timmx

An extensible CLI and Python package for exporting timm models to various deployment formats. Born out of having too many one-off export scripts for fine-tuned timm models — timmx unifies them behind a single command-line interface with a plugin-based backend system.

Supported Formats

Format	Command	Output
ONNX	timmx export onnx	.onnx
Core ML	timmx export coreml	.mlpackage / .mlmodel
LiteRT / TFLite	timmx export litert	.tflite
ncnn	timmx export ncnn	directory (.param + .bin)
TensorRT	timmx export tensorrt	.engine
ExecuTorch	timmx export executorch	.pte
torch.export	timmx export torch-export	.pt2
TorchScript	timmx export torchscript	.pt

Requirements

• Python >=3.11
• uv

Installation

Core install (includes timm, torch, typer, rich):

pip install timmx

Install with specific backend extras:

pip install 'timmx[onnx]'           # ONNX export
pip install 'timmx[coreml]'         # Core ML export
pip install 'timmx[litert]'         # LiteRT/TFLite export
pip install 'timmx[ncnn]'           # ncnn export (via pnnx)
pip install 'timmx[executorch]'     # ExecuTorch export (XNNPack, CoreML delegates)
pip install 'timmx[onnx,coreml]'    # multiple backends

TensorRT requires CUDA and must be installed separately:

pip install tensorrt  # Linux/Windows with CUDA only

Note: The executorch and litert extras have conflicting torch version requirements (executorch needs torch>=2.10.0, litert needs torch<2.10.0) and cannot be installed in the same environment.

Check which backends are available:

timmx doctor

Quick Start

uv sync --extra onnx --extra coreml --extra ncnn --group dev
uv run timmx doctor
uv run timmx --help

Usage Examples

ONNX

uv run timmx export onnx resnet18 --pretrained --output ./artifacts/resnet18.onnx

Export a fine-tuned checkpoint with dynamic batching:

uv run timmx export onnx resnet18 \
  --checkpoint ./checkpoints/model.pth \
  --input-size 3 224 224 \
  --dynamic-batch \
  --output ./artifacts/resnet18_finetuned.onnx

Exported models are automatically optimized with onnxslim (constant folding, dead-code elimination, operator fusion). To skip optimization:

uv run timmx export onnx resnet18 --pretrained --no-slim --output ./artifacts/resnet18.onnx

Core ML

uv run timmx export coreml resnet18 \
  --pretrained \
  --convert-to mlprogram \
  --compute-precision float16 \
  --output ./artifacts/resnet18.mlpackage

Using torch.export as source (beta):

uv run timmx export coreml resnet18 \
  --pretrained \
  --source torch-export \
  --convert-to mlprogram \
  --compute-precision float16 \
  --output ./artifacts/resnet18_te.mlpackage

Flexible batch size:

uv run timmx export coreml resnet18 \
  --dynamic-batch \
  --batch-size 2 \
  --batch-upper-bound 8 \
  --output ./artifacts/resnet18_dynamic.mlpackage

LiteRT / TFLite

Supported modes: fp32, fp16, dynamic-int8, int8.

uv run timmx export litert resnet18 \
  --mode fp16 \
  --output ./artifacts/resnet18_fp16.tflite

INT8 with calibration data:

# generate a calibration tensor
uv run python -c "import torch; torch.save(torch.randn(64, 3, 224, 224), 'calibration.pt')"

uv run timmx export litert resnet18 \
  --mode int8 \
  --calibration-data ./calibration.pt \
  --calibration-steps 8 \
  --output ./artifacts/resnet18_int8.tflite

NHWC input layout:

uv run timmx export litert resnet18 \
  --mode fp32 \
  --nhwc-input \
  --output ./artifacts/resnet18_nhwc.tflite

ncnn

Exports via pnnx and writes a deployment-ready ncnn model directory containing model.ncnn.param, model.ncnn.bin, and model_ncnn.py. pnnx intermediate files are removed automatically.

uv run timmx export ncnn resnet18 \
  --pretrained \
  --output ./artifacts/resnet18_ncnn

Export without fp16 weight quantization:

uv run timmx export ncnn resnet18 \
  --pretrained \
  --no-fp16 \
  --output ./artifacts/resnet18_ncnn_fp32

TensorRT

Requires an NVIDIA GPU with CUDA and the tensorrt package (pip install tensorrt).

uv run timmx export tensorrt resnet18 \
  --pretrained \
  --mode fp16 \
  --output ./artifacts/resnet18_fp16.engine

INT8 with calibration:

uv run timmx export tensorrt resnet18 \
  --pretrained \
  --mode int8 \
  --calibration-data ./calibration.pt \
  --calibration-steps 8 \
  --output ./artifacts/resnet18_int8.engine

Dynamic batch size:

uv run timmx export tensorrt resnet18 \
  --pretrained \
  --dynamic-batch \
  --batch-size 4 \
  --batch-min 1 \
  --batch-max 32 \
  --output ./artifacts/resnet18_dynamic.engine

ExecuTorch

Export with XNNPack delegation (default, runs on CPU across all platforms):

uv run timmx export executorch resnet18 \
  --pretrained \
  --output ./artifacts/resnet18.pte

CoreML delegation (macOS — targets Apple Neural Engine / GPU / CPU):

uv run timmx export executorch resnet18 \
  --pretrained \
  --delegate coreml \
  --output ./artifacts/resnet18_coreml.pte

CoreML with explicit fp32 compute precision (default is fp16):

uv run timmx export executorch resnet18 \
  --pretrained \
  --delegate coreml \
  --compute-precision float32 \
  --output ./artifacts/resnet18_coreml_fp32.pte

INT8 quantized with XNNPack:

uv run timmx export executorch resnet18 \
  --pretrained \
  --mode int8 \
  --calibration-data ./calibration.pt \
  --calibration-steps 8 \
  --output ./artifacts/resnet18_int8.pte

INT8 quantized with CoreML:

uv run timmx export executorch resnet18 \
  --pretrained \
  --delegate coreml \
  --mode int8 \
  --output ./artifacts/resnet18_coreml_int8.pte

Dynamic batch size:

uv run timmx export executorch resnet18 \
  --pretrained \
  --dynamic-batch \
  --batch-size 2 \
  --output ./artifacts/resnet18_dynamic.pte

torch.export

uv run timmx export torch-export resnet18 \
  --pretrained \
  --dynamic-batch \
  --batch-size 2 \
  --output ./artifacts/resnet18.pt2

When using --dynamic-batch, set --batch-size to at least 2 so PyTorch can capture a symbolic batch dimension.

TorchScript

uv run timmx export torchscript resnet18 \
  --pretrained \
  --output ./artifacts/resnet18.pt

Use torch.jit.script instead of the default trace:

uv run timmx export torchscript resnet18 \
  --pretrained \
  --method script \
  --output ./artifacts/resnet18_scripted.pt

Diagnostics

Run timmx doctor to check your installation and see which backends are available:

timmx doctor

This shows the timmx version, Python/torch versions, and a table of backend availability with install hints for any missing dependencies.

Roadmap

• ONNX
• Core ML
• LiteRT / TFLite
• ncnn
• torch.export
• TensorRT
• TorchScript
• ExecuTorch (XNNPack + CoreML delegates)
• OpenVINO
• TensorFlow (SavedModel / .pb)
• TensorFlow.js
• TFLite Edge TPU
• RKNN
• MNN
• PaddlePaddle

Development

uv sync --extra onnx --extra coreml --extra ncnn --group dev  # install extras + pytest
uvx ruff format .                                              # format
uvx ruff check .                                               # lint
uv run pytest                                                  # test
uv build                                                       # build

Adding a New Backend

See CONTRIBUTING.md for a step-by-step guide on implementing and registering a new export backend.

AI Disclaimer

This project is developed with the assistance of AI tools. The original export logic comes from various standalone scripts I wrote for exporting fine-tuned timm models to different deployment formats. The process of consolidating these scripts into a unified CLI tool has been aided by AI, with my oversight at every step, reviewing generated code, manually fixing issues during backend porting, and validating that exports produce correct results.