google-ai-edge
- 21 skills
- 0 followers
- 3 hours ago last updated
- ▌ Mood Music · google-ai-edge bundleA skill to suggest or play music based on the user's mood, including analyzing images or audio, by querying available genres and generating music via the Loudly API.
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- ▌ Restaurant Roulette · google-ai-edge bundleShow a roulette wheel to allow user to randomly select a restaurant based on location and cuisine.
- ▌ Mood Tracker · google-ai-edge bundleA simple mood tracking skill that stores your daily mood and comments. Use this when the user wants to log their mood, track how they feel, or see their mood history.
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- ▌ Auto Quant · google-ai-edge bundleSystematically explore quantization configurations for a TFLite model using the AI Edge Quantizer API, finding the optimal recipe that balances file size and accuracy. Use this skill whenever the user wants to quantize a model, optimize a recipe, explore quantization tradeoffs, minimize size bounds, or perform selective quantization using AI Edge Quantizer (or AEQ) framework. Applies to any model scale and modality: CNNs, segmentation nets, classifiers, embedding models, audio models, and LLMs.
- ▌ Litert CLI · google-ai-edgeLiteRT CLI tool to download, convert, quantize, run, benchmark, and visualize LiteRT models.
- ▌ Converting Pytorch To Litert · google-ai-edge bundleConverts PyTorch models (e.g. ResNet, timm, HuggingFace transformers) directly to LiteRT (.tflite) flatbuffer format. Use when converting PyTorch models to TFLite, setting up export environments, or troubleshooting torch-to-litert conversion bugs. Don't use for ONNX exports or converting existing TensorFlow models.
- ▌ Litert Model Equivalence Test · google-ai-edge bundleValidates equivalence between LiteRT models (litert_lm) and PyTorch models (transformers). Use when you need to verify that an exported LiteRT model produces the same outputs as the original Hugging Face model. Supports multi-turn conversations and custom prompts.
- ▌ Litert Quantization Calib · google-ai-edgeAssists the user to calibrate, merge, and statically quantize litert LLM models (such as Gemma 3) in standard open-source (OSS) environments. Use when the user wants to run LLM calibration, merge task JSON results, align KV cache parameters across models, protect sensitive layers in Float32, or run quantized inference testing. Don't use for JAX/PyTorch custom quantization configurations or non-litert models.
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- ▌ Learn Something New · google-ai-edge bundleA daily learning companion that teaches users a new concept, generates a beautiful image card, and schedules recurring daily learning notifications.
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- ▌ Schedule Notification · google-ai-edgeSchedule a notification for a specific date or repeating daily.
- ▌ Gpu Clean Conversion · google-ai-edgeConvert a PyTorch or Hugging Face model into a LiteRT model that runs fully on the GPU via the CompiledModel API with verified-correct output, and lay it out as a model recipe. Use when converting a new model, or when a converted model is rejected by the GPU, falls back to CPU, or returns wrong numbers on device.
- ▌ On Device Verification · google-ai-edgeProve a converted or quantized LiteRT model on the actual device via the CompiledModel API - confirm GPU residency, compare device output against the source model, and diagnose device-only failures such as silent CPU fallback, whole-graph compile ceilings, and fp16 range breaks. Use after conversion or quantization, when device output is wrong or NaN, when a clean graph fails to compile only on device, or when GPU and CPU outputs are suspiciously identical.
- ▌ Accuracy Safe Quantization · google-ai-edgeShrink a converted LiteRT model with ai-edge-quantizer (fp16 / int8 / int4) without losing accuracy, verifying parity against the float source after every step. Use when choosing a quantization recipe for a new model, when a quantized model fails to load, degrades on a task benchmark, or degenerates over long generations, or when deciding between dynamic-range, weight-only, and blockwise variants.
- ▌ Litert Conversion Workflow · google-ai-edge bundleConvert a Hugging Face LLM or vision-language model checkpoint into a .litertlm bundle that runs on the LiteRT-LM runtime with verified quality - classify the architecture against known runtime walls, pick the recipe family (dense, reasoning, hybrid SSM, VLM), export, quantize, gate the result against the source model, and publish. Use when converting a new LLM or VLM to LiteRT-LM, when a converted bundle crashes on the first message or dies at engine creation, when a quantized model answers worse than its source, or when deciding whether a model is convertible at all.
- ▌ Compiled Model App Scaffolding · google-ai-edgeBuild a new Android app (Kotlin, Compose) around a verified LiteRT model using the CompiledModel API - the app architecture, the inference-layer lifecycle rules, model delivery, and the UI traps that masquerade as model bugs. Use when turning a converted and device-verified model into a demo or product app, when an app's inference layer leaks memory or blocks the UI, or when a model that verified clean looks wrong inside an app.
- ▌ Litert Compiled Model Migration · google-ai-edge bundleRapidly migrate an Android application from legacy TensorFlow Lite (TFLite) to modern LiteRT CompiledModel API v2.1.6 in Open Source GitHub repositories. Supports True Async Execution (runAsync), Zero-Copy I/O Buffers, NPU JIT compilation, and automated 2-stage verification self-testing.