Instructions to use oliver0804/MiniCPM5-2B-RK3588-rkllm-w8a8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use oliver0804/MiniCPM5-2B-RK3588-rkllm-w8a8 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
MiniCPM5-2B β RKLLM w8a8 for the RK3588 NPU
openbmb/MiniCPM5-2B converted to Rockchip RKLLM w8a8 format for the RK3588 / RK3588S NPU. Converted with rkllm-toolkit 1.3.1; requires the librkllmrt.so 1.3.1 runtime (older runtimes fail to load the file). No architecture changes were made.
Benchmark
Radxa ROCK 5C (RK3588S, 8 GB LPDDR4x), clocks locked with the official fix_freq_rk3588.sh (CPU 1.8/2.3 GHz, DDR 2112 MHz, NPU 1 GHz), greedy decoding, thinking disabled, max_new_tokens 768, fixed 7-question zh-TW benchmark (word problem, facts, numeric comparison, letter counting, syllogism, code generation, constrained formatting):
| score | generate | prefill | memory |
|---|---|---|---|
| 6 / 7 | 10.9 tok/s | 189 tok/s | ~2.5 GB |
The only miss is "9.11 vs 9.9". Answers occasionally mix in simplified-Chinese characters. For reference on the same board and benchmark: Gemma-4-E2B-it 6/7 @ 9.3 tok/s (2.6 GB), Qwen2.5-1.5B 4.5/7 @ 15.8 tok/s (1.8 GB).
Usage
Load with any RKLLM 1.3.1 runtime from airockchip/rknn-llm, e.g. the C++ demo:
./llm_demo MiniCPM5-2B_RK3588_w8a8.rkllm 2048 4096
The file is self-contained (weights + tokenizer); just point your runtime at it.
License
Apache-2.0, inherited from the base model (see LICENSE).
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Model tree for oliver0804/MiniCPM5-2B-RK3588-rkllm-w8a8
Base model
openbmb/MiniCPM5-2B