--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: mobiletransformers pipeline_tag: text-generation tags: - mobiletransformers - onnx - on-device - android - lora - int4 --- ![MobileTransformers](mobiletransformers_banner.png) # Qwen/Qwen2.5-0.5B-Instruct — MobileTransformers package On-device (Android) package exported from **Qwen/Qwen2.5-0.5B-Instruct** with MobileTransformers. ## What this package can do - `core` — shared files every other group needs - `inference` — generate or score on device - `train` — **fine-tune on device**, then merge the adapter back into the base weights - `rag` — retrieve over documents you ingest, and ground answers in them ## Fine-tuning method - **lora** — LoRA — low-rank adapters on the attention projections. - Rank: `8` - Adapted modules: `q_proj`, `v_proj` ## Provenance - Base model: `Qwen/Qwen2.5-0.5B-Instruct` - Selected task: `text-generation-with-past` - Quantization: int4 - Toolchain: optimum-onnx 0.1.0, transformers 4.57.6, ort-training 1.23.0+cpu ## Licenses - Framework: not declared in this package — see the repository - Base model weights: see the base model above (this package redistributes an export of those weights, so their terms govern its contents) ## Android runtime - Minimum API: 28 - Required ABIs: any ## Variants | id | EP | quant | engines | features | min API | rec. RAM (MB) | | --- | --- | --- | --- | --- | --- | --- | | cpu-int4 | cpu | int4 | native | core, inference, train, rag | 28 | — | Default variant: `cpu-int4`. ## Running this model This is a **MobileTransformers package**, not a plain Hugging Face model: it is a manifest plus per-variant ONNX stages and a weight-handoff map. `transformers`, `optimum` and plain `onnxruntime` cannot load it. Use the framework: **https://github.com/martinkorelic/mobiletransformers** ```kotlin // Android — pulls, verifies and installs on first use. val model = MobileTransformers.fromPretrained( context = context, repoId = "mobiletransformers/Qwen2.5-0.5B-Instruct", ) ``` ```bash # Host — download and inspect the package without a device. mobiletransformers pull --repo-id mobiletransformers/Qwen2.5-0.5B-Instruct ``` ## Citation If you are using this framework for your own work, please cite: ```bibtex @misc{mobiletransformers2025, author = {Koreli\v{c}, Martin and Pejovi{\'c}, Veljko}, title = {MobileTransformers: An On-Device LLM PEFT Framework for Fine-Tuning and Inference}, year = {2025}, howpublished = {\url{https://gitlab.fri.uni-lj.si/lrk/mobiletransformers}} } ```