Downloads · 30 days
0
aprender-tsp-poc
paiml/aprender-tsp-poc
aprender-tsp-poc is a other model from paiml. Use it for the other task on the model card, and read the license before you ship it in a product. It is set up for aprender-tsp. The card lists the license as mit.
Pre-trained TSP (Traveling Salesman Problem) optimization models using Ant Colony Optimization, built with aprender-tsp.
Access
Public
Updated Nov 29, 2025
Repo size
—
Likes
0
Public
Repo makeup
Click a slice to open those files.
.md3.1 KB · 64%
At a glance
- Task
- other
- Library
- aprender-tsp
- License
- mit
- Access
- Public
- Created
- Nov 29, 2025
- Updated
- Nov 29, 2025
- SHA
- f3d2de65
Model card
From the Hugging Face model README
aprender-tsp POC Models
Pre-trained TSP (Traveling Salesman Problem) optimization models using Ant Colony Optimization, built with aprender-tsp.
Models Included
| Model | Instance | Cities | Optimal | Achieved | Gap | Tier |
|---|---|---|---|---|---|---|
| berlin52-aco.apr | berlin52 | 52 | 7,542 | 7,687 | 1.92% | Good |
| att48-aco.apr | att48 | 48 | 10,628 | 11,085 | 4.30% | Acceptable |
| eil51-aco.apr | eil51 | 51 | 426 | 443 | 4.07% | Acceptable |
All models achieve < 5% gap from TSPLIB optimal solutions.
Quick Start
# Install aprender-tsp
cargo install aprender-tsp
# Download a model
huggingface-cli download paiml/aprender-tsp-poc berlin52-aco.apr
# Solve a new instance using the model
aprender-tsp solve -m berlin52-aco.apr your-instance.tsp
# View model info
aprender-tsp info berlin52-aco.apr
# Benchmark against known optimal
aprender-tsp benchmark berlin52-aco.apr --instances berlin52.tsp
Training Parameters
All models trained with identical ACO parameters for reproducibility:
| Parameter | Value | Description |
|---|---|---|
| Algorithm | ACO (Ant Colony Optimization) | Uses core aprender::AntColony |
| Iterations | 2000 | Number of optimization iterations |
| Ants | 20 | Number of artificial ants |
| Alpha (α) | 1.0 | Pheromone importance |
| Beta (β) | 2.5 | Heuristic importance |
| Rho (ρ) | 0.1 | Evaporation rate |
| Seed | 42 | Random seed for reproducibility |
Instance Sources
Models are trained on standard TSPLIB benchmark instances:
- berlin52: 52 locations in Berlin, Germany (Groetschel)
- att48: 48 state capitals of the contiguous USA (Padberg/Rinaldi)
- eil51: 51-city problem (Christofides/Eilon)
Reference: TSPLIB
File Format
Models use the .apr binary format:
- Magic bytes:
APR\0 - Version: 1
- CRC32 checksum for integrity
- Compact size: ~77 bytes per model
Solution Quality Tiers
| Tier | Gap from Optimal |
|---|---|
| Optimal | < 0.1% |
| Excellent | < 1% |
| Good | < 2% |
| Acceptable | < 5% |
| Poor | >= 5% |
Train Your Own
# Train on your instance
aprender-tsp train your-instance.tsp -o your-model.apr --algorithm aco --iterations 2000 --seed 42
# Or use other algorithms
aprender-tsp train your-instance.tsp -o model.apr --algorithm tabu # Tabu Search (2-opt)
aprender-tsp train your-instance.tsp -o model.apr --algorithm ga # Genetic Algorithm
aprender-tsp train your-instance.tsp -o model.apr --algorithm hybrid # GA + Tabu + ACO
Citation
@software{aprender,
title = {Aprender: Machine Learning in Pure Rust},
author = {PAIML},
url = {https://github.com/paiml/aprender},
year = {2025}
}
License
MIT License - see LICENSE file.
- Task
- other
- Library
- aprender-tsp
- License
- mit
- Created
- Nov 29, 2025
- Updated
- Nov 29, 2025