diff --git a/configs/data/default.yaml b/configs/data/default.yaml
index 9d79061..e604360 100644
--- a/configs/data/default.yaml
+++ b/configs/data/default.yaml
@@ -23,12 +23,18 @@ max_val_lm_samples: null # Maximum number of samples for LM validation (null = u
 seed: 42
 
 # Evaluation datasets (optional)
-# Set evals: null to disable all evals, or configure specific evals below
-evals:
-  traitgym_mendelian_promoter:
-    dataset_name: songlab/TraitGym
-    dataset_config: mendelian_traits
-    genome_url: https://ftp.ensembl.org/pub/release-115/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz
-    genome_path: data/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz
-    window_size: 512
-    batch_size: 128
+# Set evals: null to disable all evals, or configure specific evals in dataset-specific configs
+# Example structure:
+# evals:
+#   - name: eval_name
+#     dataset_name: songlab/TraitGym
+#     dataset_config: mendelian_traits
+#     split: test # Dataset split to load (default: "test")
+#     genome_url: https://ftp.ensembl.org/...
+#     filter_name: traitgym_promoter # Filter from EVAL_FILTERS registry (default: "none")
+#     window_size: 512
+#     batch_size: 128
+#     label_column: label # Column to preserve as labels (default: "label")
+#     transform: minus # Transform to apply to raw LLR: minus, identity, abs (default: identity)
+#     metrics: [auprc] # Metrics to compute: auprc, auroc, spearman, pearson (default: [auprc])
+evals: null
diff --git a/configs/data/gpn_animal_promoter.yaml b/configs/data/gpn_animal_promoter.yaml
index f4a5bcd..7f20cbd 100644
--- a/configs/data/gpn_animal_promoter.yaml
+++ b/configs/data/gpn_animal_promoter.yaml
@@ -4,3 +4,17 @@ defaults:
 batch_size: 2048 # Total effective batch size
 per_device_batch_size: 128 # Batch size per device (adjust based on GPU memory)
 num_workers: 8
+
+# Evaluation datasets
+evals:
+  - name: traitgym_mendelian_promoter
+    dataset_name: songlab/TraitGym
+    dataset_config: mendelian_traits
+    split: test # Dataset split to load (default: "test")
+    genome_url: https://ftp.ensembl.org/pub/release-115/fasta/homo_sapiens/dna/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz
+    filter_name: traitgym_promoter
+    window_size: 512
+    batch_size: 128
+    label_column: label # Column to preserve as labels (default: "label")
+    transform: minus # Transform to apply to raw LLR: minus, identity, abs (default: identity)
+    metrics: [auprc] # Metrics to compute: auprc, auroc, spearman, pearson (default: [auprc])
diff --git a/configs/data/plants.yaml b/configs/data/plants.yaml
new file mode 100644
index 0000000..d527a47
--- /dev/null
+++ b/configs/data/plants.yaml
@@ -0,0 +1,24 @@
+defaults:
+  - default
+
+# Training dataset: Angiosperm 16 genomes
+dataset_name: kuleshov-group/Angiosperm_16_genomes
+
+# Batch size configuration
+batch_size: 2048 # Total effective batch size
+per_device_batch_size: 128 # Batch size per device (adjust based on GPU memory)
+num_workers: 8
+
+# Evaluation datasets
+evals:
+  - name: maize_af
+    dataset_name: plantcad/maize-allele-frequency
+    dataset_config: null
+    split: validation
+    genome_url: https://ftp.ensemblgenomes.ebi.ac.uk/pub/plants/release-62/fasta/zea_mays/dna/Zea_mays.Zm-B73-REFERENCE-NAM-5.0.dna_sm.toplevel.fa.gz
+    filter_name: none
+    window_size: 512
+    batch_size: 128
+    label_column: AF # Allele frequency column
+    transform: identity # No transform for regression (default: identity)
+    metrics: [pearson, spearman] # Correlation metrics for regression task
diff --git a/configs/experiment/clm_transformer_small.yaml b/configs/experiment/clm_transformer_small.yaml
index b2b623a..6639509 100644
--- a/configs/experiment/clm_transformer_small.yaml
+++ b/configs/experiment/clm_transformer_small.yaml
@@ -3,6 +3,7 @@
 # Short training run with small Transformer encoder for quick testing
 
 defaults:
+  - override /data: plants
   - override /model: clm_transformer_small
 
 logger:
diff --git a/configs/model/bert_bytenet_small.yaml b/configs/model/bert_bytenet_small.yaml
index 97bfdb1..d649039 100644
--- a/configs/model/bert_bytenet_small.yaml
+++ b/configs/model/bert_bytenet_small.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.MLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.MLM
diff --git a/configs/model/clm_transformer_base.yaml b/configs/model/clm_transformer_base.yaml
index caae309..df2f3d9 100644
--- a/configs/model/clm_transformer_base.yaml
+++ b/configs/model/clm_transformer_base.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.CLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.CLM
diff --git a/configs/model/clm_transformer_small.yaml b/configs/model/clm_transformer_small.yaml
index c98eddd..3147cd6 100644
--- a/configs/model/clm_transformer_small.yaml
+++ b/configs/model/clm_transformer_small.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.CLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.CLM
diff --git a/configs/model/gpn_animal_promoter.yaml b/configs/model/gpn_animal_promoter.yaml
index 50d4bc5..f06021a 100644
--- a/configs/model/gpn_animal_promoter.yaml
+++ b/configs/model/gpn_animal_promoter.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.MLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.MLM
diff --git a/configs/model/mlm_transformer_base.yaml b/configs/model/mlm_transformer_base.yaml
index 3f08d35..f877b9d 100644
--- a/configs/model/mlm_transformer_base.yaml
+++ b/configs/model/mlm_transformer_base.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.MLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.MLM
diff --git a/configs/model/mlm_transformer_small.yaml b/configs/model/mlm_transformer_small.yaml
index f829eac..11d8119 100644
--- a/configs/model/mlm_transformer_small.yaml
+++ b/configs/model/mlm_transformer_small.yaml
@@ -1,6 +1,7 @@
 _target_: glm_experiments.models.lm_lit_module.MLMLitModule
 
 soft_masked_weight: ${data.soft_masked_weight}
+evals: ${data.evals}
 
 net:
   _target_: glm_experiments.models.components.lm.MLM
diff --git a/glm_experiments/data/evals.py b/glm_experiments/data/evals.py
new file mode 100644
index 0000000..9073f31
--- /dev/null
+++ b/glm_experiments/data/evals.py
@@ -0,0 +1,186 @@
+"""Evaluation dataset loading for variant effect prediction.
+
+This module provides functions to load and transform evaluation datasets
+(TraitGym, PlantCAD, etc.) for variant effect prediction during training.
+
+Supports configurable dataset-specific filtering via a registry pattern.
+"""
+
+import logging
+import urllib.request
+from functools import partial
+from pathlib import Path
+
+import pandas as pd
+from biofoundation.data import Genome, transform_llr_clm, transform_llr_mlm
+from biofoundation.model.base import Tokenizer
+from datasets import Dataset, load_dataset
+
+log = logging.getLogger(__name__)
+
+
+def filter_traitgym_promoter(dataset: Dataset) -> Dataset:
+    """Filter TraitGym dataset to non-exonic proximal promoter variants.
+
+    Args:
+        dataset: TraitGym dataset from HuggingFace
+
+    Returns:
+        Filtered dataset containing only non-exonic proximal promoter variants
+    """
+    log.info("Filtering to non-exonic proximal promoter variants...")
+    subset_url = (
+        "https://huggingface.co/datasets/songlab/TraitGym/resolve/main/"
+        "mendelian_traits_matched_9/subset/nonexonic_AND_proximal.parquet"
+    )
+    V = dataset.to_pandas()
+    subset = pd.read_parquet(subset_url)
+    V = V.merge(subset, on=["chrom", "pos", "ref", "alt"], how="inner")
+    log.info(f"Filtered dataset size: {len(V)} variants (from {len(dataset)})")
+    return Dataset.from_pandas(V, preserve_index=False)
+
+
+# Registry mapping filter names to filter functions
+EVAL_FILTERS = {
+    "traitgym_promoter": filter_traitgym_promoter,
+    "none": lambda dataset: dataset,  # No-op filter
+}
+
+
+def download_genome(url: str, data_dir: str | Path = "data") -> Path:
+    """Download reference genome if not already present.
+
+    Path is auto-derived from URL basename (e.g., genome.fa.gz).
+
+    Args:
+        url: URL to download genome from (e.g., Ensembl FTP)
+        data_dir: Directory to save genome (default: "data")
+
+    Returns:
+        Path to the downloaded genome file
+    """
+    path = Path(data_dir) / Path(url).name
+    if path.exists():
+        log.info(f"Genome already exists at {path}, skipping download")
+        return path
+
+    log.info(f"Downloading genome from {url} to {path}...")
+    path.parent.mkdir(parents=True, exist_ok=True)
+    urllib.request.urlretrieve(url, path)  # nosec B310
+    log.info(f"Genome download complete: {path}")
+    return path
+
+
+def load_eval_dataset(
+    tokenizer: Tokenizer,
+    dataset_name: str,
+    genome_url: str,
+    filter_name: str = "none",
+    dataset_config: str | None = None,
+    split: str = "test",
+    window_size: int = 512,
+    cache_dir: str | Path = "data/evals_cache",
+    objective: str = "mlm",
+    data_dir: str | Path = "data",
+    label_column: str = "label",
+) -> Dataset:
+    """Load and transform evaluation dataset with optional filtering.
+
+    Loads a variant dataset from HuggingFace, applies optional dataset-specific
+    filtering, and transforms it using the appropriate objective (MLM or CLM).
+
+    The Genome is only loaded if the transformed dataset is not cached.
+
+    Args:
+        tokenizer: Tokenizer implementing the biofoundation Tokenizer protocol
+        dataset_name: HuggingFace dataset name (e.g., "songlab/TraitGym")
+        genome_url: URL to reference genome (path auto-derived from basename)
+        filter_name: Name of filter function in EVAL_FILTERS registry (default: "none")
+        dataset_config: Dataset configuration (e.g., "mendelian_traits")
+        split: Dataset split to load (default: "test")
+        window_size: Size of the window around variants (must be even)
+        cache_dir: Directory to cache transformed dataset
+        objective: Training objective ("mlm" or "clm") - determines transform function
+        data_dir: Directory for genome downloads (default: "data")
+        label_column: Name of the label column to preserve (default: "label")
+
+    Returns:
+        Transformed dataset with columns: input_ids, pos, ref, alt, {label_column}
+
+    Raises:
+        ValueError: If filter_name not in EVAL_FILTERS or objective not mlm/clm
+    """
+    from datasets import load_from_disk
+
+    # Validate filter name
+    if filter_name not in EVAL_FILTERS:
+        raise ValueError(
+            f"Unknown filter_name: {filter_name}. " f"Must be one of {list(EVAL_FILTERS.keys())}"
+        )
+
+    # Create cache path based on config, filter, split, and objective
+    cache_name_parts = [dataset_name.replace("/", "_")]
+    if dataset_config:
+        cache_name_parts.append(dataset_config)
+    cache_name_parts.extend([split, filter_name, f"window{window_size}", objective])
+    cache_name = "_".join(cache_name_parts)
+    cache_path = Path(cache_dir) / cache_name
+
+    # Check if cached transformed dataset exists
+    if cache_path.exists():
+        log.info(f"Loading cached evaluation dataset from {cache_path}")
+        dataset = load_from_disk(str(cache_path))
+        dataset.set_format(type="torch")
+        return dataset
+
+    # Not cached - need to transform with genome
+    log.info(f"Loading evaluation dataset: {dataset_name}")
+    if dataset_config:
+        log.info(f"  Dataset config: {dataset_config}")
+    log.info(f"  Split: {split}")
+    dataset = load_dataset(dataset_name, dataset_config, split=split)  # nosec B615
+
+    # Apply dataset-specific filtering
+    if filter_name != "none":
+        log.info(f"  Applying filter: {filter_name}")
+    dataset = EVAL_FILTERS[filter_name](dataset)
+
+    # Download genome (auto-derives path from URL)
+    genome_path = download_genome(genome_url, data_dir)
+    log.info(f"Loading reference genome from {genome_path} (this may take a minute)...")
+    genome = Genome(genome_path)
+
+    # Keep original columns for evaluation
+    original_columns = dataset.column_names
+
+    # Select transform function based on objective
+    if objective == "mlm":
+        transform_func = transform_llr_mlm
+    elif objective == "clm":
+        transform_func = transform_llr_clm
+    else:
+        raise ValueError(f"Unknown objective: {objective}. Must be 'mlm' or 'clm'.")
+
+    transform_fn = partial(
+        transform_func,
+        tokenizer=tokenizer,
+        genome=genome,
+        window_size=window_size,
+    )
+
+    # Transform the dataset
+    log.info("Transforming evaluation dataset...")
+    dataset = dataset.map(
+        transform_fn,
+        remove_columns=[c for c in original_columns if c != label_column],
+    )
+
+    # Save to cache
+    log.info(f"Saving transformed dataset to {cache_path}")
+    cache_path.parent.mkdir(parents=True, exist_ok=True)
+    dataset.save_to_disk(str(cache_path))
+
+    # Set format to PyTorch tensors for proper DataLoader collation
+    dataset.set_format(type="torch")
+
+    return dataset
diff --git a/glm_experiments/data/lm_datamodule.py b/glm_experiments/data/lm_datamodule.py
index a7d4297..430a7de 100644
--- a/glm_experiments/data/lm_datamodule.py
+++ b/glm_experiments/data/lm_datamodule.py
@@ -12,10 +12,7 @@
 from torch.utils.data import DataLoader, default_collate
 from transformers import AutoTokenizer
 
-from glm_experiments.data.traitgym import (
-    download_genome,
-    load_traitgym_mendelian_promoter_dataset,
-)
+from glm_experiments.data.evals import download_genome, load_eval_dataset
 from glm_experiments.utils.pylogger import RankedLogger
 
 log = RankedLogger(__name__, rank_zero_only=True)
@@ -155,7 +152,8 @@ def __init__(
         self.tokenizer = None
         self.data_train = None
         self.data_val = None
-        self.data_val_traitgym_mendelian_promoter = None
+        # Dynamic eval datasets (keyed by eval_name from config)
+        self.eval_datasets: dict[str, Any] = {}
 
     def apply_labels(self, input_ids: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """Apply objective-specific label creation (override in subclasses).
@@ -182,13 +180,12 @@ def prepare_data(self) -> None:
         # Download tokenizer only
         AutoTokenizer.from_pretrained(self.hparams.tokenizer_name)  # nosec B615
 
-        # Download reference genome for TraitGym Mendelian Promoter evaluation if configured
-        evals = self.hparams.get("evals") or {}
-        traitgym_cfg = evals.get("traitgym_mendelian_promoter")
-        if traitgym_cfg:
+        # Download genomes for all configured eval datasets
+        evals = self.hparams.get("evals") or []
+        for eval_cfg in evals:
             download_genome(
-                url=traitgym_cfg["genome_url"],
-                path=traitgym_cfg["genome_path"],
+                url=eval_cfg["genome_url"],
+                data_dir=eval_cfg.get("data_dir", "data"),
             )
 
     def setup(self, stage: str | None = None) -> None:
@@ -340,19 +337,22 @@ def transform_batch(examples: dict, data_aug: bool) -> dict:
             self.data_train = train_dataset
             self.data_val = val_dataset
 
-            # Load TraitGym Mendelian Promoter evaluation dataset if configured
-            evals = self.hparams.get("evals") or {}
-            traitgym_cfg = evals.get("traitgym_mendelian_promoter")
-            if traitgym_cfg:
-                self.data_val_traitgym_mendelian_promoter = (
-                    load_traitgym_mendelian_promoter_dataset(
-                        tokenizer=HFTokenizer(self.tokenizer),
-                        genome_path=traitgym_cfg["genome_path"],
-                        dataset_name=traitgym_cfg.get("dataset_name", "songlab/TraitGym"),
-                        dataset_config=traitgym_cfg.get("dataset_config", "mendelian_traits"),
-                        window_size=traitgym_cfg.get("window_size", 512),
-                        objective=self.get_objective(),
-                    )
+            # Load all configured eval datasets dynamically
+            evals = self.hparams.get("evals") or []
+            for eval_cfg in evals:
+                eval_name = eval_cfg["name"]
+                log.info(f"Loading eval dataset: {eval_name}")
+                self.eval_datasets[eval_name] = load_eval_dataset(
+                    tokenizer=HFTokenizer(self.tokenizer),
+                    dataset_name=eval_cfg["dataset_name"],
+                    genome_url=eval_cfg["genome_url"],
+                    filter_name=eval_cfg.get("filter_name", "none"),
+                    dataset_config=eval_cfg.get("dataset_config"),
+                    split=eval_cfg.get("split", "test"),
+                    window_size=eval_cfg.get("window_size", 512),
+                    objective=self.get_objective(),
+                    data_dir=eval_cfg.get("data_dir", "data"),
+                    label_column=eval_cfg.get("label_column", "label"),
                 )
 
     def train_dataloader(self) -> DataLoader:
@@ -369,10 +369,10 @@ def train_dataloader(self) -> DataLoader:
     def val_dataloader(self) -> DataLoader | list[DataLoader]:
         """Create validation dataloader(s).
 
-        Returns a single dataloader for MLM validation, or a list of dataloaders
-        if TraitGym evaluation is configured: [mlm_val_loader, traitgym_loader].
+        Returns a single dataloader for LM validation, or a list of dataloaders
+        if eval datasets are configured: [lm_val_loader, eval_loader_1, eval_loader_2, ...].
         """
-        mlm_val_loader = DataLoader(
+        lm_val_loader = DataLoader(
             dataset=self.data_val,
             batch_size=self.batch_size_per_device,
             num_workers=self.hparams.num_workers,
@@ -381,23 +381,28 @@ def val_dataloader(self) -> DataLoader | list[DataLoader]:
             collate_fn=default_collate,
         )
 
-        if self.data_val_traitgym_mendelian_promoter is not None:
-            # Get batch size from config, default to 128
-            evals = self.hparams.get("evals") or {}
-            traitgym_cfg = evals.get("traitgym_mendelian_promoter", {})
-            traitgym_batch_size = traitgym_cfg.get("batch_size", 128)
-
-            traitgym_loader = DataLoader(
-                dataset=self.data_val_traitgym_mendelian_promoter,
-                batch_size=traitgym_batch_size,
-                num_workers=self.hparams.num_workers,
-                pin_memory=self.hparams.pin_memory,
-                shuffle=False,
-                collate_fn=default_collate,
+        if not self.eval_datasets:
+            return lm_val_loader
+
+        # Create dataloaders for all eval datasets
+        eval_loaders = [lm_val_loader]
+        evals = self.hparams.get("evals") or []
+        eval_dict = {e["name"]: e for e in evals}
+
+        for eval_name, eval_dataset in self.eval_datasets.items():
+            eval_cfg = eval_dict[eval_name]
+            eval_loaders.append(
+                DataLoader(
+                    dataset=eval_dataset,
+                    batch_size=eval_cfg.get("batch_size", 128),
+                    num_workers=self.hparams.num_workers,
+                    pin_memory=self.hparams.pin_memory,
+                    shuffle=False,
+                    collate_fn=default_collate,
+                )
             )
-            return [mlm_val_loader, traitgym_loader]
 
-        return mlm_val_loader
+        return eval_loaders
 
 
 class MLMDataModule(LMDataModule):
diff --git a/glm_experiments/data/traitgym.py b/glm_experiments/data/traitgym.py
deleted file mode 100644
index f121305..0000000
--- a/glm_experiments/data/traitgym.py
+++ /dev/null
@@ -1,135 +0,0 @@
-"""TraitGym Mendelian Promoter variant dataset loading for evaluation.
-
-This module provides functions to load and transform the TraitGym dataset
-(filtered to non-exonic proximal promoter variants) for variant effect
-prediction evaluation during training.
-"""
-
-import logging
-import urllib.request
-from functools import partial
-from pathlib import Path
-
-import pandas as pd
-from biofoundation.data import Genome, transform_llr_clm, transform_llr_mlm
-from biofoundation.model.base import Tokenizer
-from datasets import Dataset, load_dataset
-
-log = logging.getLogger(__name__)
-
-
-def download_genome(url: str, path: str | Path) -> Path:
-    """Download reference genome if not already present.
-
-    Args:
-        url: URL to download genome from (e.g., Ensembl FTP)
-        path: Local path to save the genome file
-
-    Returns:
-        Path to the downloaded genome file
-    """
-    path = Path(path)
-    if path.exists():
-        return path
-
-    path.parent.mkdir(parents=True, exist_ok=True)
-    urllib.request.urlretrieve(url, path)  # nosec B310
-    return path
-
-
-def load_traitgym_mendelian_promoter_dataset(
-    tokenizer: Tokenizer,
-    genome_path: str | Path,
-    dataset_name: str = "songlab/TraitGym",
-    dataset_config: str = "mendelian_traits",
-    window_size: int = 512,
-    cache_dir: str | Path = "data/traitgym_cache",
-    objective: str = "mlm",
-) -> Dataset:
-    """Load and transform TraitGym Mendelian Promoter dataset for variant effect prediction.
-
-    Loads the TraitGym dataset from HuggingFace, filters to non-exonic proximal
-    promoter variants, and applies the appropriate transform (transform_llr_mlm or
-    transform_llr_clm) based on the objective.
-
-    The Genome is only loaded if the transformed dataset is not cached.
-
-    Args:
-        tokenizer: Tokenizer implementing the biofoundation Tokenizer protocol
-        genome_path: Path to the reference genome file
-        dataset_name: HuggingFace dataset name
-        dataset_config: Dataset configuration (e.g., "mendelian_traits")
-        window_size: Size of the window around variants (must be even)
-        cache_dir: Directory to cache transformed dataset
-        objective: Training objective ("mlm" or "clm") - determines transform function
-
-    Returns:
-        Transformed dataset with columns: input_ids, pos, ref, alt, label
-    """
-    from datasets import load_from_disk
-
-    # Create cache path based on config and objective (different transforms need separate caches)
-    cache_path = (
-        Path(cache_dir) / f"{dataset_config}_mendelian_promoter_window{window_size}_{objective}"
-    )
-
-    # Check if cached transformed dataset exists
-    if cache_path.exists():
-        log.info(f"Loading cached TraitGym Mendelian Promoter dataset from {cache_path}")
-        dataset = load_from_disk(str(cache_path))
-        dataset.set_format(type="torch")
-        return dataset
-
-    # Not cached - need to transform with genome
-    log.info(f"Loading TraitGym dataset: {dataset_name}/{dataset_config}")
-    dataset = load_dataset(dataset_name, dataset_config, split="test")  # nosec B615
-
-    # Filter to non-exonic proximal promoter variants
-    log.info("Filtering to non-exonic proximal promoter variants...")
-    subset_url = (
-        "https://huggingface.co/datasets/songlab/TraitGym/resolve/main/"
-        "mendelian_traits_matched_9/subset/nonexonic_AND_proximal.parquet"
-    )
-    V = dataset.to_pandas()
-    subset = pd.read_parquet(subset_url)
-    V = V.merge(subset, on=["chrom", "pos", "ref", "alt"], how="inner")
-    log.info(f"Filtered dataset size: {len(V)} variants (from {len(dataset)})")
-    dataset = Dataset.from_pandas(V, preserve_index=False)
-
-    log.info(f"Loading reference genome from {genome_path} (this may take a minute)...")
-    genome = Genome(genome_path)
-
-    # We need to keep the label column for evaluation
-    original_columns = dataset.column_names
-
-    # Select transform function based on objective
-    if objective == "mlm":
-        transform_func = transform_llr_mlm
-    elif objective == "clm":
-        transform_func = transform_llr_clm
-    else:
-        raise ValueError(f"Unknown objective: {objective}. Must be 'mlm' or 'clm'.")
-
-    transform_fn = partial(
-        transform_func,
-        tokenizer=tokenizer,
-        genome=genome,
-        window_size=window_size,
-    )
-
-    # Transform the dataset
-    log.info("Transforming TraitGym Mendelian Promoter dataset...")
-    dataset = dataset.map(
-        transform_fn,
-        remove_columns=[c for c in original_columns if c != "label"],
-    )
-
-    # Save to cache
-    log.info(f"Saving transformed dataset to {cache_path}")
-    cache_path.parent.mkdir(parents=True, exist_ok=True)
-    dataset.save_to_disk(str(cache_path))
-
-    # Set format to PyTorch tensors for proper DataLoader collation
-    dataset.set_format(type="torch")
-
-    return dataset
diff --git a/glm_experiments/models/lm_lit_module.py b/glm_experiments/models/lm_lit_module.py
index 7cc8437..a707e52 100644
--- a/glm_experiments/models/lm_lit_module.py
+++ b/glm_experiments/models/lm_lit_module.py
@@ -65,10 +65,11 @@ def __init__(
         optimizer: torch.optim.Optimizer,
         scheduler: torch.optim.lr_scheduler.LRScheduler,
         soft_masked_weight: float = 0.01,
+        evals: dict[str, dict] | None = None,
     ):
         super().__init__()
 
-        self.save_hyperparameters(logger=False)
+        self.save_hyperparameters(logger=False, ignore=["net"])
 
         self.net = net
         self.soft_masked_weight = soft_masked_weight
@@ -77,9 +78,45 @@ def __init__(
         self.adapter = self.create_adapter(net)
         self.loss_name = self.get_loss_name()
 
-        # CatMetrics for TraitGym Mendelian Promoter VEP evaluation
-        self.traitgym_mendelian_promoter_labels = CatMetric()
-        self.traitgym_mendelian_promoter_scores = CatMetric()
+        # Initialize dynamic eval metrics from config
+        self._eval_names: list[str] = []
+        self._eval_metrics: dict[str, dict] = {}
+
+        if evals:
+            self._setup_eval_metrics(evals)
+
+    def _setup_eval_metrics(self, evals: list[dict]) -> None:
+        """Initialize eval metrics from configuration.
+
+        Args:
+            evals: List of eval configurations (each with 'name' field)
+        """
+        from glm_experiments.utils.metrics import get_metric, get_transform
+
+        # Store eval names in list order (guaranteed consistent)
+        self._eval_names = [e["name"] for e in evals]
+
+        # Create CatMetrics and config for each eval
+        for eval_config in evals:
+            eval_name = eval_config["name"]
+
+            # Validate transform exists
+            transform_name = eval_config.get("transform", "identity")
+            get_transform(transform_name)  # Raises ValueError if invalid
+
+            # Validate metrics exist
+            metrics = eval_config.get("metrics", ["auprc"])
+            for metric_name in metrics:
+                get_metric(metric_name)  # Raises ValueError if invalid
+
+            # Store config with CatMetrics
+            self._eval_metrics[eval_name] = {
+                "scores": CatMetric(),
+                "labels": CatMetric(),
+                "transform": transform_name,
+                "metrics": metrics,
+                "label_column": eval_config.get("label_column", "label"),
+            }
 
     def create_adapter(self, net: nn.Module) -> nn.Module:
         """Create biofoundation adapter (override in subclasses).
@@ -100,17 +137,6 @@ def get_loss_name(self) -> str:
         """
         raise NotImplementedError("Subclasses must implement get_loss_name")
 
-    def compute_vep_scores(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
-        """Compute variant effect prediction scores (override in subclasses).
-
-        Args:
-            batch: Batch dict from TraitGym dataset (fields vary by objective)
-
-        Returns:
-            Pathogenicity scores (higher = more pathogenic)
-        """
-        raise NotImplementedError("Subclasses must implement compute_vep_scores")
-
     def forward(self, input_ids: torch.Tensor, labels: torch.Tensor, soft_masked: torch.Tensor):
         """Forward pass through model."""
         return self.net(input_ids, labels, soft_masked, self.soft_masked_weight)
@@ -160,12 +186,12 @@ def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> torch
     def validation_step(
         self, batch: dict[str, torch.Tensor], batch_idx: int, dataloader_idx: int = 0
     ) -> None:
-        """Validation step for LM and TraitGym Mendelian Promoter dataloaders.
+        """Validation step for LM and eval dataloaders.
 
         Args:
             batch: Batch dict (keys depend on dataloader)
             batch_idx: Batch index
-            dataloader_idx: 0 for LM validation, 1 for TraitGym Mendelian Promoter
+            dataloader_idx: 0 for LM validation, 1+ for eval datasets
         """
         if dataloader_idx == 0:
             # LM validation
@@ -197,29 +223,87 @@ def validation_step(
                 add_dataloader_idx=False,
                 sync_dist=True,
             )
-        elif dataloader_idx == 1:
-            # TraitGym Mendelian Promoter VEP evaluation
-            # Data comes as PyTorch tensors from HuggingFace dataset with set_format("torch")
-            # Use objective-specific scoring function (override in subclasses)
-            scores = self.compute_vep_scores(batch)
-            self.traitgym_mendelian_promoter_scores.update(scores)
-            self.traitgym_mendelian_promoter_labels.update(batch["label"])
+        else:
+            # Dynamic eval dataset handling
+            # dataloader_idx 1+ map to eval datasets in config order
+            eval_idx = dataloader_idx - 1
+
+            if eval_idx >= len(self._eval_names):
+                raise IndexError(
+                    f"dataloader_idx={dataloader_idx} exceeds configured evals. "
+                    f"Expected indices 0-{len(self._eval_names)} for {len(self._eval_names)} eval(s)."
+                )
+
+            eval_name = self._eval_names[eval_idx]
+            eval_config = self._eval_metrics[eval_name]
+
+            # Compute raw LLR scores using objective-specific method
+            raw_scores = self._compute_raw_llr(batch)
+
+            # Apply transform
+            from glm_experiments.utils.metrics import get_transform
+
+            transform_fn = get_transform(eval_config["transform"])
+            transformed_scores = transform_fn(raw_scores)
+
+            # Update CatMetrics
+            label_col = eval_config["label_column"]
+            eval_config["scores"].update(transformed_scores)
+            eval_config["labels"].update(batch[label_col])
+
+    def _compute_raw_llr(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Compute raw LLR scores without transformation.
+
+        This is the inverse of the old compute_vep_scores() - it returns raw LLR
+        without the hardcoded negation.
+
+        Args:
+            batch: Batch dict from eval dataset (fields vary by objective)
+
+        Returns:
+            Raw LLR scores (higher = more likely under model)
+        """
+        raise NotImplementedError("Subclasses must implement _compute_raw_llr")
 
     def on_validation_epoch_end(self) -> None:
-        """Compute and log TraitGym Mendelian Promoter AUPRC at end of validation epoch."""
-        # Only compute if we have TraitGym Mendelian Promoter data
-        if self.traitgym_mendelian_promoter_scores.update_count > 0:
-            scores = self.traitgym_mendelian_promoter_scores.compute().cpu().numpy()
-            labels = self.traitgym_mendelian_promoter_labels.compute().cpu().numpy()
+        """Compute and log metrics for all eval datasets."""
+        from glm_experiments.utils.metrics import get_metric
 
-            auprc = average_precision_score(labels, scores)
+        for eval_name, eval_config in self._eval_metrics.items():
+            # Skip if no data collected
+            if eval_config["scores"].update_count == 0:
+                continue
+
+            # Compute accumulated scores and labels
+            scores = eval_config["scores"].compute().cpu().numpy()
+            labels = eval_config["labels"].compute().cpu().numpy()
             sample_size = len(labels)
-            log.info(f"TraitGym Mendelian Promoter: sample_size={sample_size}, AUPRC={auprc:.4f}")
-            self.log("val/traitgym_mendelian_promoter_auprc", auprc, prog_bar=True)
+
+            # Compute and log each metric
+            for metric_name in eval_config["metrics"]:
+                metric_fn = get_metric(metric_name)
+                try:
+                    metric_value = metric_fn(labels, scores)
+
+                    # Log to wandb/logger
+                    log_key = f"val/{eval_name}_{metric_name}"
+                    self.log(
+                        log_key,
+                        metric_value,
+                        prog_bar=(metric_name == eval_config["metrics"][0]),
+                    )
+
+                    # Log to console
+                    log.info(
+                        f"{eval_name}: sample_size={sample_size}, "
+                        f"{metric_name.upper()}={metric_value:.4f}"
+                    )
+                except Exception as e:
+                    log.error(f"Failed to compute {metric_name} for {eval_name}: {e}")
 
             # Reset metrics for next epoch
-            self.traitgym_mendelian_promoter_scores.reset()
-            self.traitgym_mendelian_promoter_labels.reset()
+            eval_config["scores"].reset()
+            eval_config["labels"].reset()
 
     def configure_optimizers(self) -> dict[str, Any]:
         """Configure optimizer and scheduler."""
@@ -257,27 +341,22 @@ def get_loss_name(self) -> str:
         """Return MLM loss metric name."""
         return "mlm_loss"
 
-    def compute_vep_scores(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
-        """Compute MLM variant effect prediction scores.
-
-        Uses biofoundation's compute_llr_mlm function.
+    def _compute_raw_llr(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Compute raw MLM LLR scores (no transformation).
 
         Args:
             batch: Batch with keys {input_ids, pos, ref, alt, label}
 
         Returns:
-            Pathogenicity scores (higher = more pathogenic)
+            Raw LLR scores (higher = more likely under model)
         """
-        # Compute LLR using biofoundation's MLM scorer
-        llr = compute_llr_mlm(
+        return compute_llr_mlm(
             model=self.adapter,
             input_ids=batch["input_ids"],
             pos=batch["pos"],
             ref=batch["ref"],
             alt=batch["alt"],
         )
-        # Negate LLR for pathogenicity score (higher LLR = more likely, but we want score for pathogenic)
-        return -1 * llr
 
 
 class CLMLitModule(LMLitModule):
@@ -297,22 +376,16 @@ def get_loss_name(self) -> str:
         """Return CLM loss metric name."""
         return "clm_loss"
 
-    def compute_vep_scores(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
-        """Compute CLM variant effect prediction scores.
-
-        Uses biofoundation's compute_llr_clm function.
+    def _compute_raw_llr(self, batch: dict[str, torch.Tensor]) -> torch.Tensor:
+        """Compute raw CLM LLR scores (no transformation).
 
         Args:
             batch: Batch with keys {input_ids, label} where input_ids is shape [B, 2, L]
 
         Returns:
-            Pathogenicity scores (higher = more pathogenic)
+            Raw LLR scores (higher = more likely under model)
         """
-        # Compute LLR using biofoundation's CLM scorer
-        # For CLM, input_ids has shape [B, 2, L] where the 2 sequences are [ref, alt]
-        llr = compute_llr_clm(
+        return compute_llr_clm(
             model=self.adapter,
             input_ids=batch["input_ids"],
         )
-        # Negate LLR for pathogenicity score (higher LLR = more likely, but we want score for pathogenic)
-        return -1 * llr
diff --git a/glm_experiments/utils/metrics.py b/glm_experiments/utils/metrics.py
new file mode 100644
index 0000000..ee9cc80
--- /dev/null
+++ b/glm_experiments/utils/metrics.py
@@ -0,0 +1,158 @@
+"""Metrics and transformations for variant effect prediction."""
+
+import numpy as np
+import torch
+from scipy.stats import pearsonr, spearmanr
+from sklearn.metrics import average_precision_score, roc_auc_score
+
+# ============================================================================
+# TRANSFORM FUNCTIONS
+# ============================================================================
+
+
+def transform_minus(scores: torch.Tensor) -> torch.Tensor:
+    """Negate scores (higher LLR = more likely → higher score = more pathogenic).
+
+    Args:
+        scores: Raw LLR scores from model
+
+    Returns:
+        Negated scores
+    """
+    return -1 * scores
+
+
+def transform_identity(scores: torch.Tensor) -> torch.Tensor:
+    """No transformation (use raw scores).
+
+    Args:
+        scores: Raw LLR scores from model
+
+    Returns:
+        Unchanged scores
+    """
+    return scores
+
+
+def transform_abs(scores: torch.Tensor) -> torch.Tensor:
+    """Absolute value (magnitude of effect).
+
+    Args:
+        scores: Raw LLR scores from model
+
+    Returns:
+        Absolute value of scores
+    """
+    return torch.abs(scores)
+
+
+# Registry: string name -> function
+TRANSFORMS = {
+    "minus": transform_minus,
+    "identity": transform_identity,
+    "abs": transform_abs,
+}
+
+
+def get_transform(name: str):
+    """Get transform function by name.
+
+    Args:
+        name: Transform name (e.g., "minus", "identity", "abs")
+
+    Returns:
+        Transform function
+
+    Raises:
+        ValueError: If transform name not found
+    """
+    if name not in TRANSFORMS:
+        available = ", ".join(sorted(TRANSFORMS.keys()))
+        raise ValueError(f"Unknown transform: '{name}'. Available: {available}")
+    return TRANSFORMS[name]
+
+
+# ============================================================================
+# METRIC FUNCTIONS
+# ============================================================================
+
+
+def metric_auprc(labels: np.ndarray, scores: np.ndarray) -> float:
+    """Compute AUPRC (Average Precision).
+
+    Args:
+        labels: True binary labels
+        scores: Predicted scores
+
+    Returns:
+        AUPRC value
+    """
+    return average_precision_score(labels, scores)
+
+
+def metric_auroc(labels: np.ndarray, scores: np.ndarray) -> float:
+    """Compute AUROC (ROC AUC).
+
+    Args:
+        labels: True binary labels
+        scores: Predicted scores
+
+    Returns:
+        AUROC value
+    """
+    return roc_auc_score(labels, scores)
+
+
+def metric_spearman(labels: np.ndarray, scores: np.ndarray) -> float:
+    """Compute Spearman correlation.
+
+    Args:
+        labels: True continuous values
+        scores: Predicted scores
+
+    Returns:
+        Spearman correlation coefficient
+    """
+    corr, _ = spearmanr(scores, labels)
+    return corr
+
+
+def metric_pearson(labels: np.ndarray, scores: np.ndarray) -> float:
+    """Compute Pearson correlation.
+
+    Args:
+        labels: True continuous values
+        scores: Predicted scores
+
+    Returns:
+        Pearson correlation coefficient
+    """
+    corr, _ = pearsonr(scores, labels)
+    return corr
+
+
+# Registry: string name -> function
+METRICS = {
+    "auprc": metric_auprc,
+    "auroc": metric_auroc,
+    "spearman": metric_spearman,
+    "pearson": metric_pearson,
+}
+
+
+def get_metric(name: str):
+    """Get metric function by name.
+
+    Args:
+        name: Metric name (e.g., "auprc", "auroc", "spearman", "pearson")
+
+    Returns:
+        Metric function
+
+    Raises:
+        ValueError: If metric name not found
+    """
+    if name not in METRICS:
+        available = ", ".join(sorted(METRICS.keys()))
+        raise ValueError(f"Unknown metric: '{name}'. Available: {available}")
+    return METRICS[name]
diff --git a/tests/test_traitgym.py b/tests/test_evals.py
similarity index 66%
rename from tests/test_traitgym.py
rename to tests/test_evals.py
index 02adc71..21fcf19 100644
--- a/tests/test_traitgym.py
+++ b/tests/test_evals.py
@@ -1,4 +1,4 @@
-"""Tests for TraitGym Mendelian Promoter variant dataset loading."""
+"""Tests for evaluation dataset loading."""
 
 import tempfile
 from pathlib import Path
@@ -7,11 +7,14 @@
 import pytest
 import torch
 from biofoundation.model.adapters.hf import HFTokenizer
+from datasets import Dataset
 from transformers import AutoTokenizer
 
-from glm_experiments.data.traitgym import (
+from glm_experiments.data.evals import (
+    EVAL_FILTERS,
     download_genome,
-    load_traitgym_mendelian_promoter_dataset,
+    filter_traitgym_promoter,
+    load_eval_dataset,
 )
 
 
@@ -50,15 +53,35 @@ def test_mask_token_id(self, hf_tokenizer):
         assert adapter.mask_token_id == hf_tokenizer.mask_token_id
 
 
+class TestEvalFilters:
+    """Tests for evaluation dataset filter functions."""
+
+    def test_filter_registry_contains_expected_filters(self):
+        """Test that EVAL_FILTERS registry contains expected filter names."""
+        assert "traitgym_promoter" in EVAL_FILTERS
+        assert "none" in EVAL_FILTERS
+        assert callable(EVAL_FILTERS["traitgym_promoter"])
+        assert callable(EVAL_FILTERS["none"])
+
+    def test_none_filter_is_identity(self):
+        """Test that 'none' filter returns dataset unchanged."""
+
+        # Create a simple test dataset
+        data = {"col1": [1, 2, 3], "col2": ["a", "b", "c"]}
+        dataset = Dataset.from_dict(data)
+
+        filtered = EVAL_FILTERS["none"](dataset)
+
+        assert filtered is dataset  # Should be same object (identity)
+        assert len(filtered) == len(dataset)
+
+
 class TestDownloadGenome:
     """Tests for download_genome function."""
 
-    def test_download_genome_creates_file(self):
-        """Test that download_genome creates a file at the specified path."""
+    def test_download_genome_auto_derives_path_from_url(self):
+        """Test that download_genome auto-derives path from URL basename."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            # Create a mock URL and test file
-            test_path = Path(tmpdir) / "test_genome.fa.gz"
-
             # Mock urlretrieve to create a dummy file
             with patch("urllib.request.urlretrieve") as mock_retrieve:
 
@@ -67,27 +90,32 @@ def create_file(url, path):
 
                 mock_retrieve.side_effect = create_file
 
-                result = download_genome("http://example.com/genome.fa.gz", test_path)
+                result = download_genome("http://example.com/test_genome.fa.gz", tmpdir)
 
-                assert result == test_path
-                mock_retrieve.assert_called_once()
+                # Check that path is auto-derived from URL
+                expected_path = Path(tmpdir) / "test_genome.fa.gz"
+                assert result == expected_path
+                mock_retrieve.assert_called_once_with(
+                    "http://example.com/test_genome.fa.gz", expected_path
+                )
 
     def test_download_genome_skips_if_exists(self):
         """Test that download_genome skips download if file exists."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            test_path = Path(tmpdir) / "existing_genome.fa.gz"
-            test_path.touch()
+            # Create existing file
+            existing_file = Path(tmpdir) / "existing_genome.fa.gz"
+            existing_file.touch()
 
             with patch("urllib.request.urlretrieve") as mock_retrieve:
-                result = download_genome("http://example.com/genome.fa.gz", test_path)
+                result = download_genome("http://example.com/existing_genome.fa.gz", tmpdir)
 
-                assert result == test_path
+                assert result == existing_file
                 mock_retrieve.assert_not_called()
 
     def test_download_genome_creates_parent_dirs(self):
         """Test that download_genome creates parent directories."""
         with tempfile.TemporaryDirectory() as tmpdir:
-            test_path = Path(tmpdir) / "nested" / "dir" / "genome.fa.gz"
+            nested_dir = Path(tmpdir) / "nested" / "dir"
 
             with patch("urllib.request.urlretrieve") as mock_retrieve:
 
@@ -96,20 +124,14 @@ def create_file(url, path):
 
                 mock_retrieve.side_effect = create_file
 
-                result = download_genome("http://example.com/genome.fa.gz", test_path)
-
-                assert test_path.parent.exists()
+                result = download_genome("http://example.com/genome.fa.gz", nested_dir)
 
+                assert nested_dir.exists()
+                assert result == nested_dir / "genome.fa.gz"
 
-class TestLoadTraitgymMendelianPromoterDataset:
-    """Tests for load_traitgym_mendelian_promoter_dataset function.
 
-    Note: Tests that require the real genome file are marked with @pytest.mark.slow
-    and require the genome to be downloaded first. These tests verify end-to-end
-    functionality with real TraitGym Mendelian Promoter data.
-
-    Unit tests use mocks to test the data loading logic without the genome dependency.
-    """
+class TestLoadTraitgymDataset:
+    """Tests for loading TraitGym dataset with load_eval_dataset."""
 
     @pytest.fixture
     def tokenizer_adapter(self):
@@ -119,7 +141,7 @@ def tokenizer_adapter(self):
         )  # nosec B615
         return HFTokenizer(hf_tokenizer)
 
-    def test_load_traitgym_dataset_loads_raw_data(self, tokenizer_adapter):
+    def test_load_traitgym_dataset_loads_raw_data(self):
         """Test that TraitGym dataset can be loaded from HuggingFace."""
         from datasets import load_dataset
 
@@ -136,51 +158,6 @@ def test_load_traitgym_dataset_loads_raw_data(self, tokenizer_adapter):
         # Check we have data
         assert len(dataset) > 0
 
-    @pytest.mark.slow
-    @pytest.mark.skipif(
-        not Path("data/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz").exists(),
-        reason="Reference genome not downloaded. Run prepare_data() first.",
-    )
-    def test_load_traitgym_mendelian_promoter_dataset_with_real_genome(self, tokenizer_adapter):
-        """Test load_traitgym_mendelian_promoter_dataset with real genome file.
-
-        Requires the reference genome to be downloaded first.
-        """
-        from datasets import Dataset
-
-        dataset = load_traitgym_mendelian_promoter_dataset(
-            tokenizer=tokenizer_adapter,
-            genome_path="data/Homo_sapiens.GRCh38.dna_sm.toplevel.fa.gz",
-            dataset_name="songlab/TraitGym",
-            dataset_config="mendelian_traits",
-            window_size=512,
-        )
-
-        assert isinstance(dataset, Dataset)
-
-        # Check expected columns from transform_llr_mlm
-        assert "input_ids" in dataset.column_names
-        assert "pos" in dataset.column_names
-        assert "ref" in dataset.column_names
-        assert "alt" in dataset.column_names
-        assert "label" in dataset.column_names
-
-        # Get first example and verify format
-        example = dataset[0]
-
-        # Check input_ids has correct length
-        # Note: HuggingFace datasets stores tensors as lists
-        input_ids = example["input_ids"]
-        assert len(input_ids) == 512
-
-        # Check pos is a tensor in valid range (set_format("torch") returns tensors)
-        assert isinstance(example["pos"], torch.Tensor)
-        assert example["pos"].item() == 256  # Center position for window_size=512
-
-        # Check ref and alt are tensors (token IDs)
-        assert isinstance(example["ref"], torch.Tensor)
-        assert isinstance(example["alt"], torch.Tensor)
-
 
 class TestDNADataModuleWithTraitGymMendelianPromoter:
     """Tests for DNADataModule with TraitGym Mendelian Promoter evaluation."""
@@ -211,7 +188,7 @@ def test_datamodule_evals_none_by_default(self):
         dm = MLMDataModule()
 
         assert dm.hparams.evals is None
-        assert dm.data_val_traitgym_mendelian_promoter is None
+        assert dm.eval_datasets == {}
 
     def test_val_dataloader_returns_single_loader_without_evals(self):
         """Test that val_dataloader returns single DataLoader without evals."""
@@ -227,7 +204,7 @@ def test_val_dataloader_returns_single_loader_without_evals(self):
 
         # Mock the data_val attribute
         dm.data_val = [{"input_ids": torch.zeros(10), "labels": torch.zeros(10)}]
-        dm.data_val_traitgym_mendelian_promoter = None
+        dm.eval_datasets = {}
         dm.batch_size_per_device = 32
 
         result = dm.val_dataloader()
@@ -254,9 +231,11 @@ def test_val_dataloader_returns_list_with_evals(self):
 
         # Mock the data attributes
         dm.data_val = [{"input_ids": torch.zeros(10), "labels": torch.zeros(10)}]
-        dm.data_val_traitgym_mendelian_promoter = [
-            {"input_ids": torch.zeros(512), "pos": 256, "ref": 1, "alt": 2, "label": 0}
-        ]
+        dm.eval_datasets = {
+            "traitgym_mendelian_promoter": [
+                {"input_ids": torch.zeros(512), "pos": 256, "ref": 1, "alt": 2, "label": 0}
+            ]
+        }
         dm.batch_size_per_device = 32
 
         result = dm.val_dataloader()
diff --git a/tests/test_lm_lit_module.py b/tests/test_lm_lit_module.py
index 0ce0f5a..ec9e236 100644
--- a/tests/test_lm_lit_module.py
+++ b/tests/test_lm_lit_module.py
@@ -16,7 +16,10 @@
 def mlm_lit_module():
     """Create MLMLitModule from config."""
     with initialize(version_base="1.3", config_path="../configs"):
-        cfg = compose(config_name="train", overrides=["model=bert_bytenet_small"])
+        cfg = compose(
+            config_name="train",
+            overrides=["model=bert_bytenet_small", "data=gpn_animal_promoter"],
+        )
 
     import hydra
 
@@ -144,6 +147,7 @@ def test_mlm_lit_module_save_hyperparameters():
     assert hasattr(model, "hparams")
     assert "optimizer" in model.hparams
     assert "scheduler" in model.hparams
+    assert "evals" in model.hparams  # New parameter
     # net should NOT be in hparams
     assert "net" not in model.hparams
 
@@ -179,9 +183,11 @@ def test_validation_step_traitgym_mendelian_promoter(mlm_lit_module):
     # Run validation step with dataloader_idx=1 (TraitGym Mendelian Promoter)
     mlm_lit_module.validation_step(batch, batch_idx=0, dataloader_idx=1)
 
-    # Check that metrics were updated
-    assert mlm_lit_module.traitgym_mendelian_promoter_scores.update_count == 1
-    assert mlm_lit_module.traitgym_mendelian_promoter_labels.update_count == 1
+    # Check that metrics were updated using new structure
+    eval_name = "traitgym_mendelian_promoter"
+    assert eval_name in mlm_lit_module._eval_metrics
+    assert mlm_lit_module._eval_metrics[eval_name]["scores"].update_count == 1
+    assert mlm_lit_module._eval_metrics[eval_name]["labels"].update_count == 1
 
 
 def test_on_validation_epoch_end_computes_auprc(mlm_lit_module):
@@ -190,15 +196,16 @@ def test_on_validation_epoch_end_computes_auprc(mlm_lit_module):
     scores = torch.tensor([0.1, 0.4, 0.6, 0.9])
     labels = torch.tensor([0, 0, 1, 1])
 
-    mlm_lit_module.traitgym_mendelian_promoter_scores.update(scores)
-    mlm_lit_module.traitgym_mendelian_promoter_labels.update(labels)
+    eval_name = "traitgym_mendelian_promoter"
+    mlm_lit_module._eval_metrics[eval_name]["scores"].update(scores)
+    mlm_lit_module._eval_metrics[eval_name]["labels"].update(labels)
 
     # Run epoch end
     mlm_lit_module.on_validation_epoch_end()
 
     # Metrics should be reset after computation
-    assert mlm_lit_module.traitgym_mendelian_promoter_scores.update_count == 0
-    assert mlm_lit_module.traitgym_mendelian_promoter_labels.update_count == 0
+    assert mlm_lit_module._eval_metrics[eval_name]["scores"].update_count == 0
+    assert mlm_lit_module._eval_metrics[eval_name]["labels"].update_count == 0
 
 
 def test_validation_step_lm_still_works(mlm_lit_module):
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
new file mode 100644
index 0000000..c2b6bd1
--- /dev/null
+++ b/tests/test_metrics.py
@@ -0,0 +1,124 @@
+"""Tests for metrics and transformations."""
+
+import numpy as np
+import pytest
+import torch
+
+from glm_experiments.utils.metrics import (
+    METRICS,
+    TRANSFORMS,
+    get_metric,
+    get_transform,
+    metric_auprc,
+    metric_auroc,
+    metric_pearson,
+    metric_spearman,
+    transform_abs,
+    transform_identity,
+    transform_minus,
+)
+
+
+class TestTransforms:
+    """Test transform functions."""
+
+    def test_transform_minus(self):
+        """Test negation transform."""
+        scores = torch.tensor([1.0, -2.0, 3.0])
+        result = transform_minus(scores)
+        expected = torch.tensor([-1.0, 2.0, -3.0])
+        assert torch.allclose(result, expected)
+
+    def test_transform_identity(self):
+        """Test identity transform."""
+        scores = torch.tensor([1.0, -2.0, 3.0])
+        result = transform_identity(scores)
+        assert torch.allclose(result, scores)
+
+    def test_transform_abs(self):
+        """Test absolute value transform."""
+        scores = torch.tensor([1.0, -2.0, 3.0])
+        result = transform_abs(scores)
+        expected = torch.tensor([1.0, 2.0, 3.0])
+        assert torch.allclose(result, expected)
+
+    def test_get_transform_valid(self):
+        """Test getting valid transform functions."""
+        assert get_transform("minus") == transform_minus
+        assert get_transform("identity") == transform_identity
+        assert get_transform("abs") == transform_abs
+
+    def test_get_transform_invalid(self):
+        """Test getting invalid transform raises ValueError."""
+        with pytest.raises(ValueError, match="Unknown transform: 'invalid'"):
+            get_transform("invalid")
+
+    def test_transforms_registry_complete(self):
+        """Test that TRANSFORMS registry contains all expected transforms."""
+        expected_transforms = {"minus", "identity", "abs"}
+        assert set(TRANSFORMS.keys()) == expected_transforms
+
+
+class TestMetrics:
+    """Test metric functions."""
+
+    def test_metric_auprc(self):
+        """Test AUPRC metric."""
+        labels = np.array([0, 0, 1, 1])
+        scores = np.array([0.1, 0.4, 0.35, 0.8])
+        result = metric_auprc(labels, scores)
+        assert 0.0 <= result <= 1.0
+        # Perfect separation should give high AUPRC
+        perfect_scores = np.array([0.0, 0.0, 1.0, 1.0])
+        perfect_result = metric_auprc(labels, perfect_scores)
+        assert perfect_result > 0.9
+
+    def test_metric_auroc(self):
+        """Test AUROC metric."""
+        labels = np.array([0, 0, 1, 1])
+        scores = np.array([0.1, 0.4, 0.35, 0.8])
+        result = metric_auroc(labels, scores)
+        assert 0.0 <= result <= 1.0
+        # Perfect separation should give AUROC = 1.0
+        perfect_scores = np.array([0.0, 0.0, 1.0, 1.0])
+        perfect_result = metric_auroc(labels, perfect_scores)
+        assert perfect_result == 1.0
+
+    def test_metric_spearman(self):
+        """Test Spearman correlation metric."""
+        labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+        scores = np.array([1.1, 1.9, 3.2, 3.8, 5.1])
+        result = metric_spearman(labels, scores)
+        assert -1.0 <= result <= 1.0
+        # Perfect monotonic relationship should give correlation ≈ 1.0
+        perfect_scores = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+        perfect_result = metric_spearman(labels, perfect_scores)
+        assert np.isclose(perfect_result, 1.0)
+
+    def test_metric_pearson(self):
+        """Test Pearson correlation metric."""
+        labels = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+        scores = np.array([1.1, 1.9, 3.2, 3.8, 5.1])
+        result = metric_pearson(labels, scores)
+        assert -1.0 <= result <= 1.0
+        # Perfect linear relationship should give correlation = 1.0
+        perfect_scores = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
+        perfect_result = metric_pearson(labels, perfect_scores)
+        assert np.isclose(perfect_result, 1.0)
+
+    def test_get_metric_valid(self):
+        """Test getting valid metric functions."""
+        assert get_metric("auprc") == metric_auprc
+        assert get_metric("auroc") == metric_auroc
+        assert get_metric("spearman") == metric_spearman
+        assert get_metric("pearson") == metric_pearson
+
+    def test_get_metric_invalid(self):
+        """Test getting invalid metric raises ValueError."""
+        with pytest.raises(ValueError, match="Unknown metric: 'invalid'"):
+            get_metric("invalid")
+
+    def test_metrics_registry_complete(self):
+        """Test that METRICS registry contains all expected metrics."""
+        expected_metrics = {"auprc", "auroc", "spearman", "pearson"}
+        assert set(METRICS.keys()) == expected_metrics
diff --git a/uv.lock b/uv.lock
index 0261cb5..05983fb 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,6 +1,6 @@
 version = 1
 revision = 3
-requires-python = "==3.13.*"
+requires-python = ">=3.12, <3.14"
 resolution-markers = [
     "sys_platform == 'linux'",
     "sys_platform != 'linux'",
@@ -48,6 +48,23 @@ dependencies = [
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@@ -73,6 +90,7 @@ version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "frozenlist" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/61/62/06741b579156360248d1ec624842ad0edf697050bbaf7c3e46394e106ad1/aiosignal-1.4.0.tar.gz", hash = "sha256:f47eecd9468083c2029cc99945502cb7708b082c232f9aca65da147157b251c7", size = 25007, upload-time = "2025-07-03T22:54:43.528Z" }
 wheels = [
@@ -115,6 +133,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
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 sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
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