Activefastllm Open Source

Gemma 2 9B

by Google· Released June 2024· Cutoff June 2024

Gemma 2 9B is a lightweight, open-source language model from Google, designed for efficient text generation and understanding. It balances performance and accessibility, making it suitable for a wide range of NLP tasks with a 9 billion parameter count.

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

8192 tokens

Max output

8192 tokens

Modalities

text

Parameters

License

Apache-2.0

Capabilities

Text GenerationCode GenerationSummarizationQuestion AnsweringInstruction FollowingReasoning

Best For

Efficient text generation and understanding tasks with a lightweight, open-source model.

Strengths

Open-source and freely available
Efficient inference with 9B parameters
Strong performance on benchmarks relative to size
Supports multiple languages

Limitations

Smaller context window (8K tokens)
No multimodal capabilities
May not match larger models on complex reasoning
Limited to text-only tasks

Use Cases

Chatbots and virtual assistants

Content generation and summarization

Code assistance and generation

Educational tools and tutoring

Research and experimentation

Customer support automation

Data extraction and analysis

Improvements Over Previous Model

Introduced as a new model family, not an incremental update
Improved efficiency and performance over Gemma 1 7B
Larger parameter count (9B vs 7B)
Better reasoning and coding capabilities
Enhanced instruction following

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Activefastllm Open Source

Gemma 2 9B

by Google· Released June 2024· Cutoff June 2024

Official Site API Docs 🤗 Hugging Face 📄 Research Paper

Input cost

Free (open source)

Output cost

Free (open source)

Context window

8192 tokens

Max output

8192 tokens

Modalities

text

Parameters

License

Apache-2.0

Capabilities

Text GenerationCode GenerationSummarizationQuestion AnsweringInstruction FollowingReasoning

Best For

Efficient text generation and understanding tasks with a lightweight, open-source model.

Strengths

Open-source and freely available
Efficient inference with 9B parameters
Strong performance on benchmarks relative to size
Supports multiple languages

Limitations

Smaller context window (8K tokens)
No multimodal capabilities
May not match larger models on complex reasoning
Limited to text-only tasks

Use Cases

Chatbots and virtual assistants

Content generation and summarization

Code assistance and generation

Educational tools and tutoring

Research and experimentation

Customer support automation

Data extraction and analysis

Improvements Over Previous Model

Introduced as a new model family, not an incremental update
Improved efficiency and performance over Gemma 1 7B
Larger parameter count (9B vs 7B)
Better reasoning and coding capabilities
Enhanced instruction following

Back to all models