Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA-NeMo/Megatron-Bridge --skill parity-testinggit clone --depth 1 https://github.com/NVIDIA-NeMo/Megatron-BridgeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/parity-testing)<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/parity-testing"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/parity-testing/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia-nemo/megatron-bridge/parity-testing"><img src="https://agentmods.dev/badge/skills/nvidia-nemo/megatron-bridge/parity-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00069 | $0.02339 |
| Opus 5.5 | $0.00028 | $0.00936 |
| Sonnet 5.5 | $0.00014 | $0.00468 |
| Haiku 4.5 | $0.00007 | $0.00234 |
Grade A, and why
parity-testing scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Parity Testing for Megatron Bridge
This skill provides the decision framework for choosing the right
verification tool and interpreting results. For the full model onboarding
workflow (which includes parity testing as milestones 1 and 2), see the
add-model-support skill.
Quick Decision: Which Tool to Run
| What you want to verify | Tool | GPU? | When to use |
|---|---|---|---|
| All weights round-trip exactly (single GPU) | hf_megatron_roundtrip.py |
No | First check after writing a bridge |
| Weights round-trip with TP/PP/EP | hf_megatron_roundtrip_multi_gpu.py |
Yes | After single-GPU passes |
| Forward-pass logit correlation | compare_hf_and_megatron/compare.py |
Yes | After round-trip passes |
| Text generation sanity | hf_to_megatron_generate_text.py |
Yes | Separate inference evidence |
| Programmatic weight check | weights_verification_table() |
Yes | Inside Python scripts |
| VLM generation sanity | hf_to_megatron_generate_vlm.py |
Yes | VLM models |
All tools live under examples/conversion/.
3-Level Test Strategy
Level 1: State Dict Round-Trip (exact match)
Check tensor coverage and mapping behavior before interpreting forward results. Pure layout transformations must round-trip weights exactly. Investigate arithmetic parameter transformations separately from missing or misplaced weights.
# Single-GPU round-trip
uv run python examples/conversion/hf_megatron_roundtrip.py \
--hf-model-id <org>/<model>
# Multi-GPU with TP=2
uv run python -m torch.distributed.run --nproc_per_node=2 \
examples/conversion/hf_megatron_roundtrip_multi_gpu.py \
--hf-model-id <org>/<model> --tp 2
# Multi-GPU with PP=2
uv run python -m torch.distributed.run --nproc_per_node=2 \
examples/conversion/hf_megatron_roundtrip_multi_gpu.py \
--hf-model-id <org>/<model> --pp 2
Expected: Every weight mapped by pure layout transformations shows "Matches Original: checkmark". Investigate every "X" before assigning a cause.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago Changed · +1 lines bc3fcd684572
- 25d ago Changed · +15 lines e7a3d1b3ae7b
- 1mo ago Changed · +9 lines 858d41e321d4
- 1mo ago First seen · 192 lines · 69 tokens per session scan A 7ed8b6298d81
parity-testing is a skill published in the GitHub repository NVIDIA-NeMo/Megatron-Bridge (970 stars, last pushed today), licensed Apache-2.0. It adds 69 tokens to every session and 2,339 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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