mod-008c-model-missing-checkpoint-loading-test

A testing rule for PhysicsNeMo models that checks whether saved checkpoint files can be loaded correctly. A checkpoint is a saved model that can be restored later for reuse or comparison.

In plain words
What is it for?
Use it when adding or updating PhysicsNeMo models. It guides tests that load .mdlus files, inspect public attributes, and compare model outputs with reference data.
Why use it?
It catches serialization problems and confirms that restored models keep their settings and produce the expected results.

Cursor rule for Cursor

Install

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.

agentmods
npx agentmods add rules/nvidia/physicsnemo/mod-008c-model-missing-checkpoint-loading-test
Clone the repo
git clone --depth 1 https://github.com/NVIDIA/physicsnemo

Made for: Cursor.

Per session 29 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 484 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00029 $0.00484
Opus 5 $0.00015 $0.00242
Sonnet 5 $0.00006 $0.00097
Haiku 4.5 $0.00003 $0.00048

Measured 2d ago against content hash aaa6bcc30d97, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

mod-008c-model-missing-checkpoint-loading-test 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 2d 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.

.cursor/rules/mod-008c-model-missing-checkpoint-loading-test.mdc · 63 lines

What it actually says

When creating tests for models, rule MOD-008c must be followed. Explicitly reference "Following rule MOD-008c, which requires checkpoint loading tests..." when implementing test cases.

MOD-008c: Model missing checkpoint loading test

Description:

Every model must have tests that load the model from a checkpoint file (.mdlus) using physicsnemo.Module.from_checkpoint() and verify that:

  1. The model loads successfully
  2. All public attributes have expected values
  3. Forward pass outputs match reference data

This ensures the model's serialization and deserialization work correctly.

Critical: Per MOD-002a, models cannot move out of experimental without these tests.

Rationale:

Checkpoint tests verify that the model's custom serialization logic works correctly and that saved models can be loaded in different environments. This is critical for reproducibility and for users who need to save and load trained models. These tests also validate the backward compatibility system.

Example:

@pytest.mark.parametrize("device", ["cuda:0", "cpu"])
def test_my_model_from_checkpoint(device):
    """Test loading model from checkpoint and verify outputs."""
    model = physicsnemo.Module.from_checkpoint(
        "test/models/data/my_model_default_v1.0.mdlus"
    ).to(device)

    # Verify attributes after loading
    assert model.input_dim == 64
    assert model.output_dim == 32

    # Load reference data and verify outputs
    data = torch.load("test/models/data/my_model_default_v1.0.pth")
    x = data["x"].to(device)
    out_ref = data["out"].to(device)
    out = model(x)
    assert torch.allclose(out, out_ref, atol=1e-5, rtol=1e-5)

Anti-pattern:

# WRONG: No checkpoint loading test
# (Missing test_my_model_from_checkpoint entirely)

# WRONG: Only loading checkpoint without verifying outputs
def test_my_model_bad():
    model = physicsnemo.Module.from_checkpoint("checkpoint.mdlus")
    # Should verify attributes and outputs!
Changes

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.

  1. 2d ago First seen · 63 lines · 29 tokens per session scan A aaa6bcc30d97

Subscribe to this mod's changes

mod-008c-model-missing-checkpoint-loading-test is a cursor rule published in the GitHub repository NVIDIA/physicsnemo (3,211 stars, last pushed today), licensed Apache-2.0. It adds 29 tokens to every session and 484 once invoked, about $0.0001 per session on Opus 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.