Deep-Learning Model Checkpoint Loading
License: restricted — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution.
Summary
Load pre-trained neural network weights from disk checkpoints to enable inference without retraining. This skill is essential for applying trained FIDDLE models to new MS/MS spectra or batch predictions.
When to use
When you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without training from scratch. Use this skill before running inference on new samples or benchmarks.
When NOT to use
- Input checkpoints are from different FIDDLE versions with incompatible architectures (e.g., v1.x with pre-Siamese rescore vs. v2.0.0 Siamese rescore) without retesting on reference data.
- You are training a new model from scratch—use training scripts instead; checkpoint loading assumes weights are already optimized.
- Instrument type (Orbitrap vs. Q-TOF) does not match your spectra—load the corresponding instrument-specific checkpoint or retraining will be needed.
Inputs
- Trained PyTorch checkpoint file (.pt) for TCN formula prediction model
- Trained PyTorch checkpoint file (.pt) for rescore model (Siamese architecture)
- YAML configuration file specifying model architecture parameters
- MS/MS spectrum data in MGF format with required fields: TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY
Outputs
- Loaded PyTorch model object in memory ready for inference
- CSV file with scored formula candidates per spectrum (columns: ID, Mass, Pred Formula, Refined Formula (0..4), Rescore (0..4))
- JSON/structured output with ranked formula candidates and confidence scores
How to apply
Download pre-trained checkpoint files (e.g., fiddle_tcn_orbitrap.pt and fiddle_rescore_orbitrap.pt) from the FIDDLE release page or Zenodo deposit (DOI 10.5281/zenodo.19181279) and place them in the ./check_point/ directory. For CLI workflows, run msfiddle-download-models to fetch checkpoints to the default location (~/.msfiddle/check_point). Specify checkpoint paths explicitly using --resume_path (TCN model) and --rescore_resume_path (rescore model) flags, or pass them programmatically to the MsFiddlePredictor constructor. The rescore model in v2.0.0 uses a redesigned Siamese architecture that rescores initial formula predictions; ensure your checkpoint version matches your expected architecture. Validate the loaded model by running on a known reference sample (e.g., caffeine from GNPS) and checking that output CSV/JSON contains scored formula candidates.
Related tools
- msfiddle (Command-line and Python API wrapper for FIDDLE inference; provides download-models subcommand and high-level predict functions that handle checkpoint loading internally) — https://github.com/josiehong/msfiddle
- FIDDLE (Deep learning method research codebase; contains lower-level training and evaluation scripts; reference implementation for checkpoint loading via config and resume_path arguments) — https://github.com/JosieHong/FIDDLE
Examples
msfiddle --test_data ./demo/input_msms.mgf --config_path ./config/fiddle_tcn_orbitrap.yml --resume_path ./check_point/fiddle_tcn_orbitrap.pt --rescore_resume_path ./check_point/fiddle_rescore_orbitrap.pt --result_path ./demo/output_fiddle.csv --device 0
Evaluation signals
- Loaded model successfully initializes without shape mismatch or device errors; checkpoint file size and modification date match expected release.
- Running inference on reference caffeine Orbitrap spectrum from GNPS produces output CSV with caffeine formula (C8H10N4O2) ranked in top-5 candidates with non-zero rescore confidence.
- Output CSV schema matches specification: contains ID, Mass, Pred Formula, Refined Formula (0..4), Refined Mass (0..4), Rescore (0..4) columns with numeric scores in valid range [0, 1].
- Model inference latency and GPU/CPU utilization are consistent with documented expectations (checkpoint loaded once, reused across batch predictions).
- Results on external benchmark (CASMI 2016/2017, EMBL-MCF 2.0, NIST23) match published FIDDLE accuracy metrics within sampling variance.
Limitations
- Checkpoint must match the installed PyTorch and Python versions; mismatched CUDA/ROCm builds may cause silent inference errors or degraded performance. Test on a small reference set first.
- Siamese rescore architecture in v2.0.0 is a breaking change from v1.x; old v1.x checkpoints will not load without explicit version alignment or retraining.
- Instrument-specific checkpoints (Orbitrap vs. Q-TOF) are not interchangeable; applying Orbitrap-trained weights to Q-TOF spectra or vice versa will produce inaccurate predictions.
- No explicit discussion section in source documents; reproducibility gaps and failure modes (e.g., behavior on spectra from unlisted collision energies, extreme m/z ranges) are not fully documented.
Evidence
- [readme] To use the pre-trained models, please use the following scripts to download the weights from the release page and place them in the
./check_point/ directory: "To use the pre-trained models, please use the following scripts to download the weights from the release page and place them in the"
- [readme] The rescore model has been redesigned with a Siamese architecture in version 2.0.0: "The rescore model has been redesigned (Siamese architecture), see details in CHANGELOG.md"
- [readme] Download the pre-trained checkpoints before running predictions using msfiddle-download-models command: "Download the pre-trained checkpoints before running predictions: msfiddle-download-models"
- [readme] Loaded models can be reused for efficient batched prediction in Python applications: "Reuse loaded models for efficient batched prediction in Python applications"
- [other] The rescore model in FIDDLE v2.0.0 has been redesigned with a Siamese architecture, which is the operative inference architecture for the test: "The rescore model in FIDDLE v2.0.0 has been redesigned with a Siamese architecture, which is the operative inference architecture for the test"
1---2name: deep-learning-model-checkpoint-loading-23description: Use when when you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without training from scratch. Use this skill before running inference on new samples or benchmarks.4license: CC-BY-4.05---67# Deep-Learning Model Checkpoint Loading89> **License: restricted** — no clear open-source license detected for the underlying tool; verify licensing before commercial use or redistribution. <!-- asb-license-banner -->10## Summary1112Load pre-trained neural network weights from disk checkpoints to enable inference without retraining. This skill is essential for applying trained FIDDLE models to new MS/MS spectra or batch predictions.1314## When to use1516When you have MS/MS spectra from GNPS or other libraries and need to apply a pre-trained FIDDLE model (TCN formula predictor or Siamese rescore architecture) without training from scratch. Use this skill before running inference on new samples or benchmarks.1718## When NOT to use1920- Input checkpoints are from different FIDDLE versions with incompatible architectures (e.g., v1.x with pre-Siamese rescore vs. v2.0.0 Siamese rescore) without retesting on reference data.21- You are training a new model from scratch—use training scripts instead; checkpoint loading assumes weights are already optimized.22- Instrument type (Orbitrap vs. Q-TOF) does not match your spectra—load the corresponding instrument-specific checkpoint or retraining will be needed.2324## Inputs2526- Trained PyTorch checkpoint file (.pt) for TCN formula prediction model27- Trained PyTorch checkpoint file (.pt) for rescore model (Siamese architecture)28- YAML configuration file specifying model architecture parameters29- MS/MS spectrum data in MGF format with required fields: TITLE, PRECURSOR_MZ, PRECURSOR_TYPE, COLLISION_ENERGY3031## Outputs3233- Loaded PyTorch model object in memory ready for inference34- CSV file with scored formula candidates per spectrum (columns: ID, Mass, Pred Formula, Refined Formula (0..4), Rescore (0..4))35- JSON/structured output with ranked formula candidates and confidence scores3637## How to apply3839Download pre-trained checkpoint files (e.g., `fiddle_tcn_orbitrap.pt` and `fiddle_rescore_orbitrap.pt`) from the FIDDLE release page or Zenodo deposit (DOI 10.5281/zenodo.19181279) and place them in the `./check_point/` directory. For CLI workflows, run `msfiddle-download-models` to fetch checkpoints to the default location (`~/.msfiddle/check_point`). Specify checkpoint paths explicitly using `--resume_path` (TCN model) and `--rescore_resume_path` (rescore model) flags, or pass them programmatically to the `MsFiddlePredictor` constructor. The rescore model in v2.0.0 uses a redesigned Siamese architecture that rescores initial formula predictions; ensure your checkpoint version matches your expected architecture. Validate the loaded model by running on a known reference sample (e.g., caffeine from GNPS) and checking that output CSV/JSON contains scored formula candidates.4041## Related tools4243- **msfiddle** (Command-line and Python API wrapper for FIDDLE inference; provides download-models subcommand and high-level predict functions that handle checkpoint loading internally) — https://github.com/josiehong/msfiddle44- **FIDDLE** (Deep learning method research codebase; contains lower-level training and evaluation scripts; reference implementation for checkpoint loading via config and resume_path arguments) — https://github.com/JosieHong/FIDDLE4546## Examples4748```49msfiddle --test_data ./demo/input_msms.mgf --config_path ./config/fiddle_tcn_orbitrap.yml --resume_path ./check_point/fiddle_tcn_orbitrap.pt --rescore_resume_path ./check_point/fiddle_rescore_orbitrap.pt --result_path ./demo/output_fiddle.csv --device 050```5152## Evaluation signals5354- Loaded model successfully initializes without shape mismatch or device errors; checkpoint file size and modification date match expected release.55- Running inference on reference caffeine Orbitrap spectrum from GNPS produces output CSV with caffeine formula (C8H10N4O2) ranked in top-5 candidates with non-zero rescore confidence.56- Output CSV schema matches specification: contains ID, Mass, Pred Formula, Refined Formula (0..4), Refined Mass (0..4), Rescore (0..4) columns with numeric scores in valid range [0, 1].57- Model inference latency and GPU/CPU utilization are consistent with documented expectations (checkpoint loaded once, reused across batch predictions).58- Results on external benchmark (CASMI 2016/2017, EMBL-MCF 2.0, NIST23) match published FIDDLE accuracy metrics within sampling variance.5960## Limitations6162- Checkpoint must match the installed PyTorch and Python versions; mismatched CUDA/ROCm builds may cause silent inference errors or degraded performance. Test on a small reference set first.63- Siamese rescore architecture in v2.0.0 is a breaking change from v1.x; old v1.x checkpoints will not load without explicit version alignment or retraining.64- Instrument-specific checkpoints (Orbitrap vs. Q-TOF) are not interchangeable; applying Orbitrap-trained weights to Q-TOF spectra or vice versa will produce inaccurate predictions.65- No explicit discussion section in source documents; reproducibility gaps and failure modes (e.g., behavior on spectra from unlisted collision energies, extreme m/z ranges) are not fully documented.6667## Evidence6869- [readme] To use the pre-trained models, please use the following scripts to download the weights from the release page and place them in the `./check_point/` directory: "To use the pre-trained models, please use the following scripts to download the weights from the [release page](https://github.com/JosieHong/FIDDLE/releases/tag/v1.0.0) and place them in the"70- [readme] The rescore model has been redesigned with a Siamese architecture in version 2.0.0: "The rescore model has been redesigned (Siamese architecture), see details in [CHANGELOG.md](./CHANGELOG.md)"71- [readme] Download the pre-trained checkpoints before running predictions using msfiddle-download-models command: "Download the pre-trained checkpoints before running predictions: msfiddle-download-models"72- [readme] Loaded models can be reused for efficient batched prediction in Python applications: "Reuse loaded models for efficient batched prediction in Python applications"73- [other] The rescore model in FIDDLE v2.0.0 has been redesigned with a Siamese architecture, which is the operative inference architecture for the test: "The rescore model in FIDDLE v2.0.0 has been redesigned with a Siamese architecture, which is the operative inference architecture for the test"
HolobiomicsLab