gemma 4 12B it qat q4 0 unquantized

Name: gemma 4 12B it qat q4 0 unquantized
Author: Google

by GoogleGemma

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

Released June 2026context N/A12B params

A compact but capable member of Google's Gemma 4 family, this 12B parameter model runs in a quantization-aware trained (QAT) Q4_0 configuration, meaning it was trained to handle the precision loss of 4-bit quantization gracefully rather than having quantization applied as an afterthought. It handles text-in, text-out tasks with the efficiency you'd expect from a model designed to run on consumer hardware.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

gemma 4 12B it qat q4 0 unquantized

by GoogleGemma

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

Released June 2026context N/A12B params

A compact but capable member of Google's Gemma 4 family, this 12B parameter model runs in a quantization-aware trained (QAT) Q4_0 configuration, meaning it was trained to handle the precision loss of 4-bit quantization gracefully rather than having quantization applied as an afterthought. It handles text-in, text-out tasks with the efficiency you'd expect from a model designed to run on consumer hardware.

Capabilities

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

Instruction Following

Strong

Reasoning & Logic

Glossary

4-bit QuantizationA specific type of quantization that represents model weights using only 4 bits instead of the original 32 bits, enabling very efficient inference on consumer hardware.Parameter ModelA neural network described by the number of learnable weights it contains; more parameters generally mean greater capacity to learn complex patterns, but also require more computational resources.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.Precision LossThe reduction in numerical accuracy that occurs when a model is compressed, which can slightly degrade performance on complex reasoning tasks while remaining acceptable for most everyday uses.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.

gemma 4 12B it qat q4 0 unquantized

Capabilities

gemma 4 12B it qat q4 0 unquantized

Capabilities

Use Case Fit

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