---

name: frontends description: "Convert supported Keras, PyTorch, ONNX, QONNX, quantized, spiking, example, serialization, and plotting inputs into hls4ml ModelGraph projects." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Frontends

Use this sub-skill when the task starts with a frontend model or serialized frontend artifact and ends with an hls4ml ModelGraph, saved model, or conversion-ready configuration.

Handles

• Keras v2 / TensorFlow Keras
• Keras v3
• QKeras / QKeras-v3
• HGQ / HGQ2
• Distributed-arithmetic fallback in Keras v3
• PyTorch, Brevitas export-to-QONNX flows, and PQuantML
• SNN readout markers
• ONNX / QONNX preprocessing
• Example-model helpers, serialization, plotting, and legacy config/convert routes

Use this sub-skill for

• generating an hls_config
• converting a model into an hls4ml project
• inspecting the active frontend layer registry
• saving or reloading a converted model
• doing a safe conversion smoke check

Do not use this sub-skill for

• vendor synthesis, build, or report generation -> backends
• precision or resource tuning after conversion -> analysis
• custom layer or plugin authoring -> extensions

Operating flow

1. Identify the frontend and required optional packages.
2. Build or load the native model.
3. Create a frontend-specific config with the matching config_from_* helper.
4. Convert with the matching convert_from_* helper or a compatible YAML route.
5. Compile and run a small prediction smoke when possible.
6. If unsupported operators, conflicting quantization stacks, or vendor tools are required, route to the sibling skill instead of guessing.

Reference map

• references/api-reference.md
• references/keras-and-quantization.md
• references/pytorch-and-snn.md
• references/onnx-qonnx.md
• references/examples-serialization-cli.md
• references/troubleshooting.md

Safe helpers

• scripts/smoke_convert_keras.py
• scripts/smoke_convert_pytorch.py
• scripts/inspect_supported_layers.py

Verified snapshot

This skill was authored against hls4ml 0.1.0.dev1+gb90fb0673 in a CPU-only inspection environment. Keras v2/TensorFlow 2.14, PyTorch, QKeras, HGQ, ONNX, qonnx, da4ml, and snntorch were available. Keras v3, HGQ2, QKeras-v3, PQuantML, and sparsepixels remain source-documented but were not live-verified in that environment.