Activefastllm Open Source

Gemma 2 2B

by Google· Released June 2024· Cutoff May 2024

Gemma 2 2B is a lightweight, open-source language model from Google, designed for efficient deployment on resource-constrained devices. It is part of the Gemma 2 family, offering strong performance for its size with a focus on accessibility and customization.

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

2.6B

License

Apache-2.0

Capabilities

Text GenerationCode GenerationInstruction FollowingSummarizationQuestion AnsweringReasoning

Best For

On-device and edge applications requiring a capable, small language model with low latency.

Strengths

Strong performance for its size (2.6B parameters)
Efficient inference on CPUs and mobile devices
Open-source with permissive license (Apache 2.0)
Good multilingual support

Limitations

Smaller context window (8K tokens) compared to larger models
Limited reasoning and factual accuracy compared to larger models
No native multimodal capabilities (text-only)
May require fine-tuning for specialized tasks

Use Cases

On-device chatbots and virtual assistants

Text summarization for mobile apps

Code completion in lightweight IDEs

Educational tools and tutoring

Content generation for social media

Data extraction and classification

Prototyping and experimentation

Improvements Over Previous Model

Introduced as a new model family, not an update to a predecessor
Smaller and more efficient than Gemma 1 2B
Improved performance on benchmarks like MMLU and HellaSwag
Better instruction following and safety alignment
Supports more languages in tokenizer
Lower memory footprint for inference

Back to all models

Activefastllm Open Source

Gemma 2 2B

by Google· Released June 2024· Cutoff May 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

2.6B

License

Apache-2.0

Capabilities

Text GenerationCode GenerationInstruction FollowingSummarizationQuestion AnsweringReasoning

Best For

On-device and edge applications requiring a capable, small language model with low latency.

Strengths

Strong performance for its size (2.6B parameters)
Efficient inference on CPUs and mobile devices
Open-source with permissive license (Apache 2.0)
Good multilingual support

Limitations

Smaller context window (8K tokens) compared to larger models
Limited reasoning and factual accuracy compared to larger models
No native multimodal capabilities (text-only)
May require fine-tuning for specialized tasks

Use Cases

On-device chatbots and virtual assistants

Text summarization for mobile apps

Code completion in lightweight IDEs

Educational tools and tutoring

Content generation for social media

Data extraction and classification

Prototyping and experimentation

Improvements Over Previous Model

Introduced as a new model family, not an update to a predecessor
Smaller and more efficient than Gemma 1 2B
Improved performance on benchmarks like MMLU and HellaSwag
Better instruction following and safety alignment
Supports more languages in tokenizer
Lower memory footprint for inference

Back to all models