Instructions to use wardner/Qwen3-TTS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- RKLLM
How to use wardner/Qwen3-TTS 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
Commit History
3 days ago
-
Fix chip attribution: RKNN graphs target RK182x compute cards, host is RK3588
wade.weng7298b7c -
Add Apache-2.0 LICENSE and NOTICE (upstream attribution), document mirror + no-.git install
wade.weng1ad5170
4 days ago
-
Publish Qwen3-TTS RK3588 artifacts + worker manifest
wade.wengReference the RK182X-CNN-ResNet50V2 v1.2.0 publish layout: - .gitattributes: default Hub LFS template + per-path rules for the 15 weight files (.rknn/.weight/.fp16.bin/.npy/tokenizer.json) - qwen3-tts.json: worker manifest, kept as PLAIN TEXT on purpose so it stays diffable/reviewable and previewable on the Hub - SHA256SUMS: checksums for all payloads - config.json: model metadata, worker wiring and file manifest - README.md / README_CN.md: bilingual docs incl. install steps1b44f4f -
initial commit
Verified signature8ccc37f