gemma 4 E4B it OBLITERATED

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A compact, open-weight model from Google's Gemma family, Gemma 4 E4B uses a mixture-of-experts architecture to punch above its active parameter count. It handles text-in, text-out tasks with the efficiency you'd expect from a smaller footprint model. Being Apache 2.0 licensed means it's freely usable and modifiable, making it accessible for local deployment via GGUF or safetensors formats.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Strong

Creative Writing

Moderate

Tool Use

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

gemma 4 E4B it OBLITERATED

gemma

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A compact, open-weight model from Google's Gemma family, Gemma 4 E4B uses a mixture-of-experts architecture to punch above its active parameter count. It handles text-in, text-out tasks with the efficiency you'd expect from a smaller footprint model. Being Apache 2.0 licensed means it's freely usable and modifiable, making it accessible for local deployment via GGUF or safetensors formats.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Instruction Following

Strong

Creative Writing

Moderate

Tool Use

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

Active Parameter CountThe number of model parameters that are actually used during inference for a given input, as opposed to the total parameters available.ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.GGUFA file format for quantized models designed for efficient CPU and GPU inference with llama.cpp.Local DeploymentRunning a model directly on your own computer or server instead of sending requests to a remote service.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.SafetensorsA safe, fast file format for storing model weights, designed to prevent code execution vulnerabilities.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.