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litert-community
/
Qwen3-30B-A3B

Text Generation
LiteRT-LM
English
litertlm
qwen
Qwen3
Mixture of Experts
Model card Files Files and versions
xet
Community

Instructions to use litert-community/Qwen3-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • LiteRT-LM

    How to use litert-community/Qwen3-30B-A3B with LiteRT-LM:

    # LiteRT-LM runs on various platforms (Android, iOS, Windows, Linux, macOS, IoT, Web/WASM)
    # and supports many APIs (C++, Python, Kotlin, Swift, JavaScript, Flutter).
    # For platform-specific integration guides, please refer to the official developer website:
    # https://ai.google.dev/edge/litert-lm
    
    # To try LiteRT-LM, the easiest way is to use our CLI tool.
    # 1. Install the LiteRT-LM CLI tool:
    pip install -U litert-lm
    
    # 2. Download and run this model locally:
    # See: https://ai.google.dev/edge/litert-lm/cli
    # A single .litertlm file in the repo is picked automatically; otherwise the CLI asks which one to run
    # (or pass its name right after the repo id).
    litert-lm run \
      --from-huggingface-repo=litert-community/Qwen3-30B-A3B \
      --prompt="Write me a poem"
  • Notebooks
  • Google Colab
  • Kaggle
Qwen3-30B-A3B
41.3 GB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 7 commits
mlboydaisuke's picture
mlboydaisuke
README: the moe residual-add form (x + moe) + moe * 0 and why
f12ad63 verified 7 days ago
  • .gitattributes
    1.68 kB
    Mixed INT4 (int4 blockwise experts, int8 attention and lm_head), KV cache 2,048 8 days ago
  • LICENSE
    11.3 kB
    LICENSE (upstream, Apache-2.0) 8 days ago
  • README.md
    10.8 kB
    README: the moe residual-add form (x + moe) + moe * 0 and why 7 days ago
  • litertlm_manifest.json
    12.7 kB
    litertlm_manifest.json 8 days ago
  • qwen3_30b_a3b_mixed_int4_ekv2048.litertlm
    20.7 GB
    xet
    Mixed INT4 (int4 blockwise experts, int8 attention and lm_head), KV cache 2,048 8 days ago
  • qwen3_30b_a3b_mixed_int4_ekv4096.litertlm
    20.7 GB
    xet
    Mixed INT4 (int4 blockwise experts, int8 attention and lm_head), KV cache 4,096 8 days ago