Item	Meaning	How to get it
`input_dim`	Number of feature columns expected by the model	`len(dataset.feature_names)`
`feature_names`	Exact feature order used by the cache	`dataset.feature_names`
Historical target	Stored in the last feature column	Enforced by the cache pipeline
`batch`	Dictionary passed to `forward()`	`collate_variable(...)` from the training script

Do not reorder columns manually. The checkpoint was trained with the cache feature order exactly as produced by the repository pipeline.

Quick Start

Load from the Hub

Hugging Face repository id: ireneiele/agrimatnet-vegetation-forecasting

python

from agrimatnet.model_quantile import AgriMatNetQuantile

model = AgriMatNetQuantile.from_pretrained("ireneiele/agrimatnet-vegetation-forecasting")
model.eval()

Load from a local snapshot

python

from agrimatnet.model_quantile import AgriMatNetQuantile

model = AgriMatNetQuantile.from_pretrained("./agrimatnet-hf")
model.eval()

Manual instantiation + `.pth` checkpoint

Use this path if you are loading a training checkpoint produced by the repository scripts.

python

import torch
from agrimatnet.model_quantile import AgriMatNetQuantile

model = AgriMatNetQuantile(
    input_dim=28,
    quantiles=[0.1, 0.5, 0.9],
    d_model=128,
    num_layers=8,
    num_heads=8,
    dim_feedforward=512,
    dropout=0.1,
)

checkpoint = torch.load("checkpoint_best.pth", map_location="cpu")
state_dict = checkpoint.get("model_state_dict", checkpoint)
model.load_state_dict(state_dict)
model.eval()

Inference Contract

The forward pass expects a dictionary with the same structure produced by the dataset pipeline:

history
future
history_mask
future_mask
history_pad_mask
future_pad_mask
future_target_positions

The feature order must match the training cache exactly. The historical target is kept in the last feature column.

End-to-End Example

python

import torch
from torch.utils.data import DataLoader

from agrimatnet.model_quantile import AgriMatNetQuantile
from agrimatnet.train_quantile_ablation import collate_variable
from dataset_builder.torch_dataset import CacheTimeSeriesDataset

dataset = CacheTimeSeriesDataset(
    cache_dir="timeSeries/cache/<split>",
    apply_scaling=True,
    feature_engineering=True,
    discretize_target=False,
)

loader = DataLoader(
    dataset,
    batch_size=4,
    shuffle=False,
    collate_fn=collate_variable,
)

model = AgriMatNetQuantile.from_pretrained("ireneiele/agrimatnet-vegetation-forecasting")
model.eval()

batch = next(iter(loader))
with torch.no_grad():
    preds = model(batch)

print(preds.shape)  # (B, T, 3)
print(dataset.feature_names)

ireneiele/agrimatnet-vegetation-forecasting

Model configuration

Input Contract

Quick Start

Load from the Hub

Load from a local snapshot

Manual instantiation + .pth checkpoint

Inference Contract

End-to-End Example

ireneiele/
agrimatnet-vegetation-forecasting

Manual instantiation + `.pth` checkpoint