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aerialblancaservices/v2rmp-routing-ml

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v2rmp Routing ML Models

A collection of lightweight neural models for route optimization (VRP/CPP), built with Candle (pure Rust ML) and trained on synthetic + real-world VRP instances.

Part of the v2rmp project — a Rust TUI/CLI for road network extraction, compilation, and multi-vehicle route optimization.

Models

Model	File	Architecture	Purpose
AutoML Predictor	`automl_v2.safetensors`	28 → 64 → 5 MLP	Predict instance-aware solver hyperparameters (max iterations, temperature, tabu tenure, cooling rate, neighbourhood radius)
Solver Selector	`solver_selector_v2.safetensors`	28 → 128 → 64 → 6 MLP	Classify VRP instances to the best algorithm among 6 solvers (default, Clarke-Wright, sweep, Or-Opt, 2-Opt, neural-guided)
Quality Predictor	`quality_predictor_v2.safetensors`	28 → 64 → 32 → 2 MLP	Predict gap-to-optimal (%) and tour length (km) before solving
Move Scorer	`move_scorer_v2.safetensors`	16 → 32 → 16 → 1 MLP	Score candidate 2-Opt / Or-Opt moves for neural-guided local search
Graph Embedder	`graph_embed.safetensors`	2-layer GraphSAGE (10 → 64 → 64)	Produce 64-dim learned embeddings for road network edges

Input Features

All MLP models share the same 28-dim normalized instance feature vector derived from VRP instance statistics (stop count, vehicle count, bounding box spread, demand statistics, distance matrix stats, etc.).

Model Loading (Rust / Candle)

rust

use candle_core::{Device, DType};
use candle_nn::{linear, Linear, Module, VarBuilder};

// Load safetensors
let tensors = candle_core::safetensors::load("solver_selector_v2.safetensors", &device)?;
let vb = VarBuilder::from_tensors(tensors, DType::F32, &device);

// Build layers
let lin1 = linear(28, 128, vb.pp("lin1"))?;
let lin2 = linear(128, 64, vb.pp("lin2"))?;
let lin3 = linear(64, 6, vb.pp("lin3"))?;

See the v2rmp source for full loading code.

License

MIT OR Apache-2.0 (same as v2rmp crate).

Generated by ML Intern

This model repository was generated by ML Intern, an agent for machine learning research and development on the Hugging Face Hub.

—Try ML Intern: https://smolagents-ml-intern.hf.space
—Source code: https://github.com/huggingface/ml-intern

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = 'aerialblancaservices/v2rmp-routing-ml'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

For non-causal architectures, replace AutoModelForCausalLM with the appropriate AutoModel class.