---
license: gemma
base_model: google/gemma-4-E4B-it-qat-q4_0-unquantized
base_model_relation: quantized
tags:
- openvino
- int4
- qat
- intel
- arc
- igpu
- core-ultra
pipeline_tag: image-text-to-text
---

# Gemma 4 E4B QAT — OpenVINO INT4 (q4_0-matched scheme)

OpenVINO IR conversion of
[google/gemma-4-E4B-it-qat-q4_0-unquantized](https://huggingface.co/google/gemma-4-E4B-it-qat-q4_0-unquantized)
— Google's **quantization-aware-trained** Gemma 4 E4B checkpoint, converted with the
QAT-matched scheme (symmetric, group size 32):

```bash
optimum-cli export openvino -m google/gemma-4-E4B-it-qat-q4_0-unquantized \
  --task image-text-to-text --weight-format int4 --sym --group-size 32 \
  gemma-4-E4B-it-qat-int4-ov
```

QAT means this int4 build is trained to be quantized, so the quality loss is far smaller than
post-training quantization at the same bit width.

## Conversion note

Loader warnings about "missing" k/v projections on upper layers are Gemma 4's tied KV-shared
weights (verified benign). Serving and tooling: [core-ultra-llm-server](https://github.com/harmenwessels/core-ultra-llm-server).

## Usage (OpenVINO GenAI)

```python
import openvino_genai as ov_genai

pipe = ov_genai.VLMPipeline("gemma-4-E4B-it-qat-int4-ov", "GPU", CACHE_DIR="./.ovcache")
print(pipe.generate("Explain Python decorators in three sentences.", max_new_tokens=128))
```

VLM-shaped IR — requires `VLMPipeline` even for text-only use.

## Provenance & license

- Base: Google's Gemma 4 E4B QAT checkpoint (released 2026-04); weights are governed by the
  [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
- Conversion date: 2026-06-06; optimum-intel git-master, transformers 5.5.0
- No finetuning — direct quantization of Google's QAT weights