Instructions to use HanzoHuang/Spark-X2.5-1.7B-RKLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use HanzoHuang/Spark-X2.5-1.7B-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
Spark-X2.5-1.7B · RKLLM
Text generation on Rockchip RK3576 and RK3588, converted from XHToken/Spark-X2.5-1.7B.
- Conversion toolkit: RKLLM Toolkit 1.3.1.
- Inputs / output: text → text.
- License:
apache-2.0; the upstream model card describes its terms and intended use.
Choose your files
Use the files for your board’s exact SoC. w4a16 uses 4-bit weights and 16-bit activations; w4a16_g128 adds a weight group size of 128. w8a8 uses 8-bit weights and activations. File size below is download size, not total runtime RAM.
| Board | Quantization | Size | Language model |
|---|---|---|---|
| RK3576 | w4a16_g128 |
1.56 GiB | Spark-X2.5-1.7B_RK3576_w4a16_g128.rkllm |
| RK3576 | w8a8 |
2.14 GiB | Spark-X2.5-1.7B_RK3576_w8a8.rkllm |
| RK3588 | w8a8 |
2.12 GiB | Spark-X2.5-1.7B_RK3588_w8a8.rkllm |
Run on your board
With Docker installed on a matching Rockchip board, this example starts the RK3576 w4a16_g128 model and its API on port 8001:
sudo docker run --rm -d --name rkllm \
--privileged -v /dev:/dev -p 8001:8001 \
ghcr.io/hanzo-huang/rkllm-docker/llm/spark-x2.5-1.7b:rk3576-w4a16-g128
The image includes its model file. See rkllm-docker for API requests, other board variants, and configuration.
To download the files separately instead:
hf download HanzoHuang/Spark-X2.5-1.7B-RKLLM \
RK3576/Spark-X2.5-1.7B_RK3576_w4a16_g128.rkllm \
--local-dir ./models
These are native RKLLM/RKNN artifacts. Use a compatible RKLLM runtime and the model’s correct chat template when integrating them in another application.
Release notes
All three variants passed functional checks on their matching RK3576/RK3588 boards using RKLLM 1.3.1 and the packaged Docker images: arithmetic, a factual question, streamed output, and conversation recall. These are smoke tests, not a quality benchmark.
The exported context length is 4,096 tokens. Conversion used ModelScope source weights and SDK-generated calibration samples. Spark calibration required an isolated Transformers 4.57.1 environment; other conversion environments were left unchanged.
SHA-256 checksums
725bed8bbd4e6c682b542920dd43081a7f1c29ce5e38b69eceb3e9e8c710e5b4 RK3576/Spark-X2.5-1.7B_RK3576_w4a16_g128.rkllm
fe47ea39dd7968f0cfd4673e9ed189dbec768600dd35f068ca3f33c38a9b8267 RK3576/Spark-X2.5-1.7B_RK3576_w8a8.rkllm
4c87d982c70424ce493a96f2c06bfb49ca9f5e7fd9f805c70f5cf7eb14f64577 RK3588/Spark-X2.5-1.7B_RK3588_w8a8.rkllm
A downloadable checksum list is also available in SHA256SUMS.
Credits
Original model: XHToken/Spark-X2.5-1.7B. Conversion tools: Rockchip RKLLM.
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