converting-pytorch-to-litert

Converts 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.

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저장소: google-ai-edge/litert-torch
최근 소스 활동: 2026년 10월 25일 21:16
감지된 SKILL.md 언어: 영어
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name: converting-pytorch-to-litert
description: Converts 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.

# PyTorch to LiteRT Conversion Guide **ATTENTION ALL AGENTS:** If you are tasked with converting a PyTorch model (e.g., ResNet or similar architectures) to a `.tflite` flatbuffer format in this repository, **you must strictly follow these rules based on recent project findings:** ## 1. Direct Conversion Only (No ONNX) Bypass ONNX entirely. Do not attempt to export the model to ONNX or use `onnx2tf`. Convert directly from PyTorch to LiteRT. ## 2. Use `litert-torch` Use the `litert-torch` library for the direct PyTorch to TFLite conversion. Note that `ai-edge-torch` is deprecated and has been officially renamed to `litert-torch`. ## 3. Strict Python Environment Rules - **Do NOT use Python 3.14**. It currently has compatibility issues with typing in `torchao` (`AttributeError: 'typing.Union' object has no attribute '__module__'`). - **Use Python 3.11** (e.g., `python3.11`) as it is highly recommended for stability and avoids the aforementioned type hint bugs. ## 4. Dependencies Before running any conversion script, ensure the following are installed in your `python3.11` environment: ```bash python3.11 -m pip install torch transformers timm litert-torch ai-edge-litert litert-cli-nightly

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litert-model-equivalence-test

Validates 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.

2026-10-05

litert-quantization-calib

Assists 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.

2026-08-07

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converting-pytorch-to-litert

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