diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..3f57ecd
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,7 @@
+.env
+.venv/
+__pycache__/
+*.pyc
+*.pt
+checkpoints/
+wandb/
diff --git a/charge3net_ft/__init__.py b/charge3net_ft/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/charge3net_ft/data.py b/charge3net_ft/data.py
new file mode 100644
index 0000000..34ffa8d
--- /dev/null
+++ b/charge3net_ft/data.py
@@ -0,0 +1,326 @@
+"""
+Dataset and DataLoader for LeMatRho charge density data.
+
+Reads Parquet files produced by lematerial-fetcher, converts to charge3net's
+expected input format (padded dict batches), and builds PBC-aware atom-atom
+and atom-probe graphs using charge3net's KdTreeGraphConstructor.
+
+Uses lazy loading: only an index of valid rows is stored in memory (~2 MB).
+Parquet rows are read on-the-fly in __getitem__. Each DataLoader worker caches
+opened tables per chunk file so each file is read from disk only once per worker.
+"""
+
+import json
+import sys
+from functools import partial
+from pathlib import Path
+from typing import Optional
+
+import ase
+import ase.data
+import numpy as np
+import pyarrow.parquet as pq
+import torch
+from torch.utils.data import DataLoader, Dataset, random_split
+
+# ---------------------------------------------------------------------------
+# charge3net imports — add the cloned repo to sys.path so that its internal
+# `from src.charge3net.data...` imports resolve correctly.
+# Expects: <parent of LeMat-Rho>/charge3net/ (cloned from AIforGreatGood/charge3net)
+# ---------------------------------------------------------------------------
+_CHARGE3NET_ROOT = Path(__file__).resolve().parent.parent.parent / "charge3net"
+if not _CHARGE3NET_ROOT.exists():
+    raise RuntimeError(
+        f"charge3net repo not found at {_CHARGE3NET_ROOT}.\n"
+        "Clone it with: git clone https://github.com/AIforGreatGood/charge3net "
+        f"{_CHARGE3NET_ROOT}"
+    )
+if str(_CHARGE3NET_ROOT) not in sys.path:
+    sys.path.insert(0, str(_CHARGE3NET_ROOT))
+
+from src.charge3net.data.collate import collate_list_of_dicts  # noqa: E402
+from src.charge3net.data.graph_construction import KdTreeGraphConstructor  # noqa: E402
+from src.utils.data import calculate_grid_pos  # noqa: E402
+
+# Columns we actually need from Parquet
+_COLUMNS = [
+    "species_at_sites",
+    "cartesian_site_positions",
+    "lattice_vectors",
+    "compressed_charge_density",
+]
+
+# ---------------------------------------------------------------------------
+# Element symbol -> atomic number lookup
+# ---------------------------------------------------------------------------
+_SYMBOL_TO_Z = {s: z for z, s in enumerate(ase.data.chemical_symbols)}
+
+# Process-local table cache: keyed by file index, populated on first access.
+# Each DataLoader worker process has its own cache, so each chunk file is read
+# from disk at most once per worker instead of once per __getitem__ call.
+_TABLE_CACHE: dict = {}
+
+
+def _parse_grid_json(json_str: str) -> np.ndarray:
+    """Parse a JSON-serialised 3D grid string into a float32 numpy array."""
+    grid = json.loads(json_str)
+    return np.array(grid, dtype=np.float32)
+
+
+def _row_to_atoms_and_density(row: dict) -> tuple:
+    """
+    Convert a single Parquet row dict into an ase.Atoms object, a 3D density
+    grid, and the grid origin.
+
+    Returns
+    -------
+    atoms : ase.Atoms
+        The periodic structure.
+    density : np.ndarray
+        Shape (Nx, Ny, Nz) charge density grid.
+    origin : np.ndarray
+        Grid origin, [0, 0, 0] (charge3net convention).
+    """
+    species = row["species_at_sites"]
+    atomic_numbers = [_SYMBOL_TO_Z[s] for s in species]
+    positions = np.array(row["cartesian_site_positions"], dtype=np.float64)
+    cell = np.array(row["lattice_vectors"], dtype=np.float64)
+
+    atoms = ase.Atoms(
+        numbers=atomic_numbers,
+        positions=positions,
+        cell=cell,
+        pbc=True,
+    )
+
+    density = _parse_grid_json(row["compressed_charge_density"])
+    origin = np.zeros(3)
+
+    return atoms, density, origin
+
+
+def _build_parquet_index(parquet_dir: Path) -> tuple:
+    """
+    Scan all Parquet chunks and build a lightweight index of valid rows
+    (those with non-null compressed_charge_density).
+
+    Returns
+    -------
+    file_paths : list[Path]
+        Sorted list of Parquet file paths.
+    index : list[tuple[int, int]]
+        Each entry is (file_index, row_index_within_file).
+    """
+    file_paths = sorted(parquet_dir.glob("chunk_*.parquet"))
+    if not file_paths:
+        raise FileNotFoundError(f"No chunk_*.parquet files in {parquet_dir}")
+
+    index = []
+    n_total = 0
+    for fi, fp in enumerate(file_paths):
+        # Read only the charge density column to check for nulls.
+        # Use .is_valid (Arrow scalar) rather than .as_py() is not None to
+        # avoid creating Python objects for every row.
+        col = pq.read_table(fp, columns=["compressed_charge_density"]).column(
+            "compressed_charge_density"
+        )
+        n_rows = len(col)
+        n_total += n_rows
+        for ri in range(n_rows):
+            if col[ri].is_valid:
+                index.append((fi, ri))
+
+    n_valid = len(index)
+    print(f"LeMatRhoDataset: {n_valid}/{n_total} valid rows indexed from {len(file_paths)} files")
+    return file_paths, index
+
+
+class LeMatRhoDataset(Dataset):
+    """
+    PyTorch Dataset that lazily reads LeMatRho Parquet chunks and yields
+    charge3net-compatible graph dicts.
+
+    Only a lightweight index (~2 MB for 65k rows) is stored in memory.
+    Each __getitem__ call retrieves a single row. Chunk files are cached
+    per-worker so each file is loaded from disk only once per worker process.
+
+    Parameters
+    ----------
+    parquet_dir : str or Path
+        Directory containing chunk_*.parquet files.
+    cutoff : float
+        Cutoff radius for neighbor finding (Angstrom). Must match model.
+    num_probes : int or None
+        If set, randomly subsample this many probe points per sample
+        (used during training to save memory). If None, use all grid points.
+    _shared_index : tuple or None
+        Internal: pre-computed (file_paths, index) to share between
+        train/val datasets without scanning files twice.
+    """
+
+    def __init__(
+        self,
+        parquet_dir: str = None,
+        cutoff: float = 4.0,
+        num_probes: Optional[int] = None,
+        _shared_index: tuple = None,
+    ):
+        self.cutoff = cutoff
+        self.num_probes = num_probes
+
+        self.graph_constructor = KdTreeGraphConstructor(
+            cutoff=cutoff,
+            num_probes=num_probes,
+            disable_pbc=False,
+        )
+
+        if _shared_index is not None:
+            self._file_paths, self._index = _shared_index
+        else:
+            self._file_paths, self._index = _build_parquet_index(Path(parquet_dir))
+
+    def __len__(self):
+        return len(self._index)
+
+    def _read_row(self, idx: int) -> dict:
+        """
+        Read a single row from disk via its index entry.
+
+        Uses a process-local cache (_TABLE_CACHE) so each chunk file is
+        loaded from disk only once per worker, not on every __getitem__ call.
+        """
+        fi, ri = self._index[idx]
+        if fi not in _TABLE_CACHE:
+            _TABLE_CACHE[fi] = pq.read_table(self._file_paths[fi], columns=_COLUMNS)
+        table = _TABLE_CACHE[fi]
+        row = {}
+        for col in _COLUMNS:
+            row[col] = table.column(col)[ri].as_py()
+        return row
+
+    def __getitem__(self, idx: int) -> dict:
+        row = self._read_row(idx)
+        atoms, density, origin = _row_to_atoms_and_density(row)
+
+        # Generate grid positions — same function charge3net uses internally.
+        # For a (Nx, Ny, Nz) density grid, this creates fractional coordinates
+        # [0/Nx, 1/Nx, ..., (Nx-1)/Nx] in each dimension, then maps to
+        # Cartesian via the cell matrix.
+        grid_pos = calculate_grid_pos(density, origin, atoms.get_cell())
+
+        # Build the graph dict using charge3net's KdTreeGraphConstructor.
+        # This handles:
+        #   - Random probe subsampling (if num_probes is set)
+        #   - Dynamic supercell expansion for PBC
+        #   - KD-tree based atom->probe neighbor finding
+        #   - ASE-based atom->atom neighbor finding
+        graph_dict = self.graph_constructor(density, atoms, grid_pos)
+
+        return graph_dict
+
+
+def build_dataloaders(
+    parquet_dir: str,
+    cutoff: float = 4.0,
+    train_probes: int = 200,
+    val_probes: int = 1000,
+    batch_size: int = 4,
+    val_frac: float = 0.05,
+    test_frac: float = 0.05,
+    num_workers: int = 4,
+    seed: int = 42,
+    pin_memory: bool = False,
+) -> tuple:
+    """
+    Build train, validation, and test DataLoaders.
+
+    Scans Parquet files once, then creates three datasets (with different
+    probe sampling) that share the same lightweight index.
+
+    The split is deterministic given (seed, val_frac, test_frac). These
+    values must not change between runs if reported metrics are to remain
+    comparable.
+
+    Parameters
+    ----------
+    parquet_dir : str
+        Path to directory with chunk_*.parquet files.
+    cutoff : float
+        Neighbor cutoff (Angstrom).
+    train_probes : int
+        Number of randomly sampled probes per sample during training.
+    val_probes : int
+        Number of probes per sample during validation and test.
+    batch_size : int
+        Batch size.
+    val_frac : float
+        Fraction of data to use for validation.
+    test_frac : float
+        Fraction of data to hold out as a test set (never used for model
+        selection — only evaluated once at the end of training).
+    num_workers : int
+        DataLoader workers.
+    seed : int
+        Random seed for split reproducibility.
+    pin_memory : bool
+        Pin memory for GPU transfer.
+
+    Returns
+    -------
+    train_loader, val_loader, test_loader : DataLoader, DataLoader, DataLoader
+    """
+    # Build the index once, share between all three datasets
+    shared_index = _build_parquet_index(Path(parquet_dir))
+
+    train_dataset = LeMatRhoDataset(
+        cutoff=cutoff, num_probes=train_probes, _shared_index=shared_index
+    )
+    val_dataset = LeMatRhoDataset(
+        cutoff=cutoff, num_probes=val_probes, _shared_index=shared_index
+    )
+    test_dataset = LeMatRhoDataset(
+        cutoff=cutoff, num_probes=val_probes, _shared_index=shared_index
+    )
+
+    # Split indices (same for all three datasets, different probe sampling)
+    n = len(train_dataset)
+    n_val = int(n * val_frac)
+    n_test = int(n * test_frac)
+    n_train = n - n_val - n_test
+    generator = torch.Generator().manual_seed(seed)
+    train_indices, val_indices, test_indices = random_split(
+        range(n), [n_train, n_val, n_test], generator=generator
+    )
+
+    train_subset = torch.utils.data.Subset(train_dataset, train_indices.indices)
+    val_subset = torch.utils.data.Subset(val_dataset, val_indices.indices)
+    test_subset = torch.utils.data.Subset(test_dataset, test_indices.indices)
+
+    collate_fn = partial(collate_list_of_dicts, pin_memory=pin_memory)
+
+    train_loader = DataLoader(
+        train_subset,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+        pin_memory=pin_memory,
+    )
+    val_loader = DataLoader(
+        val_subset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+        pin_memory=pin_memory,
+    )
+    test_loader = DataLoader(
+        test_subset,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=num_workers,
+        collate_fn=collate_fn,
+        pin_memory=pin_memory,
+    )
+
+    return train_loader, val_loader, test_loader
diff --git a/charge3net_ft/model.py b/charge3net_ft/model.py
new file mode 100644
index 0000000..c70629e
--- /dev/null
+++ b/charge3net_ft/model.py
@@ -0,0 +1,143 @@
+"""
+ChargE3Net model wrapper for fine-tuning on LeMatRho data.
+
+Thin wrapper around the official E3DensityModel that handles:
+1. Instantiation with the correct MP hyperparameters.
+2. Loading pre-trained checkpoint weights (both legacy PL and new format).
+3. Forward pass returning predicted density at probe points.
+"""
+
+import sys
+from pathlib import Path
+
+import torch
+from torch import nn
+
+# ---------------------------------------------------------------------------
+# charge3net imports
+# Expects: <parent of LeMat-Rho>/charge3net/ (cloned from AIforGreatGood/charge3net)
+# ---------------------------------------------------------------------------
+_CHARGE3NET_ROOT = Path(__file__).resolve().parent.parent.parent / "charge3net"
+if not _CHARGE3NET_ROOT.exists():
+    raise RuntimeError(
+        f"charge3net repo not found at {_CHARGE3NET_ROOT}.\n"
+        "Clone it with: git clone https://github.com/AIforGreatGood/charge3net "
+        f"{_CHARGE3NET_ROOT}"
+    )
+if str(_CHARGE3NET_ROOT) not in sys.path:
+    sys.path.insert(0, str(_CHARGE3NET_ROOT))
+
+from src.charge3net.models.e3 import E3DensityModel  # noqa: E402
+
+# ---------------------------------------------------------------------------
+# Default hyperparameters matching the Materials Project checkpoint
+# (from configs/charge3net/model/e3_density.yaml)
+# ---------------------------------------------------------------------------
+MP_MODEL_DEFAULTS = {
+    "num_interactions": 3,
+    "num_neighbors": 20,
+    "mul": 500,
+    "lmax": 4,
+    "cutoff": 4.0,
+    "basis": "gaussian",
+    "num_basis": 20,
+}
+
+
+class ChargE3NetWrapper(nn.Module):
+    """
+    Wrapper around the official E3DensityModel.
+
+    Instantiates the model with hyperparameters matching the pre-trained
+    Materials Project checkpoint, and provides utilities for loading weights
+    and running the forward pass.
+
+    Parameters
+    ----------
+    ckpt_path : str or None
+        Path to a pre-trained checkpoint (.pt file). If provided, weights
+        are loaded immediately.
+    model_kwargs : dict
+        Override any of the default MP hyperparameters.
+    """
+
+    def __init__(self, ckpt_path: str = None, **model_kwargs):
+        super().__init__()
+
+        # Merge user overrides with MP defaults
+        params = {**MP_MODEL_DEFAULTS, **model_kwargs}
+        self.model = E3DensityModel(**params)
+        self.cutoff = params["cutoff"]
+
+        if ckpt_path is not None:
+            self.load_pretrained(ckpt_path)
+
+    def load_pretrained(self, ckpt_path: str):
+        """
+        Load pre-trained weights from a checkpoint file.
+
+        Handles three formats:
+        1. Legacy PyTorch Lightning: state_dict keys prefixed with "network."
+        2. New charge3net format: checkpoint["model"] contains the state_dict.
+        3. Raw state_dict: the file IS the state_dict.
+
+        Note: weights_only=False is required for the legacy PL format which
+        stores non-tensor objects. The checkpoint is our own internal file
+        (AIforGreatGood/charge3net repo), not untrusted user input.
+        """
+        ckpt_path = Path(ckpt_path)
+        if not ckpt_path.exists():
+            raise FileNotFoundError(f"Checkpoint not found: {ckpt_path}")
+
+        checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
+
+        if isinstance(checkpoint, dict):
+            if "pytorch-lightning_version" in checkpoint:
+                # Legacy PL format: keys like "network.atom_model.interactions.0...."
+                state_dict = {
+                    k.replace("network.", ""): v
+                    for k, v in checkpoint["state_dict"].items()
+                }
+                print(f"Loaded legacy PL checkpoint from {ckpt_path}")
+            elif "model" in checkpoint:
+                # New charge3net trainer format
+                state_dict = checkpoint["model"]
+                print(f"Loaded charge3net checkpoint from {ckpt_path}")
+            elif "state_dict" in checkpoint:
+                # Generic PL format without version key
+                state_dict = {
+                    k.replace("network.", ""): v
+                    for k, v in checkpoint["state_dict"].items()
+                }
+                print(f"Loaded state_dict checkpoint from {ckpt_path}")
+            else:
+                # Assume the dict IS the state_dict
+                state_dict = checkpoint
+                print(f"Loaded raw state_dict from {ckpt_path}")
+        else:
+            raise ValueError(f"Unexpected checkpoint format: {type(checkpoint)}")
+
+        missing, unexpected = self.model.load_state_dict(state_dict, strict=False)
+        if missing:
+            print(f"  Warning: {len(missing)} missing keys: {missing[:5]}...")
+        if unexpected:
+            print(f"  Warning: {len(unexpected)} unexpected keys: {unexpected[:5]}...")
+
+    def forward(self, input_dict: dict) -> torch.Tensor:
+        """
+        Run the E3DensityModel forward pass.
+
+        Parameters
+        ----------
+        input_dict : dict
+            Batch dict with keys matching charge3net's expected format:
+            nodes, atom_xyz, cell, atom_edges, atom_edges_displacement,
+            num_nodes, num_atom_edges, probe_xyz, probe_edges,
+            probe_edges_displacement, num_probes, num_probe_edges.
+
+        Returns
+        -------
+        torch.Tensor
+            Predicted density at probe points, shape [B, max_probes].
+        """
+        return self.model(input_dict)
diff --git a/charge3net_ft/train.py b/charge3net_ft/train.py
new file mode 100644
index 0000000..c30b277
--- /dev/null
+++ b/charge3net_ft/train.py
@@ -0,0 +1,488 @@
+"""
+Training script for fine-tuning ChargE3Net on LeMatRho charge density data.
+
+Usage:
+    python -m charge3net_ft.train \
+        --parquet-dir /path/to/lematrho_full_10x10x10 \
+        --ckpt-path /path/to/charge3net/models/charge3net_mp.pt \
+        --epochs 50
+
+    # Or set LEMATRHO_DATA_DIR env var and omit --parquet-dir.
+
+To do a quick smoke test (1 batch, no checkpoint):
+    python -m charge3net_ft.train \
+        --parquet-dir /path/to/lematrho_full_10x10x10 \
+        --smoke-test
+"""
+
+import argparse
+import os
+import random
+import sys
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+import wandb
+from dotenv import load_dotenv
+
+# ---------------------------------------------------------------------------
+# charge3net imports (for the LR scheduler)
+# ---------------------------------------------------------------------------
+_CHARGE3NET_ROOT = Path(__file__).resolve().parent.parent.parent / "charge3net"
+if not _CHARGE3NET_ROOT.exists():
+    raise RuntimeError(
+        f"charge3net repo not found at {_CHARGE3NET_ROOT}.\n"
+        "Clone it with: git clone https://github.com/AIforGreatGood/charge3net "
+        f"{_CHARGE3NET_ROOT}"
+    )
+if str(_CHARGE3NET_ROOT) not in sys.path:
+    sys.path.insert(0, str(_CHARGE3NET_ROOT))
+
+from src.charge3net.models.scheduler import PowerDecayScheduler  # noqa: E402
+
+from .data import build_dataloaders  # noqa: E402
+from .model import ChargE3NetWrapper  # noqa: E402
+
+
+def _probe_mask(targets: torch.Tensor, num_probes: torch.Tensor) -> torch.Tensor:
+    """Boolean mask [B, max_probes], True for real probe points (not padding)."""
+    return torch.arange(targets.shape[1], device=targets.device)[None] < num_probes[:, None]
+
+
+def compute_nmape(
+    preds: torch.Tensor, targets: torch.Tensor, num_probes: torch.Tensor = None
+) -> torch.Tensor:
+    """
+    Integral-Normalized Mean Absolute Percentage Error (%).
+
+    NMAPE = sum(|target - pred|) / sum(|target|) * 100
+
+    Computed per-sample in the batch, then averaged.
+    This is charge3net's primary validation metric.
+
+    Parameters
+    ----------
+    num_probes : torch.Tensor, optional
+        Shape [B]. If provided, masks out zero-padding before computing
+        metrics (required when samples have variable probe counts).
+    """
+    if num_probes is not None:
+        mask = _probe_mask(targets, num_probes)
+        diff = (torch.abs(targets - preds) * mask).sum(dim=1)
+        denom = (torch.abs(targets) * mask).sum(dim=1) + 1e-10
+    else:
+        diff = torch.abs(targets - preds).sum(dim=1)
+        denom = torch.abs(targets).sum(dim=1) + 1e-10
+    return (diff / denom * 100.0).mean()
+
+
+def compute_rmse(
+    preds: torch.Tensor, targets: torch.Tensor, num_probes: torch.Tensor = None
+) -> torch.Tensor:
+    """Root Mean Squared Error (e/ų), averaged over the batch."""
+    if num_probes is not None:
+        mask = _probe_mask(targets, num_probes)
+        n = mask.sum(dim=1).float()
+        mse = ((targets - preds) ** 2 * mask).sum(dim=1) / (n + 1e-10)
+    else:
+        mse = ((targets - preds) ** 2).mean(dim=1)
+    return mse.sqrt().mean()
+
+
+def compute_nrmse(
+    preds: torch.Tensor, targets: torch.Tensor, num_probes: torch.Tensor = None
+) -> torch.Tensor:
+    """Normalized RMSE (%) — RMSE / mean(|target|) * 100, per-sample then averaged."""
+    if num_probes is not None:
+        mask = _probe_mask(targets, num_probes)
+        n = mask.sum(dim=1).float()
+        mse = ((targets - preds) ** 2 * mask).sum(dim=1) / (n + 1e-10)
+        mean_abs = (torch.abs(targets) * mask).sum(dim=1) / (n + 1e-10)
+    else:
+        mse = ((targets - preds) ** 2).mean(dim=1)
+        mean_abs = torch.abs(targets).mean(dim=1)
+    rmse = mse.sqrt()
+    return (rmse / (mean_abs + 1e-10) * 100.0).mean()
+
+
+def train_one_epoch(model, train_loader, optimizer, scheduler, device, global_step,
+                    log_every=50, use_wandb=False):
+    """Run one training epoch, return (average loss, updated global_step)."""
+    model.train()
+    total_loss = 0.0
+    n_batches = 0
+
+    for i, batch in enumerate(train_loader):
+        batch = {
+            k: v.to(device) if isinstance(v, torch.Tensor) else v
+            for k, v in batch.items()
+        }
+
+        preds = model(batch)
+        loss = F.l1_loss(preds, batch["probe_target"])
+
+        optimizer.zero_grad()
+        loss.backward()
+        optimizer.step()
+        scheduler.step()
+
+        total_loss += loss.item()
+        n_batches += 1
+        global_step += 1
+
+        if (i + 1) % log_every == 0:
+            lr = optimizer.param_groups[0]["lr"]
+            print(f"  step {i+1}: loss={loss.item():.6f}  lr={lr:.2e}")
+            if use_wandb:
+                wandb.log({"train/loss_step": loss.item(), "lr": lr}, step=global_step)
+
+    return total_loss / max(n_batches, 1), global_step
+
+
+@torch.no_grad()
+def validate(model, loader, device):
+    """Run evaluation, return average L1, NMAPE, RMSE, and NRMSE."""
+    model.eval()
+    total_loss = 0.0
+    total_nmape = 0.0
+    total_rmse = 0.0
+    total_nrmse = 0.0
+    n_batches = 0
+
+    for batch in loader:
+        batch = {
+            k: v.to(device) if isinstance(v, torch.Tensor) else v
+            for k, v in batch.items()
+        }
+
+        preds = model(batch)
+        targets = batch["probe_target"]
+        num_probes = batch.get("num_probes")
+
+        total_loss += F.l1_loss(preds, targets).item()
+        total_nmape += compute_nmape(preds, targets, num_probes).item()
+        total_rmse += compute_rmse(preds, targets, num_probes).item()
+        total_nrmse += compute_nrmse(preds, targets, num_probes).item()
+        n_batches += 1
+
+    denom = max(n_batches, 1)
+    return {
+        "L1": total_loss / denom,
+        "NMAPE": total_nmape / denom,
+        "RMSE": total_rmse / denom,
+        "NRMSE": total_nrmse / denom,
+    }
+
+
+def save_checkpoint(model, optimizer, scheduler, epoch, best_nmape, global_step, path):
+    """Save training checkpoint."""
+    torch.save(
+        {
+            "epoch": epoch,
+            "model": model.model.state_dict(),
+            "optimizer": optimizer.state_dict(),
+            "scheduler": scheduler.state_dict(),
+            "best_nmape": best_nmape,
+            "global_step": global_step,
+        },
+        path,
+    )
+
+
+def load_checkpoint(path, model, optimizer, scheduler, device):
+    """Load training checkpoint, return (start_epoch, best_nmape, global_step)."""
+    ckpt = torch.load(path, map_location=device, weights_only=False)
+    model.model.load_state_dict(ckpt["model"])
+    optimizer.load_state_dict(ckpt["optimizer"])
+    scheduler.load_state_dict(ckpt["scheduler"])
+    start_epoch = ckpt["epoch"] + 1
+    best_nmape = ckpt["best_nmape"]
+    global_step = ckpt.get("global_step", 0)
+    lr = optimizer.param_groups[0]["lr"]
+    print(
+        f"Resumed from checkpoint: epoch {start_epoch}, "
+        f"best_nmape={best_nmape:.2f}%, step={global_step}, lr={lr:.2e}"
+    )
+    return start_epoch, best_nmape, global_step
+
+
+def main():
+    load_dotenv()  # load .env (WANDB_API_KEY, etc.)
+
+    parser = argparse.ArgumentParser(description="Fine-tune ChargE3Net on LeMatRho")
+    parser.add_argument(
+        "--parquet-dir",
+        type=str,
+        default=os.environ.get("LEMATRHO_DATA_DIR"),
+        help=(
+            "Directory with chunk_*.parquet files. "
+            "Defaults to $LEMATRHO_DATA_DIR env var."
+        ),
+    )
+    parser.add_argument("--ckpt-path", type=str, default=None, help="Pre-trained checkpoint (.pt)")
+    parser.add_argument("--save-dir", type=str, default="./checkpoints", help="Save directory")
+    parser.add_argument("--cutoff", type=float, default=4.0, help="Neighbor cutoff (A)")
+    parser.add_argument("--train-probes", type=int, default=200, help="Probes per sample (train)")
+    parser.add_argument("--val-probes", type=int, default=1000, help="Probes per sample (val/test)")
+    parser.add_argument("--batch-size", type=int, default=4, help="Batch size")
+    parser.add_argument("--lr", type=float, default=5e-4, help="Learning rate")
+    parser.add_argument("--epochs", type=int, default=50, help="Number of epochs")
+    parser.add_argument("--val-frac", type=float, default=0.05,
+                        help="Validation fraction. Do not change after first run.")
+    parser.add_argument("--test-frac", type=float, default=0.05,
+                        help="Test fraction (held out, evaluated once at end). "
+                             "Do not change after first run.")
+    parser.add_argument("--num-workers", type=int, default=4, help="DataLoader workers")
+    parser.add_argument("--seed", type=int, default=42, help="Random seed")
+    parser.add_argument("--log-every", type=int, default=50, help="Log every N steps")
+    parser.add_argument(
+        "--smoke-test", action="store_true", help="Run 1 forward pass and exit"
+    )
+    parser.add_argument(
+        "--overfit-single-batch",
+        action="store_true",
+        help="Overfit on a single batch to verify the model can learn",
+    )
+    parser.add_argument(
+        "--device",
+        type=str,
+        default=None,
+        help="Force device (cpu, cuda, mps). Auto-detect if not set.",
+    )
+    parser.add_argument("--resume-from", type=str, default=None,
+                        help="Path to training checkpoint (latest.pt) to resume from")
+    parser.add_argument("--wandb-project", type=str, default="lemat-rho-charge3net")
+    parser.add_argument("--wandb-entity", type=str, default="dtts")
+    parser.add_argument("--no-wandb", action="store_true", help="Disable W&B logging")
+    parser.add_argument("--wandb-mode", type=str, default="online",
+                        choices=["online", "offline", "disabled"],
+                        help="W&B mode (use 'offline' on air-gapped clusters)")
+    args = parser.parse_args()
+
+    if args.parquet_dir is None:
+        parser.error(
+            "--parquet-dir is required (or set the LEMATRHO_DATA_DIR environment variable)"
+        )
+
+    # Seed everything for reproducibility
+    torch.manual_seed(args.seed)
+    np.random.seed(args.seed)
+    random.seed(args.seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(args.seed)
+
+    # Device
+    if args.device:
+        device = torch.device(args.device)
+    elif torch.cuda.is_available():
+        device = torch.device("cuda")
+    else:
+        device = torch.device("cpu")
+    print(f"Using device: {device}")
+
+    # W&B
+    use_wandb = not args.no_wandb and not args.smoke_test
+    if use_wandb:
+        wandb.init(
+            project=args.wandb_project,
+            entity=args.wandb_entity,
+            config=vars(args),
+            settings=wandb.Settings(init_timeout=300),
+            mode=args.wandb_mode,
+        )
+
+    # Data
+    print("Building dataloaders...")
+    train_loader, val_loader, test_loader = build_dataloaders(
+        parquet_dir=args.parquet_dir,
+        cutoff=args.cutoff,
+        train_probes=args.train_probes,
+        val_probes=args.val_probes,
+        batch_size=args.batch_size,
+        val_frac=args.val_frac,
+        test_frac=args.test_frac,
+        num_workers=args.num_workers,
+        seed=args.seed,
+    )
+    print(
+        f"Train: {len(train_loader.dataset)} samples, "
+        f"Val: {len(val_loader.dataset)} samples, "
+        f"Test: {len(test_loader.dataset)} samples"
+    )
+
+    # Model
+    print("Initializing ChargE3Net...")
+    model = ChargE3NetWrapper(ckpt_path=args.ckpt_path, cutoff=args.cutoff)
+    model = model.to(device)
+    n_params = sum(p.numel() for p in model.parameters())
+    print(f"Model parameters: {n_params:,}")
+
+    # Smoke test: just run one forward pass
+    if args.smoke_test:
+        print("\n--- Smoke test ---")
+        model.eval()
+        batch = next(iter(train_loader))
+        batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()}
+        print(f"Batch keys: {list(batch.keys())}")
+        for k, v in batch.items():
+            if isinstance(v, torch.Tensor):
+                print(f"  {k}: shape={v.shape} dtype={v.dtype}")
+        with torch.no_grad():
+            preds = model(batch)
+        print(f"Predictions shape: {preds.shape}")
+        loss = F.l1_loss(preds, batch["probe_target"])
+        print(f"L1 loss: {loss.item():.6f}")
+        print("Smoke test passed.")
+        return
+
+    # Optimizer
+    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
+
+    # -----------------------------------------------------------------------
+    # Single-batch overfit test
+    # -----------------------------------------------------------------------
+    if args.overfit_single_batch:
+        print("\n--- Single-batch overfit test ---")
+        print(f"lr={args.lr}  epochs={args.epochs}  probes={args.train_probes}")
+
+        # Fetch exactly one batch and pin it to device
+        fixed_batch = next(iter(train_loader))
+        fixed_batch = {
+            k: v.to(device) if isinstance(v, torch.Tensor) else v
+            for k, v in fixed_batch.items()
+        }
+        n_atoms = fixed_batch["num_nodes"].tolist()
+        n_probes = fixed_batch["num_probes"].tolist()
+        print(f"Batch: {len(n_atoms)} samples, atoms={n_atoms}, probes={n_probes}")
+
+        model.train()
+        for epoch in range(1, args.epochs + 1):
+            preds = model(fixed_batch)
+            targets = fixed_batch["probe_target"]
+            num_probes = fixed_batch.get("num_probes")
+            loss = F.l1_loss(preds, targets)
+            optimizer.zero_grad()
+            loss.backward()
+            optimizer.step()
+
+            nmape = compute_nmape(preds, targets, num_probes)
+            rmse = compute_rmse(preds, targets, num_probes)
+            nrmse = compute_nrmse(preds, targets, num_probes)
+            print(
+                f"Epoch {epoch:>4d}/{args.epochs}  L1={loss.item():.6f}  "
+                f"NMAPE={nmape.item():.2f}%  RMSE={rmse.item():.4f}  NRMSE={nrmse.item():.2f}%"
+            )
+            if use_wandb:
+                wandb.log({
+                    "overfit/L1": loss.item(),
+                    "overfit/NMAPE": nmape.item(),
+                    "overfit/RMSE": rmse.item(),
+                    "overfit/NRMSE": nrmse.item(),
+                    "epoch": epoch,
+                })
+
+        print("\nOverfit test complete.")
+        if use_wandb:
+            wandb.finish()
+        return
+
+    # -----------------------------------------------------------------------
+    # Normal training loop
+    # -----------------------------------------------------------------------
+    # charge3net's power decay scheduler: lr = alpha^(step/beta)
+    # For fine-tuning we use a gentler decay (higher beta = slower decay)
+    scheduler = PowerDecayScheduler(optimizer, alpha=0.96, beta=5e4)
+
+    save_dir = Path(args.save_dir)
+    save_dir.mkdir(parents=True, exist_ok=True)
+    best_nmape = float("inf")
+
+    global_step = 0
+    start_epoch = 0
+
+    if args.resume_from:
+        start_epoch, best_nmape, global_step = load_checkpoint(
+            args.resume_from, model, optimizer, scheduler, device,
+        )
+
+    print(f"\nStarting training from epoch {start_epoch + 1} to {args.epochs}...")
+    for epoch in range(start_epoch, args.epochs):
+        t0 = time.time()
+        train_loss, global_step = train_one_epoch(
+            model, train_loader, optimizer, scheduler, device, global_step,
+            log_every=args.log_every, use_wandb=use_wandb,
+        )
+        val = validate(model, val_loader, device)
+        elapsed = time.time() - t0
+
+        print(
+            f"Epoch {epoch+1}/{args.epochs}  "
+            f"train_L1={train_loss:.6f}  "
+            f"val_L1={val['L1']:.6f}  "
+            f"val_NMAPE={val['NMAPE']:.2f}%  "
+            f"val_RMSE={val['RMSE']:.4f}  "
+            f"val_NRMSE={val['NRMSE']:.2f}%  "
+            f"time={elapsed:.0f}s"
+        )
+
+        if use_wandb:
+            wandb.log({
+                "train/L1": train_loss,
+                "val/L1": val["L1"],
+                "val/NMAPE": val["NMAPE"],
+                "val/RMSE": val["RMSE"],
+                "val/NRMSE": val["NRMSE"],
+                "epoch": epoch + 1,
+            }, step=global_step)
+
+        # Save best checkpoint (selected on val NMAPE)
+        if val["NMAPE"] < best_nmape:
+            best_nmape = val["NMAPE"]
+            save_checkpoint(
+                model, optimizer, scheduler, epoch, best_nmape, global_step,
+                save_dir / "best.pt",
+            )
+            print(f"  -> New best val NMAPE: {best_nmape:.2f}%")
+
+        # Save latest checkpoint every epoch (for SLURM resumption)
+        save_checkpoint(
+            model, optimizer, scheduler, epoch, best_nmape, global_step,
+            save_dir / "latest.pt",
+        )
+
+    # -----------------------------------------------------------------------
+    # Test set evaluation — run once at the end using the best checkpoint.
+    # These numbers are the uncontaminated held-out performance estimate.
+    # -----------------------------------------------------------------------
+    print("\nLoading best checkpoint for test evaluation...")
+    best_ckpt_path = save_dir / "best.pt"
+    if best_ckpt_path.exists():
+        load_checkpoint(best_ckpt_path, model, optimizer, scheduler, device)
+    else:
+        print("  Warning: best.pt not found, using current model weights.")
+
+    test = validate(model, test_loader, device)
+    print(
+        f"\nTest set results (best.pt, held-out):\n"
+        f"  L1={test['L1']:.6f}  NMAPE={test['NMAPE']:.2f}%  "
+        f"RMSE={test['RMSE']:.4f}  NRMSE={test['NRMSE']:.2f}%"
+    )
+    if use_wandb:
+        wandb.log({
+            "test/L1": test["L1"],
+            "test/NMAPE": test["NMAPE"],
+            "test/RMSE": test["RMSE"],
+            "test/NRMSE": test["NRMSE"],
+        })
+
+    print(f"\nTraining complete. Best val NMAPE: {best_nmape:.2f}%")
+    print(f"Checkpoints saved to {save_dir}")
+    if use_wandb:
+        wandb.finish()
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index 0309bcf..08f1061 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,14 @@ dependencies = [
     "material-hasher",
     "atomate2",
     "fireworks",
+    "torch>=2.0",
+    "numpy>=1.24",
+    "e3nn>=0.5.0",
+    "scipy>=1.10.0",
+    "lz4>=4.0.0",
+    "pyarrow>=14.0.0",
+    "wandb>=0.16.0",
+    "python-dotenv>=1.0.0",
 ]
 
 [tool.uv.sources]
diff --git a/submit_charge3net.sh b/submit_charge3net.sh
new file mode 100755
index 0000000..dab0f56
--- /dev/null
+++ b/submit_charge3net.sh
@@ -0,0 +1,48 @@
+#!/bin/bash
+#SBATCH --job-name=charge3net_ft
+#SBATCH --account=dqo@a100
+#SBATCH -C a100
+#SBATCH --gres=gpu:1
+#SBATCH --cpus-per-task=8
+#SBATCH --hint=nomultithread
+#SBATCH --time=20:00:00
+#SBATCH --qos=qos_gpu_a100-t3
+#SBATCH --output=%x_%j.out
+#SBATCH --error=%x_%j.err
+
+set -eo pipefail
+
+# --- Environment ---
+source /etc/profile
+eval "$($WORK/miniforge3/bin/conda shell.bash hook)"
+conda activate uma12
+
+cd $SCRATCH/LeMat-Rho
+
+# Load W&B API key and other secrets from .env
+set -a; source .env; set +a
+export PYTHONUNBUFFERED=1
+
+# --- Train ---
+RESUME_FLAG=""
+if [ -f "$SCRATCH/charge3net_checkpoints/latest.pt" ]; then
+    RESUME_FLAG="--resume-from $SCRATCH/charge3net_checkpoints/latest.pt"
+    echo "Resuming from $SCRATCH/charge3net_checkpoints/latest.pt"
+fi
+
+python -m charge3net_ft.train \
+    --parquet-dir $SCRATCH/charge3net_data/lematrho_full_10x10x10 \
+    --ckpt-path $SCRATCH/charge3net/models/charge3net_mp.pt \
+    --save-dir $SCRATCH/charge3net_checkpoints \
+    --epochs 50 \
+    --batch-size 4 \
+    --lr 5e-4 \
+    --train-probes 200 \
+    --val-probes 1000 \
+    --num-workers 8 \
+    --wandb-project lemat-rho-charge3net \
+    --wandb-entity dtts \
+    --wandb-mode offline \
+    $RESUME_FLAG
+
+echo "Done. Exit code: $?"
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_data.py b/tests/test_data.py
new file mode 100644
index 0000000..4ef046b
--- /dev/null
+++ b/tests/test_data.py
@@ -0,0 +1,147 @@
+"""
+Unit tests for charge3net_ft data utilities.
+
+Uses synthetic in-memory data — no real Parquet files, no charge3net dep.
+"""
+
+import json
+import tempfile
+from pathlib import Path
+
+import numpy as np
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pytest
+
+
+# ---------------------------------------------------------------------------
+# We test only the pure utility functions that don't import charge3net.
+# Import them by reaching into the module after patching the sys.path block.
+# ---------------------------------------------------------------------------
+def _import_data_utils():
+    """Import _parse_grid_json and _row_to_atoms_and_density without triggering
+    the charge3net RuntimeError (which fires if the sibling repo is absent)."""
+    import importlib
+    import sys
+    from unittest.mock import patch
+
+    # Stub out the charge3net modules so the import succeeds without the repo
+    fake_modules = [
+        "src", "src.charge3net", "src.charge3net.data",
+        "src.charge3net.data.collate", "src.charge3net.data.graph_construction",
+        "src.utils", "src.utils.data",
+    ]
+    stubs = {}
+    for mod in fake_modules:
+        stubs[mod] = type(sys)("mod")
+    stubs["src.charge3net.data.collate"].collate_list_of_dicts = lambda *a, **kw: None
+    stubs["src.charge3net.data.graph_construction"].KdTreeGraphConstructor = object
+    stubs["src.utils.data"].calculate_grid_pos = lambda *a, **kw: None
+
+    with patch.dict(sys.modules, stubs):
+        # Also patch the existence check so it doesn't raise
+        with patch("pathlib.Path.exists", return_value=True):
+            import importlib
+            # Force reimport with stubs in place
+            if "charge3net_ft.data" in sys.modules:
+                del sys.modules["charge3net_ft.data"]
+            mod = importlib.import_module("charge3net_ft.data")
+    return mod
+
+
+class TestParseGridJson:
+    def test_roundtrip_3d(self):
+        grid = [[[1.0, 2.0], [3.0, 4.0]], [[5.0, 6.0], [7.0, 8.0]]]
+        json_str = json.dumps(grid)
+        from charge3net_ft.data import _parse_grid_json
+        result = _parse_grid_json(json_str)
+        assert result.shape == (2, 2, 2)
+        assert result.dtype == np.float32
+        np.testing.assert_allclose(result, np.array(grid, dtype=np.float32))
+
+    def test_10x10x10(self):
+        from charge3net_ft.data import _parse_grid_json
+        grid = np.random.rand(10, 10, 10).tolist()
+        result = _parse_grid_json(json.dumps(grid))
+        assert result.shape == (10, 10, 10)
+
+
+class TestRowToAtomsAndDensity:
+    def _make_row(self):
+        return {
+            "species_at_sites": ["Fe", "O"],
+            "cartesian_site_positions": [[0.0, 0.0, 0.0], [1.4, 1.4, 1.4]],
+            "lattice_vectors": [[4.0, 0.0, 0.0], [0.0, 4.0, 0.0], [0.0, 0.0, 4.0]],
+            "compressed_charge_density": json.dumps(np.ones((10, 10, 10)).tolist()),
+        }
+
+    def test_atoms_species(self):
+        import ase
+        from charge3net_ft.data import _row_to_atoms_and_density
+        row = self._make_row()
+        atoms, density, origin = _row_to_atoms_and_density(row)
+        assert isinstance(atoms, ase.Atoms)
+        assert list(atoms.get_chemical_symbols()) == ["Fe", "O"]
+
+    def test_pbc(self):
+        from charge3net_ft.data import _row_to_atoms_and_density
+        atoms, _, _ = _row_to_atoms_and_density(self._make_row())
+        assert all(atoms.pbc)
+
+    def test_density_shape(self):
+        from charge3net_ft.data import _row_to_atoms_and_density
+        _, density, _ = _row_to_atoms_and_density(self._make_row())
+        assert density.shape == (10, 10, 10)
+
+    def test_origin_is_zero(self):
+        from charge3net_ft.data import _row_to_atoms_and_density
+        _, _, origin = _row_to_atoms_and_density(self._make_row())
+        np.testing.assert_array_equal(origin, [0.0, 0.0, 0.0])
+
+    def test_unknown_species_raises(self):
+        from charge3net_ft.data import _row_to_atoms_and_density
+        row = self._make_row()
+        row["species_at_sites"] = ["Xx"]  # invalid symbol
+        with pytest.raises(KeyError):
+            _row_to_atoms_and_density(row)
+
+
+class TestBuildParquetIndex:
+    def _write_chunk(self, path: Path, n_valid: int, n_null: int):
+        """Write a synthetic chunk_*.parquet file."""
+        valid = [json.dumps(np.ones((10, 10, 10)).tolist())] * n_valid
+        null = [None] * n_null
+        table = pa.table({
+            "compressed_charge_density": pa.array(valid + null, type=pa.string()),
+            "species_at_sites": pa.array([["Fe"]] * (n_valid + n_null)),
+            "cartesian_site_positions": pa.array([[[0.0, 0.0, 0.0]]] * (n_valid + n_null)),
+            "lattice_vectors": pa.array([[[4.0, 0.0, 0.0], [0.0, 4.0, 0.0], [0.0, 0.0, 4.0]]] * (n_valid + n_null)),
+        })
+        pq.write_table(table, path)
+
+    def test_counts_valid_rows(self):
+        from charge3net_ft.data import _build_parquet_index
+        with tempfile.TemporaryDirectory() as tmp:
+            d = Path(tmp)
+            self._write_chunk(d / "chunk_000.parquet", n_valid=5, n_null=2)
+            self._write_chunk(d / "chunk_001.parquet", n_valid=3, n_null=1)
+            file_paths, index = _build_parquet_index(d)
+            assert len(index) == 8  # 5 + 3 valid
+            assert len(file_paths) == 2
+
+    def test_index_entries_reference_correct_file(self):
+        from charge3net_ft.data import _build_parquet_index
+        with tempfile.TemporaryDirectory() as tmp:
+            d = Path(tmp)
+            self._write_chunk(d / "chunk_000.parquet", n_valid=3, n_null=0)
+            self._write_chunk(d / "chunk_001.parquet", n_valid=2, n_null=0)
+            _, index = _build_parquet_index(d)
+            file_indices = [fi for fi, _ in index]
+            assert file_indices[:3] == [0, 0, 0]
+            assert file_indices[3:] == [1, 1]
+
+    def test_raises_on_empty_dir(self):
+        from charge3net_ft.data import _build_parquet_index
+        with tempfile.TemporaryDirectory() as tmp:
+            with pytest.raises(FileNotFoundError):
+                _build_parquet_index(Path(tmp))
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..853f34e
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,91 @@
+"""
+Unit tests for charge3net_ft metric functions.
+
+These tests use synthetic tensors with known ground-truth values and do not
+require real data, GPU, or the charge3net dependency.
+"""
+
+import pytest
+import torch
+
+# Import directly so tests work without the charge3net sibling repo installed
+import sys
+
+# Patch the charge3net path check so we can import train.py without the repo
+sys.modules.setdefault("src", type(sys)("src"))
+
+# We import the metric functions directly by exec'ing only the relevant parts
+# of train.py to avoid triggering the charge3net sys.path block at module level.
+from charge3net_ft.train import compute_nmape, compute_nrmse, compute_rmse  # noqa: E402
+
+
+class TestComputeNmape:
+    def test_perfect_prediction(self):
+        t = torch.tensor([[1.0, 2.0, 3.0]])
+        assert compute_nmape(t, t).item() == pytest.approx(0.0, abs=1e-5)
+
+    def test_known_value(self):
+        # preds all zero, targets = [2, 2] → NMAPE = 4/(4) * 100 = 100%
+        preds = torch.zeros(1, 2)
+        targets = torch.tensor([[2.0, 2.0]])
+        assert compute_nmape(preds, targets).item() == pytest.approx(100.0, rel=1e-4)
+
+    def test_batch_average(self):
+        # Sample 1: 0% error; Sample 2: 100% error → average 50%
+        preds = torch.tensor([[1.0, 1.0], [0.0, 0.0]])
+        targets = torch.tensor([[1.0, 1.0], [1.0, 1.0]])
+        assert compute_nmape(preds, targets).item() == pytest.approx(50.0, rel=1e-4)
+
+    def test_with_mask_ignores_padding(self):
+        # Probe 3 is padding (zero); should give same result as without it
+        preds = torch.tensor([[0.0, 0.0, 0.0]])
+        targets = torch.tensor([[2.0, 2.0, 0.0]])
+        num_probes = torch.tensor([2])
+        masked = compute_nmape(preds, targets, num_probes).item()
+        assert masked == pytest.approx(100.0, rel=1e-4)
+
+    def test_mask_vs_no_mask_differ_when_padding_nonzero(self):
+        # Without mask, zero-padding in targets dilutes the denominator
+        preds = torch.tensor([[0.0, 0.0, 0.0]])
+        targets = torch.tensor([[2.0, 2.0, 100.0]])  # row 3 is "padding"
+        num_probes = torch.tensor([2])
+        masked = compute_nmape(preds, targets, num_probes).item()
+        unmasked = compute_nmape(preds, targets).item()
+        assert masked != pytest.approx(unmasked, rel=1e-2)
+
+
+class TestComputeRmse:
+    def test_perfect_prediction(self):
+        t = torch.tensor([[1.0, 2.0]])
+        assert compute_rmse(t, t).item() == pytest.approx(0.0, abs=1e-5)
+
+    def test_known_value(self):
+        # preds = [0], targets = [3] → MSE = 9 → RMSE = 3
+        preds = torch.zeros(1, 1)
+        targets = torch.tensor([[3.0]])
+        assert compute_rmse(preds, targets).item() == pytest.approx(3.0, rel=1e-4)
+
+    def test_with_mask(self):
+        # Same as above but with a padding element appended
+        preds = torch.zeros(1, 2)
+        targets = torch.tensor([[3.0, 999.0]])
+        num_probes = torch.tensor([1])
+        assert compute_rmse(preds, targets, num_probes).item() == pytest.approx(3.0, rel=1e-4)
+
+
+class TestComputeNrmse:
+    def test_perfect_prediction(self):
+        t = torch.tensor([[2.0, 4.0]])
+        assert compute_nrmse(t, t).item() == pytest.approx(0.0, abs=1e-5)
+
+    def test_known_value(self):
+        # preds = [0, 0], targets = [2, 2] → RMSE = 2, mean(|target|) = 2 → NRMSE = 100%
+        preds = torch.zeros(1, 2)
+        targets = torch.tensor([[2.0, 2.0]])
+        assert compute_nrmse(preds, targets).item() == pytest.approx(100.0, rel=1e-4)
+
+    def test_with_mask(self):
+        preds = torch.zeros(1, 3)
+        targets = torch.tensor([[2.0, 2.0, 999.0]])
+        num_probes = torch.tensor([2])
+        assert compute_nrmse(preds, targets, num_probes).item() == pytest.approx(100.0, rel=1e-4)
diff --git a/tests/test_model.py b/tests/test_model.py
new file mode 100644
index 0000000..d9d1bde
--- /dev/null
+++ b/tests/test_model.py
@@ -0,0 +1,100 @@
+"""
+Unit tests for ChargE3NetWrapper checkpoint loading.
+
+Tests all three checkpoint format branches using mock state dicts,
+without requiring the real charge3net repo or a GPU.
+"""
+
+import pytest
+import torch
+import torch.nn as nn
+from unittest.mock import patch
+import sys
+import tempfile
+
+
+def _make_mock_e3density():
+    """Return a tiny 2-param nn.Module standing in for E3DensityModel."""
+    class _Tiny(nn.Module):
+        def __init__(self, **kwargs):
+            super().__init__()
+            self.w = nn.Parameter(torch.zeros(2))
+        def forward(self, x):
+            return x
+    return _Tiny
+
+
+def _import_wrapper():
+    """Import ChargE3NetWrapper with charge3net stubbed out."""
+    fake = {
+        "src": type(sys)("src"),
+        "src.charge3net": type(sys)("src.charge3net"),
+        "src.charge3net.models": type(sys)("src.charge3net.models"),
+        "src.charge3net.models.e3": type(sys)("src.charge3net.models.e3"),
+    }
+    MockModel = _make_mock_e3density()
+    fake["src.charge3net.models.e3"].E3DensityModel = MockModel
+
+    with patch.dict(sys.modules, fake):
+        with patch("pathlib.Path.exists", return_value=True):
+            if "charge3net_ft.model" in sys.modules:
+                del sys.modules["charge3net_ft.model"]
+            import importlib
+            mod = importlib.import_module("charge3net_ft.model")
+    return mod.ChargE3NetWrapper, MockModel
+
+
+class TestLoadPretrained:
+    def _save_and_load(self, checkpoint, wrapper_cls):
+        with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f:
+            path = f.name
+        torch.save(checkpoint, path)
+        w = wrapper_cls()
+        w.load_pretrained(path)
+        return w
+
+    def test_new_charge3net_format(self):
+        WrapperCls, MockModel = _import_wrapper()
+        state_dict = MockModel().state_dict()
+        ckpt = {"model": state_dict}
+        w = self._save_and_load(ckpt, WrapperCls)
+        # Should load without errors
+        assert w is not None
+
+    def test_legacy_pl_format(self):
+        WrapperCls, MockModel = _import_wrapper()
+        state_dict = {f"network.{k}": v for k, v in MockModel().state_dict().items()}
+        ckpt = {
+            "pytorch-lightning_version": "1.9.0",
+            "state_dict": state_dict,
+        }
+        w = self._save_and_load(ckpt, WrapperCls)
+        assert w is not None
+
+    def test_generic_state_dict_format(self):
+        WrapperCls, MockModel = _import_wrapper()
+        state_dict = {f"network.{k}": v for k, v in MockModel().state_dict().items()}
+        ckpt = {"state_dict": state_dict}
+        w = self._save_and_load(ckpt, WrapperCls)
+        assert w is not None
+
+    def test_raw_state_dict_format(self):
+        WrapperCls, MockModel = _import_wrapper()
+        ckpt = MockModel().state_dict()  # the dict IS the state_dict
+        w = self._save_and_load(ckpt, WrapperCls)
+        assert w is not None
+
+    def test_missing_file_raises(self):
+        WrapperCls, _ = _import_wrapper()
+        w = WrapperCls()
+        with pytest.raises(FileNotFoundError):
+            w.load_pretrained("/nonexistent/path/checkpoint.pt")
+
+    def test_unexpected_format_raises(self):
+        WrapperCls, _ = _import_wrapper()
+        with tempfile.NamedTemporaryFile(suffix=".pt", delete=False) as f:
+            path = f.name
+        torch.save("not_a_dict", path)
+        w = WrapperCls()
+        with pytest.raises(ValueError):
+            w.load_pretrained(path)
diff --git a/uv.lock b/uv.lock
index 2f0e990..ace9d7d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,10 +1,14 @@
 version = 1
-revision = 2
+revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12' and sys_platform == 'win32'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform == 'win32'",
+    "python_full_version >= '3.12' and platform_python_implementation == 'PyPy' and sys_platform == 'win32'",
     "python_full_version < '3.12' and sys_platform == 'win32'",
-    "python_full_version >= '3.12' and sys_platform != 'win32'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy' and sys_platform != 'win32'",
+    "python_full_version >= '3.12' and platform_python_implementation == 'PyPy' and sys_platform != 'win32'",
     "python_full_version < '3.12' and sys_platform != 'win32'",
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+wheels = [
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 ]
 
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+wheels = [
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+]
+
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+    { name = "custodian" },
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+    { name = "jobflow" },
+    { name = "monty" },
+    { name = "numpy" },
+    { name = "pydantic" },
+    { name = "pydantic-settings" },
+    { name = "pymatgen" },
+    { name = "pymongo" },
+    { name = "pyyaml" },
+]
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