version: "1.0.1" name: nvfp4 description: > Generate a working NVFP4 (W4A4) quantization example script and save a compressed-tensors checkpoint. Triggers on: "nvfp4", "NVFP4", "fp4", "nvfp4 example", "quantize to nvfp4", "w4a4". allowed-tools: [Read, Write, Glob, Bash(make style), Bash(ls ), Bash(find ), WebFetch]

Write NVFP4 Example

Generate a working Python example script that quantizes a model to an NVFP4 scheme and saves a compressed-tensors checkpoint.

Shared Documentation

Read .claude/skills/shared_quantization.md for common steps on gathering model information, applying model-type adjustments (dense, MoE, multimodal), GPTQ, transforms, and calibration dataset configuration.

Step 1 — Gather information

Follow the shared documentation for gathering model information, GPTQ, transforms, and calibration dataset configuration.

IMPORTANT: NVFP4 is a W4A4 quantization scheme with:

• Weights: fp4 with per-group-16 scaling
• Activations: fp4 with calibrated global scale
• Requires calibration dataset for both weight and activation quantization — always use the shared oneshot_with_data.py template
• model_free_ptq is NOT supported — NVFP4 uses the oneshot path only

If the user specifically requests model_free_ptq, inform them it's not available for NVFP4 and proceed with the oneshot approach.

Step 2 — Use the oneshot template (only path for NVFP4)

Read the shared template at .claude/skills/templates/oneshot_with_data.py and use it as the starting point. Set scheme="NVFP4".

Follow the shared documentation to apply GPTQ and/or transform modifications to the recipe if requested.

Apply the model-type adjustments from the shared documentation before writing the final file.

Step 3 — Apply model-type adjustments

Apply the model-type adjustments documented in .claude/skills/shared_quantization.md.

Note: For MoE models, the pipeline automatically handles expert calibration via CalibrationAfmoeMoE module — no manual intervention needed.

Step 4 — Write the file

Place the file in examples/quantization_w4a4_fp4/.

Name the file {model_name_slug}_nvfp4.py (e.g. llama3_nvfp4.py, gemma4_nvfp4.py).

Run make style after writing the file.

Notes

• NVFP4 requires calibration data — unlike FP8 schemes, you cannot use oneshot(model=model, recipe=recipe) without a dataset.
• model_free_ptq is not supported for NVFP4 — always use the oneshot path with calibration data.
• NVFP4 targets NVIDIA hardware for W4A4 quantization with per-group weight scaling and calibrated activation scaling.
• The oneshot call must include dataset, max_seq_length, and num_calibration_samples parameters.
• save_compressed=True is optional — the checkpoint saves in compressed-tensors format either way. Omit unless explicitly requested.
• For MoE models, expert calibration is handled automatically by the CalibrationAfmoeMoE module during the calibration phase.