train-model
Execute model training with optimization algorithms. Use when running training loops on datasets.
Packaged view
This page reorganizes the original catalog entry around fit, installability, and workflow context first. The original raw source lives below.
Install command
npx @skill-hub/cli install benchflow-ai-skillsbench-train-model
Repository
Skill path: registry/terminal_bench_2.0/full_batch_reviewed/terminal_bench_2_0_train-fasttext/environment/skills/train-model
Execute model training with optimization algorithms. Use when running training loops on datasets.
Open repositoryBest for
Primary workflow: Analyze Data & AI.
Technical facets: Data / AI.
Target audience: everyone.
License: Unknown.
Original source
Catalog source: SkillHub Club.
Repository owner: benchflow-ai.
This is still a mirrored public skill entry. Review the repository before installing into production workflows.
What it helps with
- Install train-model into Claude Code, Codex CLI, Gemini CLI, or OpenCode workflows
- Review https://github.com/benchflow-ai/SkillsBench before adding train-model to shared team environments
- Use train-model for ml workflows
Works across
Favorites: 0.
Sub-skills: 0.
Aggregator: No.
Original source / Raw SKILL.md
---
name: train-model
description: "Execute model training with optimization algorithms. Use when running training loops on datasets."
mcp_fallback: none
category: ml
tier: 2
user-invocable: false
---
# Train Model
Implement and execute model training loops including forward/backward passes, gradient updates, and checkpoint management.
## When to Use
- Running full training pipeline on datasets
- Fine-tuning pretrained models
- Experimenting with hyperparameter variations
- Reproducing paper results
## Quick Reference
```mojo
# Mojo training loop pattern
struct Trainer:
var model: NeuralNetwork
var optimizer: Optimizer
var loss_fn: LossFn
fn train_epoch(mut self, mut dataloader: BatchLoader) -> Float32:
var total_loss: Float32 = 0.0
var batches: Int = 0
for batch in dataloader:
var predictions = self.model(batch.inputs)
var loss = self.loss_fn(predictions, batch.targets)
# Backward pass and optimization
total_loss += loss
batches += 1
return total_loss / Float32(batches)
```
## Workflow
1. **Prepare data pipeline**: Load and batch training data
2. **Initialize model**: Create network with specified architecture
3. **Set up optimizer**: Choose optimizer (SGD, Adam) with learning rate
4. **Implement training loop**: Forward pass, compute loss, backward pass, update weights
5. **Monitor progress**: Log loss, save checkpoints, validate periodically
## Output Format
Training report:
- Loss values per epoch
- Training time per epoch
- Validation metrics (accuracy, loss)
- Learning curves (loss vs epoch)
- Final model performance
- Checkpoint locations
## References
- See `prepare-dataset` skill for data pipeline setup
- See `evaluate-model` skill for validation
- See CLAUDE.md > Mojo for training loop patterns