# Model Weight Loading And Deployment

> Use when you have a pre-trained MSGO model checkpoint (PFAS or lipid variant) and need to evaluate it against a real mass spectrometry dataset (300+ real spectra, LC–QTOF, or custom CSV) to generate predicted molecular structures and compare against ground truth or baseline results.

- Skill: `holobiomicslab/model-weight-loading-and-deployment` (Agent Skill)
- Install (CLI): `npx skillmds@latest add holobiomicslab/model-weight-loading-and-deployment`
- Raw SKILL.md: https://api.skillmd.com/api/skills/holobiomicslab/model-weight-loading-and-deployment/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- License: CC-BY-4.0
- Author: HolobiomicsLab (https://skillmd.com/u/holobiomicslab)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/holobiomicslab/model-weight-loading-and-deployment

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# model-weight-loading-and-deployment

## Summary

Load pre-trained deep learning model weights from a repository and deploy them for inference on new spectral data to generate molecular structure predictions. This skill enables reproducibility of published structure-generation performance without retraining.

## When to use

You have a pre-trained MSGO model checkpoint (PFAS or lipid variant) and need to evaluate it against a real mass spectrometry dataset (300+ real spectra, LC–QTOF, or custom CSV) to generate predicted molecular structures and compare against ground truth or baseline results.

## When NOT to use

- Model weights have not been downloaded or checkpoint path is invalid—training must be performed first using tools/train.py
- Input spectra are in non-standard formats (not CSV or incompatible with eval_standard.py schema)
- Evaluating on pseudo SMILES-spectrum pairs used for training—use the 300+ real spectrum validation set instead to assess generalization

## Inputs

- Pre-trained model checkpoint directory (ckpts/pfas or ckpts/lipid)
- Real mass spectrometry spectrum dataset (CSV format with spectrum features)
- Polarization mode specification (pos or neg)
- Beam search size parameter (integer, 300–500)

## Outputs

- Results CSV file with predicted SMILES structures
- Top-10 ranked predictions per spectrum with scores
- Inference time and structure-generation performance metrics

## How to apply

Load the released MSGO model weights from github.com/aaronma2020/MSGO using Python 3.7 and Torch 1.7.1 by specifying the checkpoint path (ckpts/pfas or ckpts/lipid). Prepare your input spectrum dataset as a CSV file compatible with the eval_standard.py evaluation script, specifying polarization mode (pos or neg) and beam search size (300–500 depending on model variant). Execute inference by calling tools/eval.py or tools/eval_standard.py with the model path and input CSV, collecting predicted SMILES structures ranked by beam search score. Verify correctness by confirming the output CSV includes top-10 predictions with associated confidence scores for each spectrum.

## Related tools

- **Python** (Runtime environment for model loading and inference)
- **Torch** (Deep learning framework for checkpoint deserialization and GPU-accelerated inference)
- **MSGO repository** (Source of pre-trained model weights and evaluation scripts (eval.py, eval_standard.py)) — github.com/aaronma2020/MSGO

## Examples

```
python tools/eval_standard.py --log_path ckpts/pfas --real_csv ./data/example/pfas.csv --out_csv ./pfas_results.csv --beam_size 500 --polar neg
```

## Evaluation signals

- Model checkpoint successfully loads without Torch deserialization errors
- Output CSV contains exactly 10 ranked predictions per spectrum with monotonically decreasing beam search scores
- Predicted SMILES are valid and canonicalizable (no malformed SMILES strings)
- Structure-generation metrics (e.g., exact match rate on 300+ real spectra) match or closely reproduce the reported paper results
- Inference completes without CUDA out-of-memory or framework compatibility errors for the specified Python 3.7 and Torch 1.7.1 versions

## Limitations

- Model weights are specialized to PFAS or lipid chemical classes—deployment on spectra from other compound classes may yield poor predictions
- Evaluation requires exact Python 3.7 and Torch 1.7.1 versions; newer PyTorch releases may break checkpoint compatibility
- Pseudo SMILES-spectrum pairs used during training may introduce systematic bias in predicted structures; real-world validation datasets are recommended
- Beam search size (300–500) trades inference speed against coverage of candidate structures—smaller beam sizes may miss correct predictions

## Evidence

- [other] Load the pre-trained MSGO model weights from the released github.com/aaronma2020/MSGO repository using Python 3.7 and Torch 1.7.1.: "Load the pre-trained MSGO model weights from the released github.com/aaronma2020/MSGO repository using Python 3.7 and Torch 1.7.1."
- [readme] Download the model weights in ckpts/pfas or ckpts/lipid, run python tools/eval.py --log_path [ckpts/pfas or ckpts/lipid]: "Download the model weights in ckpts/pfas or ckpts/lipid, run
```
python tools/eval.py --log_path [ckpts/pfas or ckpts/lipid]
```"
- [other] Execute inference on each spectrum using the MSGO model to generate predicted molecular structures. Collect and format the structure-generation predictions and performance metrics into a results file.: "Execute inference on each spectrum using the MSGO model to generate predicted molecular structures. Collect and format the structure-generation predictions and performance metrics into a results file."
- [readme] Then you can obatin a results csv file inluding top 10 predicts.: "Then you can obatin a results csv file inluding top 10 predicts."
- [readme] For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid. For evaluation, we use 300+ real specturm to verify our method: "For Training, we use 30k+ pseudo smiles-specturm pairs generated by cfmid. For evaluation, we use 300+ real specturm to verify our method"