Gemma 1 2B
Parameters
2B
Context Length
8K
Modality
Text
Architecture
Dense
License
Gemma Terms of Use
Release Date
21 Feb 2024
Knowledge Cutoff
-
API Pricing (per 1M)
Input: $0.10 · Output: $0.10
System Requirements
VRAM requirements for different quantization methods and context sizes
1,024 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
8,192 tokens
Consumer
1x RTX 4090
24GB VRAM
Datacenter
1x NVIDIA A100
80GB VRAM
Apple Silicon
1x Apple M3 Max
128GB VRAM
Architecture Diagram
Evaluation Benchmarks
Rank
#200
| Benchmark | Score | Rank |
|---|---|---|
General Text | 1093 | 156 |
Rankings
Overall Rank
#200
Coding Rank
-
About Gemma 1 2B
Gemma 1 2B is a lightweight open language model by Google designed for on-device conversational tasks, summarization, and local NLP prototyping. It features Multi-Query Attention for low-latency inference on consumer-grade hardware.
Technical Specifications
Attention
Attention Structure
Multi-Query Attention
Attention Heads
16
Key-Value Heads
1
Attention Head Dimension
-
Position Embedding
ROPE
RoPE Theta
-
Sliding Window Attention
-
Sliding Window Size
-
Sliding Window Ratio
-
Linear Attention
-
Linear Attention Ratio
-
Normalization
RMS Normalization
Activation Function
-
Dimensions
Hidden Dimension Size
2,048
Number of Layers
18
FFN Intermediate Size (Dense)
-
Multi-Token Prediction Heads
-
Tokenizer
Vocabulary Size
-
About Gemma 1
Gemma 1 is a family of lightweight, decoder-only transformer models from Google, available in 2B and 7B parameter sizes. Designed for various text generation tasks, they incorporate rotary positional embeddings, shared input/output embeddings, GEGLU activation, and RMSNorm. The 2B model uses multi-query attention, while 7B uses multi-head attention.