Technical Specifications

System Requirements

VRAM requirements for different quantization methods and context sizes

Gemma 1 2B

Gemma 1 2B is a lightweight, state-of-the-art open language model developed by Google, stemming from the same research and technology that underpins the Gemini family of models. This model is designed as a text-to-text, decoder-only transformer, primarily available in English, with both pre-trained and instruction-tuned variants. Its architectural design focuses on efficiency, making it suitable for deployment in environments with limited computational resources, such as laptops, desktops, or personal cloud infrastructure.

Architecturally, Gemma 1 2B incorporates several advanced components. It utilizes Multi-Query Attention (MQA) with a single key-value head, a design choice that optimizes for faster inference by sharing key and value projections across attention heads. Positional encoding is handled through Rotary Positional Embeddings (RoPE). The model's non-linear activation function is GeGLU (Gated Linear Unit), a variant of GLU that enhances expressive power. Normalization within the network is performed using RMSNorm. These elements contribute to the model's performance while maintaining a compact footprint.

The 2B variant is well-suited for a variety of text generation applications, including question answering, summarization, and reasoning tasks. The instruction-tuned versions of Gemma 1 2B are specifically refined to follow instructions effectively and engage in multi-turn conversations, making them adaptable for interactive applications like chatbots. Its compact size ensures it can operate on consumer-grade hardware, democratizing access to advanced AI capabilities for developers and researchers.

No evaluation benchmarks for Gemma 1 2B available.