Instructions to use macunaima/Qwen3-0.6B-SM8650-LiteRT-LM-Float with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT
How to use macunaima/Qwen3-0.6B-SM8650-LiteRT-LM-Float with LiteRT:
# 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
- LiteRT-LM
How to use macunaima/Qwen3-0.6B-SM8650-LiteRT-LM-Float 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 litert-lm run \ --from-huggingface-repo=macunaima/Qwen3-0.6B-SM8650-LiteRT-LM-Float \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Qwen3 0.6B β Qualcomm SM8650 β Float LiteRT-LM
Complete LiteRT-LM package containing the Qualcomm AOT prefill/decode model, float embedder, auxiliary model, Qwen tokenizer, and chat metadata.
Derived from Qwen/Qwen3-0.6B.
Source revision: c1899de289a04d12100db370d81485cdf75e47ca. The original Apache 2.0 license is included.
Target and precision
- Qualcomm SM8650, compiled with
aot_backend=qualcommandaot_soc_model=SM8650. - No quantization step:
quantization_recipe=''; no calibration, SRQ, INT4, or weight-only quantization. - The exported float graphs have no tensors with quantization scales. Qualcomm's internal compiler precision is not guaranteed to remain FP32 throughout.
- Prefill length: 128; cache length: 1280. Uses split cache and an external float embedder.
- Requires a compatible LiteRT-LM / Qualcomm runtime; compilation does not establish compatibility with every phone or firmware using this SoC.
Artifact
File: model.litertlm
Size: 2,682,744,028 bytes (2.50 GiB)
SHA-256: a559f0146486b411473a306ac6a5bbe2a108702ebf4aa9d42f17dec28dc6be2e
Validation
Host export and Qualcomm AOT compilation completed. The packaged TFLite sections
were hash-matched to the exported models, and Qwen3 tokenizer/chat metadata was
inspected. The AOT main model contains two subgraphs with two DISPATCH_OP calls.
No physical SM8650 inference test has been performed. Quality, memory use,
latency, tokens per second, and runtime fallback behavior are unmeasured.
Build versions and source provenance are recorded in artifact_manifest.json.
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