Sentence Similarity
sentence-transformers
RKLLM
English
Chinese
rk3588
rockchip
npu
quantized
qwen3
embedding
Instructions to use GatekeeperZA/Qwen3-Embedding-0.6B-RKLLM-v1.2.3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use GatekeeperZA/Qwen3-Embedding-0.6B-RKLLM-v1.2.3 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("GatekeeperZA/Qwen3-Embedding-0.6B-RKLLM-v1.2.3") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - RKLLM
How to use GatekeeperZA/Qwen3-Embedding-0.6B-RKLLM-v1.2.3 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
File size: 2,436 Bytes
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license: apache-2.0
base_model: Qwen/Qwen3-Embedding-0.6B
tags:
- rkllm
- rk3588
- rockchip
- npu
- quantized
- qwen3
- embedding
- sentence-transformers
language:
- en
- zh
pipeline_tag: sentence-similarity
---
# Qwen3-Embedding-0.6B — RKLLM v1.2.3 (w8a8, RK3588)
RKLLM conversion of [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) for Rockchip RK3588 NPU inference.
Converted with RKLLM Toolkit v1.2.3. This model generates dense vector embeddings for semantic search, RAG pipelines, and similarity tasks — all running on the NPU without a GPU.
## Key Details
| Property | Value |
|----------|-------|
| Base Model | Qwen/Qwen3-Embedding-0.6B |
| Toolkit Version | RKLLM Toolkit v1.2.3 |
| Runtime Version | RKLLM Runtime ≥ v1.2.1 (v1.2.3 recommended) |
| Quantization | w8a8 (8-bit weights, 8-bit activations) |
| Target Platform | RK3588 |
| NPU Cores | 3 |
| Optimization Level | 1 |
| Hybrid Ratio | 0.5 |
| Model Type | Embedding |
| Languages | English, Chinese (multilingual) |
## Why This Model?
Running a dedicated embedding model on the RK3588 NPU allows the main LLM to use the full NPU without context-switching. Qwen3-Embedding-0.6B achieves strong retrieval performance at a compact size, making it ideal for local RAG pipelines on edge hardware.
Pair with the Qwen3-Reranker-0.6B for a complete retrieval stack.
## Hardware Tested
- **Orange Pi 5 Plus** — RK3588, 16GB RAM, Armbian Linux
- RKNPU driver 0.9.8
- RKLLM Runtime v1.2.3
## Usage
### With the RKLLM Embedding Service
```bash
mkdir -p ~/models/Qwen3-Embedding-0.6B
cd ~/models/Qwen3-Embedding-0.6B
git lfs install && git clone https://huggingface.co/GatekeeperZA/Qwen3-Embedding-0.6B-RKLLM-v1.2.3 .
```
Use with [GatekeeperZA/RKLLM-API-Server](https://github.com/GatekeeperZA/RKLLM-API-Server) embedding endpoint.
## File Listing
| File | Description |
|------|-------------|
| `Qwen3-Embedding-0.6B-rk3588-w8a8-opt-1-hybrid-ratio-0.5.rkllm` | Quantized embedding model for RK3588 NPU |
## Compatibility Notes
- Minimum runtime: RKLLM Runtime v1.2.1. v1.2.3 recommended.
- RKNPU driver: ≥ 0.9.6
- SoCs: RK3588 / RK3588S. Not compatible with RK3576 without reconversion.
- RAM: ~1GB loaded.
## Acknowledgements
- Alibaba Qwen Team for Qwen3-Embedding
- Rockchip / airockchip for the RKLLM toolkit and runtime
- Converted by [GatekeeperZA](https://huggingface.co/GatekeeperZA)
|