Instructions to use litert-community/Phi-4-mini-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LiteRT-LM
How to use litert-community/Phi-4-mini-instruct 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=litert-community/Phi-4-mini-instruct \ --prompt="Write me a poem"
- Notebooks
- Google Colab
- Kaggle
Add measured Galaxy S26 GPU-vs-CPU rows for the .litertlm bundle
#2
by mlboydaisuke - opened
Thanks for publishing this bundle β Phi-4-mini through litert-lm 0.16.0 on a Galaxy S26 GPU delegates fully and generates correctly.
This PR adds one subsection under Performance: measured Galaxy S26 (SM8850) rows for Phi-4-mini-instruct_multi-prefill-seq_q8_ekv4096.litertlm, GPU (OpenCL) against CPU (XNNPACK), conditions inline. Both backends generated a correct answer before the numbers were quoted; the GPU rows are full delegation (decode 1648/1648 ops on LITERT_CL). The section is labeled as a different runtime path from the S24 Ultra table above it, so the two are not conflated.
Happy to reshape to the house style if you prefer.
mlboydaisuke changed pull request status to open