Instructions to use HanzoHuang/gemma-4-E2B-it-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/gemma-4-E2B-it-RKLLM with RKLLM:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
license: apache-2.0
base_model: google/gemma-4-E2B-it
pipeline_tag: text-generation
library_name: rkllm
tags:
- rkllm
- rockchip
- rk3576
- rk3588
- gemma
- gemma-4
gemma-4-E2B-it-RKLLM
RKLLM-converted Gemma 4 E2B IT language-model artifacts for Rockchip RK3576 and RK3588 NPUs.
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
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.