Important: the upstream Gemma 4 E2B IT model is multimodal/VLM-capable, but this repository contains only the RKLLM language-model binaries. The converted model exposed here is LLM-only: it does not accept images and does not include an RKNN vision encoder.

These hardware-specific .rkllm files require a compatible Rockchip RKLLM runtime. They are not Transformers checkpoints and cannot be loaded directly with Transformers, llama.cpp, or Ollama.

Base model

Upstream model: google/gemma-4-E2B-it
License: Apache-2.0
Upstream type: VLM/multimodal
RKLLM type: LLM-only

Conversion and variants

Toolkit version

RKLLM Toolkit: v1.3.0

Use a file built for the exact target SoC.

Target	Quantization	File	SHA256
RK3576	W4A16 (g128)	gemma-4-E2B-it_RK3576_w4a16_g128.rkllm	`973008fd7ebed81e2a26dcf7dcc1c28fde631837e3e021c7f3007d6e82647165`
RK3576	W8A8	gemma-4-E2B-it_RK3576_w8a8.rkllm	`b7357c01fcd51c8f896f06ee3a3746bc233031abd04e2fa0cebb5908b1382a01`
RK3588	W8A8	gemma-4-E2B-it_RK3588_w8a8.rkllm	`ae81d1e37eb54b40d2d366ef622a0c05acfb15537272e982efc1dfdd60557979`

The repository also includes gemma-4-E2B-it_data_quant.json, used as calibration data during conversion.

Usage

bash

hf download HanzoHuang/gemma-4-E2B-it-RKLLM \
  RK3576/gemma-4-E2B-it_RK3576_w4a16_g128.rkllm \
  --local-dir gemma-4-E2B-it-RKLLM

Run the file with the RKLLM runtime and the upstream Gemma instruction prompt format. For Docker deployment, see Hanzo-Huang/rkllm-docker.

Limitations

Vision input is not supported by these artifacts. Conversion can change output quality relative to the upstream model; validate the result on your target device.

Acknowledgements

Thanks to Google, Rockchip, and the RKLLM community.