemma 2 27B IT: features, use cases and important details

Gemma 2 27B IT is the instruction-tuned 27B member of Google’s Gemma 2 family.

Verified model facts

Google’s Gemma 2 documentation describes text input and text output, English-focused training and open weights under the Gemma Terms. The 9B and 27B sizes were released on June 27, 2024.

Best fit

It is suited to self-hosted assistants, summarization, question answering, reasoning experiments and custom fine-tuning.

Limitations

The context window is modest compared with newer model families, outputs may be inaccurate, and the Gemma Terms should be reviewed before deployment.

Get started

How to use this model

  1. Accept the Gemma terms.
  2. Download the official gemma-2-27b-it checkpoint.
  3. Use a supported Transformers or Gemma runtime.
  4. Apply the instruction/chat format.
  5. Evaluate quality and safety on your workload.
Copy and try

Example prompts

  • Summarize this document and list the key decisions.
  • Explain this concept in plain English.
  • Compare these two technical approaches.
Capabilities

What it can do

  • Instruction following
  • Text generation
  • Question answering
  • Summarization
  • Reasoning
Best for

Practical use cases

  • Local assistants
  • Document summarization
  • Education
  • Research
  • Custom fine-tuning
Pricing

What does it cost?

Open-weight checkpoint under the Gemma terms; no single provider token price applies to the downloadable model.

Simple summaryWeights are downloadable under Google's Gemma terms. Hosting cost depends on selected hardware or cloud infrastructure.

What stands out

  • Official Google open weights
  • Instruction-tuned checkpoint
  • 27B option for different quality and hardware tradeoffs

Things to consider

  • 8K context is short by current standards
  • English-focused
  • Gemma terms are not a permissive OSI license
Limitations

Important restrictions and trade-offs

  • Can generate inaccurate output
  • Static model with no live knowledge
  • Requires appropriate hardware and license review
SimplifyAITools verdict

Our editorial take

A verified Gemma 2 27B instruction checkpoint that remains useful for self-hosted text applications, though newer Gemma generations offer longer context and multimodality.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗
  3. Open source 3 ↗
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