diff --git a/.github/bump_version.py b/.github/bump_version.py
index bb0fd6d..779a82e 100644
--- a/.github/bump_version.py
+++ b/.github/bump_version.py
@@ -19,9 +19,7 @@ def get_current_version(pyproject_path: Path) -> str:
 
 def infer_bump(changelog_dir: Path) -> str:
     fragments = [
-        f
-        for f in changelog_dir.iterdir()
-        if f.is_file() and f.name != ".gitkeep"
+        f for f in changelog_dir.iterdir() if f.is_file() and f.name != ".gitkeep"
     ]
     if not fragments:
         print("No changelog fragments found", file=sys.stderr)
diff --git a/Makefile b/Makefile
index 86835d7..99f8ff0 100644
--- a/Makefile
+++ b/Makefile
@@ -7,7 +7,7 @@ help:
 	@echo "  make install      Install package in editable mode"
 	@echo "  make install-dev  Install with development dependencies"
 	@echo "  make test         Run tests with coverage"
-	@echo "  make format       Format code with black"
+	@echo "  make format       Format code with ruff"
 	@echo "  make type-check   Run mypy type checker"
 	@echo "  make changelog    Update changelog and version"
 	@echo "  make clean        Remove build artifacts"
@@ -26,7 +26,7 @@ test:
 	pytest tests/ -v --cov=l0 --cov-report=term-missing
 
 format:
-	black . -l 79
+	ruff format .
 
 type-check:
 	mypy l0 --ignore-missing-imports
diff --git a/changelog.d/switch-to-ruff.changed b/changelog.d/switch-to-ruff.changed
new file mode 100644
index 0000000..aeb771e
--- /dev/null
+++ b/changelog.d/switch-to-ruff.changed
@@ -0,0 +1 @@
+Switched code formatter from Black to Ruff.
diff --git a/docs/examples/advanced.ipynb b/docs/examples/advanced.ipynb
index c236fe0..3f7bc68 100644
--- a/docs/examples/advanced.ipynb
+++ b/docs/examples/advanced.ipynb
@@ -32,8 +32,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Set device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f'Using device: {device}')"
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
    ]
   },
   {
diff --git a/docs/examples/basic_l0.ipynb b/docs/examples/basic_l0.ipynb
index 28b8d22..1f4dc1a 100644
--- a/docs/examples/basic_l0.ipynb
+++ b/docs/examples/basic_l0.ipynb
@@ -37,8 +37,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Set device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f'Using device: {device}')"
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
    ]
   },
   {
@@ -74,10 +74,7 @@
     "    def __init__(self, n_features, init_sparsity=0.9):\n",
     "        super().__init__()\n",
     "        self.l0_linear = L0Linear(\n",
-    "            n_features, 1,\n",
-    "            bias=False,\n",
-    "            init_sparsity=init_sparsity,\n",
-    "            temperature=0.5\n",
+    "            n_features, 1, bias=False, init_sparsity=init_sparsity, temperature=0.5\n",
     "        )\n",
     "\n",
     "    def forward(self, x):\n",
@@ -89,6 +86,7 @@
     "    def get_sparsity(self):\n",
     "        return self.l0_linear.get_sparsity()\n",
     "\n",
+    "\n",
     "model = L0LinearRegression(n_features, init_sparsity=0.5).to(device)\n",
     "print(f\"Initial sparsity: {model.get_sparsity():.2%}\")"
    ]
@@ -110,13 +108,11 @@
     "# Create a simple linear regression model with L0 regularization\n",
     "class L0LinearRegression(nn.Module):\n",
     "    \"\"\"Linear regression with L0 regularization.\"\"\"\n",
+    "\n",
     "    def __init__(self, n_features, init_sparsity=0.9):\n",
     "        super().__init__()\n",
     "        self.l0_linear = L0Linear(\n",
-    "            n_features, 1,\n",
-    "            bias=False,\n",
-    "            init_sparsity=init_sparsity,\n",
-    "            temperature=0.5\n",
+    "            n_features, 1, bias=False, init_sparsity=init_sparsity, temperature=0.5\n",
     "        )\n",
     "\n",
     "    def forward(self, x):\n",
@@ -204,25 +200,29 @@
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
     "\n",
     "ax1.plot(losses)\n",
-    "ax1.set_xlabel('Epoch')\n",
-    "ax1.set_ylabel('Total Loss')\n",
-    "ax1.set_title('Training Loss')\n",
+    "ax1.set_xlabel(\"Epoch\")\n",
+    "ax1.set_ylabel(\"Total Loss\")\n",
+    "ax1.set_title(\"Training Loss\")\n",
     "ax1.grid(True)\n",
     "\n",
     "ax2.plot(sparsities)\n",
-    "ax2.set_xlabel('Epoch')\n",
-    "ax2.set_ylabel('Sparsity')\n",
-    "ax2.set_title('Learned Sparsity')\n",
+    "ax2.set_xlabel(\"Epoch\")\n",
+    "ax2.set_ylabel(\"Sparsity\")\n",
+    "ax2.set_title(\"Learned Sparsity\")\n",
     "ax2.grid(True)\n",
-    "ax2.axhline(y=(n_features-n_informative)/n_features, color='r', linestyle='--',\n",
-    "            label=f'True sparsity: {(n_features-n_informative)/n_features:.1%}')\n",
+    "ax2.axhline(\n",
+    "    y=(n_features - n_informative) / n_features,\n",
+    "    color=\"r\",\n",
+    "    linestyle=\"--\",\n",
+    "    label=f\"True sparsity: {(n_features - n_informative) / n_features:.1%}\",\n",
+    ")\n",
     "ax2.legend()\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
     "print(f\"Final sparsity: {model.get_sparsity():.2%}\")\n",
-    "print(f\"True sparsity: {(n_features-n_informative)/n_features:.1%}\")"
+    "print(f\"True sparsity: {(n_features - n_informative) / n_features:.1%}\")"
    ]
   },
   {
@@ -319,44 +319,55 @@
     "    n_active = int((1 - final_sparsity) * n_features)\n",
     "    final_mse = F.mse_loss(model(X).squeeze(), y).item()\n",
     "\n",
-    "    results.append({\n",
-    "        'l0_lambda': l0_lambda,\n",
-    "        'sparsity': final_sparsity,\n",
-    "        'n_active': n_active,\n",
-    "        'mse': final_mse\n",
-    "    })\n",
+    "    results.append(\n",
+    "        {\n",
+    "            \"l0_lambda\": l0_lambda,\n",
+    "            \"sparsity\": final_sparsity,\n",
+    "            \"n_active\": n_active,\n",
+    "            \"mse\": final_mse,\n",
+    "        }\n",
+    "    )\n",
     "\n",
-    "    print(f\"L0 penalty={l0_lambda:8.1e}: {n_active:2d} active features, \"\n",
-    "          f\"sparsity={final_sparsity:.1%}, MSE={final_mse:.4f}\")\n",
+    "    print(\n",
+    "        f\"L0 penalty={l0_lambda:8.1e}: {n_active:2d} active features, \"\n",
+    "        f\"sparsity={final_sparsity:.1%}, MSE={final_mse:.4f}\"\n",
+    "    )\n",
     "\n",
     "# Visualize the relationship\n",
     "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
     "\n",
     "# Plot 1: Number of active features vs L0 penalty\n",
-    "l0_vals = [r['l0_lambda'] for r in results[1:]]  # Skip 0\n",
-    "n_active_vals = [r['n_active'] for r in results[1:]]\n",
-    "ax1.semilogx(l0_vals, n_active_vals, 'o-', linewidth=2, markersize=8)\n",
-    "ax1.axhline(y=n_true_features, color='r', linestyle='--', label=f'True features ({n_true_features})')\n",
-    "ax1.set_xlabel('L0 Penalty (λ)')\n",
-    "ax1.set_ylabel('Number of Active Features')\n",
-    "ax1.set_title('L0 Penalty Controls Sparsity')\n",
+    "l0_vals = [r[\"l0_lambda\"] for r in results[1:]]  # Skip 0\n",
+    "n_active_vals = [r[\"n_active\"] for r in results[1:]]\n",
+    "ax1.semilogx(l0_vals, n_active_vals, \"o-\", linewidth=2, markersize=8)\n",
+    "ax1.axhline(\n",
+    "    y=n_true_features,\n",
+    "    color=\"r\",\n",
+    "    linestyle=\"--\",\n",
+    "    label=f\"True features ({n_true_features})\",\n",
+    ")\n",
+    "ax1.set_xlabel(\"L0 Penalty (λ)\")\n",
+    "ax1.set_ylabel(\"Number of Active Features\")\n",
+    "ax1.set_title(\"L0 Penalty Controls Sparsity\")\n",
     "ax1.legend()\n",
     "ax1.grid(True, alpha=0.3)\n",
     "\n",
     "# Plot 2: Trade-off between sparsity and accuracy\n",
-    "sparsity_vals = [r['sparsity'] for r in results]\n",
-    "mse_vals = [r['mse'] for r in results]\n",
-    "ax2.plot([100*s for s in sparsity_vals], mse_vals, 'o-', linewidth=2, markersize=8)\n",
-    "ax2.set_xlabel('Sparsity (%)')\n",
-    "ax2.set_ylabel('MSE Loss')\n",
-    "ax2.set_title('Sparsity vs Accuracy Trade-off')\n",
+    "sparsity_vals = [r[\"sparsity\"] for r in results]\n",
+    "mse_vals = [r[\"mse\"] for r in results]\n",
+    "ax2.plot([100 * s for s in sparsity_vals], mse_vals, \"o-\", linewidth=2, markersize=8)\n",
+    "ax2.set_xlabel(\"Sparsity (%)\")\n",
+    "ax2.set_ylabel(\"MSE Loss\")\n",
+    "ax2.set_title(\"Sparsity vs Accuracy Trade-off\")\n",
     "ax2.grid(True, alpha=0.3)\n",
     "\n",
     "plt.tight_layout()\n",
     "plt.show()\n",
     "\n",
     "print(\"\\nKey insight: Higher L0 penalty → Fewer active features → Higher sparsity\")\n",
-    "print(f\"Ground truth had {n_true_features} important features out of {n_features} total\")"
+    "print(\n",
+    "    f\"Ground truth had {n_true_features} important features out of {n_features} total\"\n",
+    ")"
    ]
   },
   {
diff --git a/docs/examples/comparison.ipynb b/docs/examples/comparison.ipynb
index 477857b..64b6af1 100644
--- a/docs/examples/comparison.ipynb
+++ b/docs/examples/comparison.ipynb
@@ -37,8 +37,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Set device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f'Using device: {device}')"
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
    ]
   },
   {
@@ -75,13 +75,11 @@
     "# Create models for comparison\n",
     "class L0LinearRegression(nn.Module):\n",
     "    \"\"\"Linear regression with L0 regularization.\"\"\"\n",
+    "\n",
     "    def __init__(self, n_features, init_sparsity=0.9):\n",
     "        super().__init__()\n",
     "        self.l0_linear = L0Linear(\n",
-    "            n_features, 1,\n",
-    "            bias=False,\n",
-    "            init_sparsity=init_sparsity,\n",
-    "            temperature=0.5\n",
+    "            n_features, 1, bias=False, init_sparsity=init_sparsity, temperature=0.5\n",
     "        )\n",
     "\n",
     "    def forward(self, x):\n",
@@ -93,8 +91,10 @@
     "    def get_sparsity(self):\n",
     "        return self.l0_linear.get_sparsity()\n",
     "\n",
+    "\n",
     "class L1Linear(nn.Module):\n",
     "    \"\"\"Standard linear layer with L1 regularization.\"\"\"\n",
+    "\n",
     "    def __init__(self, in_features, out_features):\n",
     "        super().__init__()\n",
     "        self.linear = nn.Linear(in_features, out_features, bias=False)\n",
@@ -108,6 +108,7 @@
     "    def get_sparsity(self, threshold=1e-3):\n",
     "        return (self.linear.weight.abs() < threshold).float().mean().item()\n",
     "\n",
+    "\n",
     "# Train both models on the same data\n",
     "n_features = 100\n",
     "X_train = torch.randn(500, n_features)\n",
@@ -195,33 +196,33 @@
     "l1_weights = l1_model.linear.weight.squeeze().detach().cpu().numpy()\n",
     "\n",
     "# Plot weight distributions\n",
-    "axes[0, 0].hist(l0_effective, bins=50, edgecolor='black')\n",
-    "axes[0, 0].set_title('L0: Effective Weight Distribution')\n",
-    "axes[0, 0].set_xlabel('Weight Value')\n",
-    "axes[0, 0].set_ylabel('Count')\n",
-    "axes[0, 0].axvline(x=0, color='r', linestyle='--')\n",
-    "\n",
-    "axes[0, 1].hist(l1_weights, bins=50, edgecolor='black')\n",
-    "axes[0, 1].set_title('L1: Weight Distribution')\n",
-    "axes[0, 1].set_xlabel('Weight Value')\n",
-    "axes[0, 1].set_ylabel('Count')\n",
-    "axes[0, 1].axvline(x=0, color='r', linestyle='--')\n",
+    "axes[0, 0].hist(l0_effective, bins=50, edgecolor=\"black\")\n",
+    "axes[0, 0].set_title(\"L0: Effective Weight Distribution\")\n",
+    "axes[0, 0].set_xlabel(\"Weight Value\")\n",
+    "axes[0, 0].set_ylabel(\"Count\")\n",
+    "axes[0, 0].axvline(x=0, color=\"r\", linestyle=\"--\")\n",
+    "\n",
+    "axes[0, 1].hist(l1_weights, bins=50, edgecolor=\"black\")\n",
+    "axes[0, 1].set_title(\"L1: Weight Distribution\")\n",
+    "axes[0, 1].set_xlabel(\"Weight Value\")\n",
+    "axes[0, 1].set_ylabel(\"Count\")\n",
+    "axes[0, 1].axvline(x=0, color=\"r\", linestyle=\"--\")\n",
     "\n",
     "# Plot sparsity evolution\n",
-    "axes[1, 0].plot(l0_sparsities, label='L0', linewidth=2)\n",
-    "axes[1, 0].plot(l1_sparsities, label='L1', linewidth=2)\n",
-    "axes[1, 0].set_xlabel('Epoch')\n",
-    "axes[1, 0].set_ylabel('Sparsity')\n",
-    "axes[1, 0].set_title('Sparsity Evolution')\n",
+    "axes[1, 0].plot(l0_sparsities, label=\"L0\", linewidth=2)\n",
+    "axes[1, 0].plot(l1_sparsities, label=\"L1\", linewidth=2)\n",
+    "axes[1, 0].set_xlabel(\"Epoch\")\n",
+    "axes[1, 0].set_ylabel(\"Sparsity\")\n",
+    "axes[1, 0].set_title(\"Sparsity Evolution\")\n",
     "axes[1, 0].legend()\n",
     "axes[1, 0].grid(True)\n",
     "\n",
     "# Plot gate distribution for L0\n",
-    "axes[1, 1].hist(l0_gates, bins=50, edgecolor='black')\n",
-    "axes[1, 1].set_title('L0: Gate Value Distribution')\n",
-    "axes[1, 1].set_xlabel('Gate Value')\n",
-    "axes[1, 1].set_ylabel('Count')\n",
-    "axes[1, 1].axvline(x=0.5, color='r', linestyle='--', label='Threshold')\n",
+    "axes[1, 1].hist(l0_gates, bins=50, edgecolor=\"black\")\n",
+    "axes[1, 1].set_title(\"L0: Gate Value Distribution\")\n",
+    "axes[1, 1].set_xlabel(\"Gate Value\")\n",
+    "axes[1, 1].set_ylabel(\"Count\")\n",
+    "axes[1, 1].axvline(x=0.5, color=\"r\", linestyle=\"--\", label=\"Threshold\")\n",
     "axes[1, 1].legend()\n",
     "\n",
     "plt.tight_layout()\n",
diff --git a/docs/examples/feature_selection.ipynb b/docs/examples/feature_selection.ipynb
index 0ccf6b4..f1b9048 100644
--- a/docs/examples/feature_selection.ipynb
+++ b/docs/examples/feature_selection.ipynb
@@ -38,8 +38,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Set device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f'Using device: {device}')"
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
    ]
   },
   {
@@ -102,9 +102,10 @@
     "feature_gate = FeatureGate(\n",
     "    n_features=n_features,\n",
     "    max_features=50,  # Select at most 50 features\n",
-    "    temperature=0.2\n",
+    "    temperature=0.2,\n",
     ").to(device)\n",
     "\n",
+    "\n",
     "# Simple classifier on top of selected features\n",
     "class FeatureSelectClassifier(nn.Module):\n",
     "    def __init__(self, feature_gate):\n",
@@ -118,6 +119,7 @@
     "        x_gated = x * gates\n",
     "        return self.classifier(x_gated).squeeze()\n",
     "\n",
+    "\n",
     "model = FeatureSelectClassifier(feature_gate).to(device)\n",
     "optimizer = optim.Adam(model.parameters(), lr=0.01)\n",
     "\n",
@@ -136,7 +138,9 @@
     "\n",
     "    if (epoch + 1) % 25 == 0:\n",
     "        n_selected = len(feature_gate.get_active_indices())\n",
-    "        print(f\"Epoch {epoch+1}: Loss={total_loss:.4f}, Selected features={n_selected}\")"
+    "        print(\n",
+    "            f\"Epoch {epoch + 1}: Loss={total_loss:.4f}, Selected features={n_selected}\"\n",
+    "        )"
    ]
   },
   {
@@ -179,26 +183,30 @@
     "top_30_indices = torch.topk(importance, 30).indices.cpu().numpy()\n",
     "\n",
     "print(\"\\nTop 30 selected features:\")\n",
-    "print(f\"True informative features in top 30: {sum(i < n_informative for i in top_30_indices)}/{n_informative}\")\n",
+    "print(\n",
+    "    f\"True informative features in top 30: {sum(i < n_informative for i in top_30_indices)}/{n_informative}\"\n",
+    ")\n",
     "\n",
     "# Visualize feature importance\n",
     "plt.figure(figsize=(14, 5))\n",
     "\n",
     "plt.subplot(1, 2, 1)\n",
     "plt.bar(range(100), importance[:100].cpu().numpy())\n",
-    "plt.axvline(x=n_informative-0.5, color='r', linestyle='--', label='True/Noise boundary')\n",
-    "plt.xlabel('Feature Index')\n",
-    "plt.ylabel('Importance (Gate Value)')\n",
-    "plt.title('Feature Importance (First 100 features)')\n",
+    "plt.axvline(\n",
+    "    x=n_informative - 0.5, color=\"r\", linestyle=\"--\", label=\"True/Noise boundary\"\n",
+    ")\n",
+    "plt.xlabel(\"Feature Index\")\n",
+    "plt.ylabel(\"Importance (Gate Value)\")\n",
+    "plt.title(\"Feature Importance (First 100 features)\")\n",
     "plt.legend()\n",
     "\n",
     "plt.subplot(1, 2, 2)\n",
     "# Show importance distribution\n",
-    "plt.hist(importance.cpu().numpy(), bins=50, edgecolor='black')\n",
-    "plt.xlabel('Importance Value')\n",
-    "plt.ylabel('Count')\n",
-    "plt.title('Distribution of Feature Importance')\n",
-    "plt.axvline(x=0.5, color='r', linestyle='--', label='Selection threshold')\n",
+    "plt.hist(importance.cpu().numpy(), bins=50, edgecolor=\"black\")\n",
+    "plt.xlabel(\"Importance Value\")\n",
+    "plt.ylabel(\"Count\")\n",
+    "plt.title(\"Distribution of Feature Importance\")\n",
+    "plt.axvline(x=0.5, color=\"r\", linestyle=\"--\", label=\"Selection threshold\")\n",
     "plt.legend()\n",
     "\n",
     "plt.tight_layout()\n",
diff --git a/docs/examples/neural_networks.ipynb b/docs/examples/neural_networks.ipynb
index 336ecd9..499911e 100644
--- a/docs/examples/neural_networks.ipynb
+++ b/docs/examples/neural_networks.ipynb
@@ -41,8 +41,8 @@
     "np.random.seed(42)\n",
     "\n",
     "# Set device\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "print(f'Using device: {device}')"
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "print(f\"Using device: {device}\")"
    ]
   },
   {
@@ -89,13 +89,23 @@
     "    def __init__(self, init_sparsity=0.5, temperature=0.5):\n",
     "        super().__init__()\n",
     "        # Conv layers with L0\n",
-    "        self.conv1 = L0Conv2d(1, 6, 5, init_sparsity=init_sparsity, temperature=temperature)\n",
-    "        self.conv2 = L0Conv2d(6, 16, 5, init_sparsity=init_sparsity, temperature=temperature)\n",
+    "        self.conv1 = L0Conv2d(\n",
+    "            1, 6, 5, init_sparsity=init_sparsity, temperature=temperature\n",
+    "        )\n",
+    "        self.conv2 = L0Conv2d(\n",
+    "            6, 16, 5, init_sparsity=init_sparsity, temperature=temperature\n",
+    "        )\n",
     "\n",
     "        # FC layers with L0\n",
-    "        self.fc1 = L0Linear(16 * 4 * 4, 120, init_sparsity=init_sparsity, temperature=temperature)\n",
-    "        self.fc2 = L0Linear(120, 84, init_sparsity=init_sparsity, temperature=temperature)\n",
-    "        self.fc3 = L0Linear(84, 10, init_sparsity=init_sparsity, temperature=temperature)\n",
+    "        self.fc1 = L0Linear(\n",
+    "            16 * 4 * 4, 120, init_sparsity=init_sparsity, temperature=temperature\n",
+    "        )\n",
+    "        self.fc2 = L0Linear(\n",
+    "            120, 84, init_sparsity=init_sparsity, temperature=temperature\n",
+    "        )\n",
+    "        self.fc3 = L0Linear(\n",
+    "            84, 10, init_sparsity=init_sparsity, temperature=temperature\n",
+    "        )\n",
     "\n",
     "        self.pool = nn.MaxPool2d(2, 2)\n",
     "\n",
@@ -111,17 +121,18 @@
     "    def get_l0_loss(self):\n",
     "        l0_loss = 0\n",
     "        for module in self.modules():\n",
-    "            if hasattr(module, 'get_l0_penalty'):\n",
+    "            if hasattr(module, \"get_l0_penalty\"):\n",
     "                l0_loss = l0_loss + module.get_l0_penalty()\n",
     "        return l0_loss\n",
     "\n",
     "    def get_layer_sparsities(self):\n",
     "        sparsities = {}\n",
     "        for name, module in self.named_modules():\n",
-    "            if hasattr(module, 'get_sparsity'):\n",
+    "            if hasattr(module, \"get_sparsity\"):\n",
     "                sparsities[name] = module.get_sparsity()\n",
     "        return sparsities\n",
     "\n",
+    "\n",
     "lenet = L0LeNet5(init_sparsity=0.1, temperature=0.5).to(device)\n",
     "print(\"Initial layer sparsities:\")\n",
     "for name, sparsity in lenet.get_layer_sparsities().items():\n",
@@ -191,7 +202,9 @@
    "source": [
     "# Training LeNet with L0 and temperature annealing\n",
     "optimizer = optim.Adam(lenet.parameters(), lr=0.001)\n",
-    "temp_scheduler = TemperatureScheduler(initial_temp=1.0, final_temp=0.05, anneal_epochs=20)\n",
+    "temp_scheduler = TemperatureScheduler(\n",
+    "    initial_temp=1.0, final_temp=0.05, anneal_epochs=20\n",
+    ")\n",
     "l0_lambda = 1e-3\n",
     "\n",
     "train_losses = []\n",
@@ -226,7 +239,9 @@
     "\n",
     "    if (epoch + 1) % 10 == 0:\n",
     "        avg_sparsity = np.mean(list(lenet.get_layer_sparsities().values()))\n",
-    "        print(f\"Epoch {epoch+1}: Loss={train_losses[-1]:.4f}, Temp={temp:.3f}, Avg Sparsity={avg_sparsity:.2%}\")"
+    "        print(\n",
+    "            f\"Epoch {epoch + 1}: Loss={train_losses[-1]:.4f}, Temp={temp:.3f}, Avg Sparsity={avg_sparsity:.2%}\"\n",
+    "        )"
    ]
   },
   {
@@ -275,17 +290,17 @@
     "\n",
     "plt.subplot(1, 2, 1)\n",
     "plt.plot(train_losses)\n",
-    "plt.xlabel('Epoch')\n",
-    "plt.ylabel('Training Loss')\n",
-    "plt.title('LeNet-5 Training with L0')\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Training Loss\")\n",
+    "plt.title(\"LeNet-5 Training with L0\")\n",
     "plt.grid(True)\n",
     "\n",
     "plt.subplot(1, 2, 2)\n",
     "for name, sparsity_history in layer_sparsities.items():\n",
     "    plt.plot(sparsity_history, label=name)\n",
-    "plt.xlabel('Epoch')\n",
-    "plt.ylabel('Sparsity')\n",
-    "plt.title('Layer-wise Sparsity Evolution')\n",
+    "plt.xlabel(\"Epoch\")\n",
+    "plt.ylabel(\"Sparsity\")\n",
+    "plt.title(\"Layer-wise Sparsity Evolution\")\n",
     "plt.legend()\n",
     "plt.grid(True)\n",
     "\n",
@@ -297,17 +312,17 @@
     "total_params = 0\n",
     "active_params = 0\n",
     "for name, module in lenet.named_modules():\n",
-    "    if hasattr(module, 'get_sparsity'):\n",
+    "    if hasattr(module, \"get_sparsity\"):\n",
     "        sparsity = module.get_sparsity()\n",
-    "        if hasattr(module, 'weight'):\n",
+    "        if hasattr(module, \"weight\"):\n",
     "            n_params = module.weight.numel()\n",
     "            n_active = int(n_params * (1 - sparsity))\n",
     "            total_params += n_params\n",
     "            active_params += n_active\n",
     "            print(f\"  {name}: {sparsity:.2%} sparse ({n_active}/{n_params} active)\")\n",
     "\n",
-    "print(f\"\\nOverall sparsity: {1 - active_params/total_params:.2%}\")\n",
-    "print(f\"Compression ratio: {total_params/active_params:.1f}x\")"
+    "print(f\"\\nOverall sparsity: {1 - active_params / total_params:.2%}\")\n",
+    "print(f\"Compression ratio: {total_params / active_params:.1f}x\")"
    ]
   }
  ],
diff --git a/examples/sparse_calibration_stress_test.py b/examples/sparse_calibration_stress_test.py
index 885011c..b7506dc 100644
--- a/examples/sparse_calibration_stress_test.py
+++ b/examples/sparse_calibration_stress_test.py
@@ -29,9 +29,7 @@
 # Create sparse true weights
 w_true = np.zeros(N)
 active_indices = np.random.choice(N, size=N_active, replace=False)
-w_true[active_indices] = np.random.lognormal(
-    mean=2.0, sigma=1.0, size=N_active
-)
+w_true[active_indices] = np.random.lognormal(mean=2.0, sigma=1.0, size=N_active)
 
 # Target vector y
 y = M @ w_true
@@ -39,7 +37,7 @@
 # Checks to make sure we really have this level of sparsity:
 print(f"non-zeros elements in M: {M.nnz:,} ({M.nnz / (Q * N) * 100:.1f}%)")
 print(
-    f"non-zeros elements in w_true: {np.count_nonzero(w_true)} ({ np.count_nonzero(w_true) / N * 100:.1f}%)"
+    f"non-zeros elements in w_true: {np.count_nonzero(w_true)} ({np.count_nonzero(w_true) / N * 100:.1f}%)"
 )
 
 model = SparseCalibrationWeights(
@@ -75,9 +73,7 @@
     assert np.all(final_weights >= 0), "Weights should be non-negative!"
 
     active_weights = final_weights[final_weights > 1e-6]
-    print(
-        f"Final Sparsity (% zero): {100 - len(active_weights) / N * 100:.1f}%"
-    )
+    print(f"Final Sparsity (% zero): {100 - len(active_weights) / N * 100:.1f}%")
     if len(active_weights) > 0:
         print(f"\nActive weight statistics:")
         print(f"  Min: {active_weights.min():.4f}")
diff --git a/examples/sparse_regression_demo.py b/examples/sparse_regression_demo.py
index ee35e41..91409da 100644
--- a/examples/sparse_regression_demo.py
+++ b/examples/sparse_regression_demo.py
@@ -68,9 +68,7 @@ def final_gate_no_temp(log_alpha, beta, zeta, gamma):
 
 # Initialize parameters
 b0_param = torch.tensor(y_np.mean(), dtype=torch.float32, requires_grad=True)
-b_param = torch.tensor(
-    0.1 * np.random.rand(p), dtype=torch.float32, requires_grad=True
-)
+b_param = torch.tensor(0.1 * np.random.rand(p), dtype=torch.float32, requires_grad=True)
 
 # L0 gate parameters
 keep_prob = 0.5
@@ -137,9 +135,7 @@ def final_gate_no_temp(log_alpha, beta, zeta, gamma):
     print("Gate computation comparison (for learned log_alpha values):")
     print("-" * 60)
     print(f"log_alpha:                {log_alpha.numpy().round(3)}")
-    print(
-        f"sigmoid(log_alpha/beta):  {(log_alpha/beta).sigmoid().numpy().round(3)}"
-    )
+    print(f"sigmoid(log_alpha/beta):  {(log_alpha / beta).sigmoid().numpy().round(3)}")
     print(f"sigmoid(log_alpha):       {log_alpha.sigmoid().numpy().round(3)}")
     print("\nDividing by temperature (beta=2/3) amplifies the logit,")
     print("pushing sigmoid outputs closer to 0 or 1.")
diff --git a/l0/calibration.py b/l0/calibration.py
index 2a02996..1348832 100644
--- a/l0/calibration.py
+++ b/l0/calibration.py
@@ -121,8 +121,7 @@ def __init__(
         # Add jitter to break symmetry (if specified)
         if self.log_alpha_jitter_sd > 0:
             jitter = (
-                torch.randn(n_features, device=self.device)
-                * self.log_alpha_jitter_sd
+                torch.randn(n_features, device=self.device) * self.log_alpha_jitter_sd
             )
             self.log_alpha = nn.Parameter(mu + jitter)
         else:
@@ -132,9 +131,7 @@ def __init__(
         self._cached_M_torch: torch.sparse.Tensor | None = None
         self._cached_M_shape: tuple[int, int] | None = None
 
-    def _convert_sparse_to_torch(
-        self, M_sparse: sp.spmatrix
-    ) -> torch.sparse.Tensor:
+    def _convert_sparse_to_torch(self, M_sparse: sp.spmatrix) -> torch.sparse.Tensor:
         """
         Convert scipy sparse matrix to torch sparse tensor.
 
@@ -148,9 +145,7 @@ def _convert_sparse_to_torch(
             return self._cached_M_torch
 
         M_coo = M_sparse.tocoo()
-        indices = torch.LongTensor(np.vstack([M_coo.row, M_coo.col])).to(
-            self.device
-        )
+        indices = torch.LongTensor(np.vstack([M_coo.row, M_coo.col])).to(self.device)
         values = torch.FloatTensor(M_coo.data).to(self.device)
         M_torch = torch.sparse_coo_tensor(
             indices,
@@ -360,9 +355,7 @@ def fit(
 
         # Validate group_multipliers
         if group_multipliers is not None and target_groups is None:
-            raise ValueError(
-                "group_multipliers requires target_groups to be set"
-            )
+            raise ValueError("group_multipliers requires target_groups to be set")
 
         # Compute group weights for loss averaging
         if target_groups is not None:
@@ -385,8 +378,7 @@ def fit(
                     group_mask = target_groups == gid
                     if not group_mask.any():
                         raise ValueError(
-                            f"group_multipliers key {gid} not found "
-                            f"in target_groups"
+                            f"group_multipliers key {gid} not found in target_groups"
                         )
                     group_weights[group_mask] *= mult
         else:
@@ -396,9 +388,7 @@ def fit(
             if target_groups is not None:
                 n_groups = len(torch.unique(target_groups))
                 parts = [f"{n_groups} groups"]
-                parts.append(
-                    f"normalize={'on' if normalize_groups else 'off'}"
-                )
+                parts.append(f"normalize={'on' if normalize_groups else 'off'}")
                 if group_multipliers is not None:
                     parts.append(f"multipliers={group_multipliers}")
                 print(f"Groups: {', '.join(parts)}")
@@ -408,10 +398,7 @@ def fit(
         # Add jitter to weights to break symmetry (if jitter_sd > 0)
         if self.log_weight_jitter_sd > 0:
             with torch.no_grad():
-                jitter = (
-                    torch.randn_like(self.log_weight)
-                    * self.log_weight_jitter_sd
-                )
+                jitter = torch.randn_like(self.log_weight) * self.log_weight_jitter_sd
                 self.log_weight.data += jitter
 
         # Setup optimizer
@@ -428,9 +415,7 @@ def fit(
                 # Adding 1 to avoid division by zero
                 relative_errors = (y - y_pred) / (y + 1)
                 # Apply group weights and then average
-                weighted_squared_errors = (
-                    relative_errors.pow(2) * group_weights
-                )
+                weighted_squared_errors = relative_errors.pow(2) * group_weights
                 data_loss = (
                     weighted_squared_errors.sum()
                 )  # Sum because weights already normalize
@@ -480,18 +465,14 @@ def fit(
                         group_losses = []
                         for group_id in torch.unique(target_groups):
                             group_mask = target_groups == group_id
-                            group_mean_err = (
-                                rel_errors[group_mask].mean().item()
-                            )
+                            group_mean_err = rel_errors[group_mask].mean().item()
                             group_losses.append(group_mean_err)
                         mean_group_mare = np.mean(group_losses)
                     else:
                         mean_group_mare = mean_rel_err
 
                     # Calculate sparsity percentage
-                    sparsity_pct = 100 * (
-                        1 - active_info["count"] / self.n_features
-                    )
+                    sparsity_pct = 100 * (1 - active_info["count"] / self.n_features)
 
                     # Weight distribution diagnostic
                     if active_weights.numel() > 0:
@@ -512,10 +493,7 @@ def fit(
                             .item()
                         )
                         w_large = (
-                            (
-                                (active_weights >= 10.0)
-                                & (active_weights < 1000.0)
-                            )
+                            ((active_weights >= 10.0) & (active_weights < 1000.0))
                             .sum()
                             .item()
                         )
@@ -523,12 +501,12 @@ def fit(
 
                         total_active = active_weights.numel()
                         weight_dist = (
-                            f"[<0.01: {100*w_tiny/total_active:.1f}%, "
-                            f"0.01-0.1: {100*w_small/total_active:.1f}%, "
-                            f"0.1-1: {100*w_med/total_active:.1f}%, "
-                            f"1-10: {100*w_normal/total_active:.1f}%, "
-                            f"10-1000: {100*w_large/total_active:.1f}%, "
-                            f">1000: {100*w_huge/total_active:.1f}%]"
+                            f"[<0.01: {100 * w_tiny / total_active:.1f}%, "
+                            f"0.01-0.1: {100 * w_small / total_active:.1f}%, "
+                            f"0.1-1: {100 * w_med / total_active:.1f}%, "
+                            f"1-10: {100 * w_normal / total_active:.1f}%, "
+                            f"10-1000: {100 * w_large / total_active:.1f}%, "
+                            f">1000: {100 * w_huge / total_active:.1f}%]"
                         )
                     else:
                         weight_dist = "[no active weights]"
@@ -540,7 +518,7 @@ def fit(
 
                     if target_groups is not None:
                         print(
-                            f"Epoch {epoch+1:4d}: "
+                            f"Epoch {epoch + 1:4d}: "
                             f"mean_group_mare={mean_group_mare:.4%}, "
                             f"max_error={max_rel_err:.1%}, "
                             f"total_loss={actual_total_loss:.3f}, "
@@ -549,7 +527,7 @@ def fit(
                         )
                     else:
                         print(
-                            f"Epoch {epoch+1:4d}: "
+                            f"Epoch {epoch + 1:4d}: "
                             f"mean_error={mean_rel_err:.4%}, "
                             f"max_error={max_rel_err:.1%}, "
                             f"total_loss={actual_total_loss:.3f}, "
diff --git a/l0/distributions.py b/l0/distributions.py
index 9d1707b..40c5c98 100644
--- a/l0/distributions.py
+++ b/l0/distributions.py
@@ -69,9 +69,7 @@ def reset_parameters(self) -> None:
             init_val = math.log(self.init_mean / (1 - self.init_mean))
             self.qz_logits.data.fill_(init_val)
 
-    def forward(
-        self, input_shape: tuple[int, ...] | None = None
-    ) -> torch.Tensor:
+    def forward(self, input_shape: tuple[int, ...] | None = None) -> torch.Tensor:
         """
         Sample or compute gates.
 
diff --git a/l0/gates.py b/l0/gates.py
index 05b5bb7..4e6bb4d 100644
--- a/l0/gates.py
+++ b/l0/gates.py
@@ -130,9 +130,7 @@ def __init__(
         self.n_samples = n_samples
         self.target_samples = target_samples
 
-    def select_samples(
-        self, data: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def select_samples(self, data: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Select samples from data.
 
@@ -219,9 +217,7 @@ def __init__(
         self.n_features = n_features
         self.max_features = max_features
 
-    def select_features(
-        self, data: torch.Tensor
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    def select_features(self, data: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
         """
         Select features from data.
 
@@ -377,9 +373,7 @@ def select(
 
         # Track selection type
         # Create initial selection type array
-        _ = np.array(
-            ["l0"] * len(l0_indices) + ["random"] * len(random_indices)
-        )
+        _ = np.array(["l0"] * len(l0_indices) + ["random"] * len(random_indices))
 
         # Reorder selection_type to match sorted indices
         _ = torch.cat(
diff --git a/l0/layers.py b/l0/layers.py
index 2af2597..6c2b8f9 100644
--- a/l0/layers.py
+++ b/l0/layers.py
@@ -222,9 +222,7 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
             gates = gates.view(self.weight.shape)
             masked_weight = self.weight * gates
 
-        return F.conv2d(
-            input, masked_weight, self.bias, self.stride, self.padding
-        )
+        return F.conv2d(input, masked_weight, self.bias, self.stride, self.padding)
 
     def get_l0_penalty(self) -> torch.Tensor:
         """Get L0 penalty."""
@@ -398,9 +396,7 @@ def get_sparsity_stats(self) -> dict[str, dict[str, Any]]:
         """Get sparsity statistics for all layers."""
         stats = {}
         for name, module in self.named_modules():
-            if hasattr(module, "get_sparsity") and hasattr(
-                module, "get_l0_penalty"
-            ):
+            if hasattr(module, "get_sparsity") and hasattr(module, "get_l0_penalty"):
                 stats[name] = {
                     "sparsity": module.get_sparsity(),
                     "active_params": module.get_l0_penalty().item(),
diff --git a/l0/penalties.py b/l0/penalties.py
index e2484e4..c90b860 100644
--- a/l0/penalties.py
+++ b/l0/penalties.py
@@ -107,9 +107,7 @@ def get_sparsity_stats(model: nn.Module) -> dict[str, dict[str, Any]]:
     stats = {}
 
     for name, module in model.named_modules():
-        if hasattr(module, "get_sparsity") and hasattr(
-            module, "get_l0_penalty"
-        ):
+        if hasattr(module, "get_sparsity") and hasattr(module, "get_l0_penalty"):
             active_params = module.get_l0_penalty().item()
 
             # Calculate total parameters
@@ -204,16 +202,12 @@ def get_temperature(self, epoch: int) -> float:
         progress = epoch / self.anneal_epochs
 
         if self.schedule == "linear":
-            temp = (
-                self.initial_temp
-                - (self.initial_temp - self.final_temp) * progress
-            )
+            temp = self.initial_temp - (self.initial_temp - self.final_temp) * progress
 
         elif self.schedule == "exponential":
             log_temp = (
                 np.log(self.initial_temp)
-                - (np.log(self.initial_temp) - np.log(self.final_temp))
-                * progress
+                - (np.log(self.initial_temp) - np.log(self.final_temp)) * progress
             )
             temp = np.exp(log_temp)
 
diff --git a/l0/sparse.py b/l0/sparse.py
index db398e4..0b6a425 100644
--- a/l0/sparse.py
+++ b/l0/sparse.py
@@ -7,7 +7,6 @@
 
 # ruff: noqa: N803 N806
 
-
 import numpy as np
 import torch
 import torch.nn as nn
@@ -67,18 +66,14 @@ def __init__(
         # L0 gate parameters
         mu = torch.log(torch.tensor(init_keep_prob / (1 - init_keep_prob)))
         self.log_alpha = nn.Parameter(
-            torch.normal(
-                mu.item(), 0.01, size=(n_features,), device=self.device
-            )
+            torch.normal(mu.item(), 0.01, size=(n_features,), device=self.device)
         )
 
         # Cache for sparse tensor conversion
         self._cached_X_torch: torch.sparse.Tensor | None = None
         self._cached_X_shape: tuple[int, int] | None = None
 
-    def _convert_sparse_to_torch(
-        self, X_sparse: sp.spmatrix
-    ) -> torch.sparse.Tensor:
+    def _convert_sparse_to_torch(self, X_sparse: sp.spmatrix) -> torch.sparse.Tensor:
         """
         Convert scipy sparse matrix to torch sparse tensor.
 
@@ -92,9 +87,7 @@ def _convert_sparse_to_torch(
             return self._cached_X_torch
 
         X_coo = X_sparse.tocoo()
-        indices = torch.LongTensor(np.vstack([X_coo.row, X_coo.col])).to(
-            self.device
-        )
+        indices = torch.LongTensor(np.vstack([X_coo.row, X_coo.col])).to(self.device)
         values = torch.FloatTensor(X_coo.data).to(self.device)
         X_torch = torch.sparse_coo_tensor(
             indices,
@@ -291,7 +284,7 @@ def fit(
                 with torch.no_grad():
                     n_active = (self.get_deterministic_gates() > 0.01).sum()
                     print(
-                        f"Epoch {epoch+1:4d}: "
+                        f"Epoch {epoch + 1:4d}: "
                         f"loss={loss.item():.4f}, "
                         f"mse={mse_loss.item():.4f}, "
                         f"l0={l0_loss.item():.2f}, "
@@ -300,9 +293,7 @@ def fit(
 
         return self
 
-    def predict(
-        self, X_sparse: sp.spmatrix | torch.sparse.Tensor
-    ) -> torch.Tensor:
+    def predict(self, X_sparse: sp.spmatrix | torch.sparse.Tensor) -> torch.Tensor:
         """
         Make predictions on new data.
 
diff --git a/pyproject.toml b/pyproject.toml
index 9a43092..bac73df 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,7 +31,7 @@ keywords = ["l0", "regularization", "sparsity", "neural networks", "calibration"
 dev = [
     "pytest>=7.0",
     "pytest-cov>=3.0",
-    "black>=22.0",
+    "ruff>=0.9.0",
     "build>=1.3",
     "scikit-learn>=1.0",
     "yaml-changelog>=0.3.0",
@@ -58,10 +58,6 @@ Repository = "https://github.com/PolicyEngine/L0"
 Documentation = "https://github.com/PolicyEngine/L0#readme"
 Issues = "https://github.com/PolicyEngine/L0/issues"
 
-[tool.black]
-line-length = 79
-target-version = ['py313']
-
 [tool.pytest.ini_options]
 testpaths = ["tests"]
 python_files = ["test_*.py"]
diff --git a/tests/test_calibration.py b/tests/test_calibration.py
index cd8869b..b48ed9c 100644
--- a/tests/test_calibration.py
+++ b/tests/test_calibration.py
@@ -44,9 +44,7 @@ def test_sparse_ground_truth_relative_loss(self):
 
         w_true = np.zeros(N)
         active_indices = np.random.choice(N, size=N_active, replace=False)
-        w_true[active_indices] = np.random.lognormal(
-            mean=2.0, sigma=1.0, size=N_active
-        )
+        w_true[active_indices] = np.random.lognormal(mean=2.0, sigma=1.0, size=N_active)
 
         y = M @ w_true
 
@@ -74,9 +72,7 @@ def test_sparse_ground_truth_relative_loss(self):
 
         # Check sparsity is reasonable (between 30% and 70%)
         sparsity = model.get_sparsity()
-        assert (
-            0.3 <= sparsity <= 0.7
-        ), f"Sparsity {sparsity:.2%} not in expected range"
+        assert 0.3 <= sparsity <= 0.7, f"Sparsity {sparsity:.2%} not in expected range"
 
         # Check relative loss is low
         with torch.no_grad():
@@ -130,9 +126,9 @@ def test_relative_vs_mse_loss(self):
             rel_err_rel = np.mean(np.abs((y - y_pred_rel) / (y + 1)))
 
             # Relative loss should do better on relative error
-            assert (
-                rel_err_rel <= rel_err_mse * 1.5
-            ), f"Relative loss should handle scale better: {rel_err_rel:.4f} vs {rel_err_mse:.4f}"
+            assert rel_err_rel <= rel_err_mse * 1.5, (
+                f"Relative loss should handle scale better: {rel_err_rel:.4f} vs {rel_err_mse:.4f}"
+            )
 
     def test_sparsity_control(self):
         """Test that L0 penalty controls sparsity level."""
@@ -162,12 +158,12 @@ def test_sparsity_control(self):
             sparsities.append(model.get_sparsity())
 
         # Higher penalty should give more sparsity
-        assert (
-            sparsities[0] < sparsities[1]
-        ), "Higher L0 penalty should increase sparsity"
-        assert (
-            sparsities[1] < sparsities[2]
-        ), "Higher L0 penalty should increase sparsity"
+        assert sparsities[0] < sparsities[1], (
+            "Higher L0 penalty should increase sparsity"
+        )
+        assert sparsities[1] < sparsities[2], (
+            "Higher L0 penalty should increase sparsity"
+        )
 
     def test_get_active_weights(self):
         """Test active weight extraction."""
@@ -243,9 +239,9 @@ def test_l2_regularization(self):
             weights_with_l2 = model_with_l2.get_weights(deterministic=True)
 
             # L2 should reduce weight magnitudes
-            assert (
-                weights_with_l2.max() <= weights_no_l2.max() * 2.0
-            ), "L2 should prevent extreme weights"
+            assert weights_with_l2.max() <= weights_no_l2.max() * 2.0, (
+                "L2 should prevent extreme weights"
+            )
 
     def test_pure_l2_penalty(self):
         """Test pure L2 regularization without L0."""
@@ -301,30 +297,30 @@ def test_pure_l2_penalty(self):
             active_l2 = weights_l2[weights_l2 > 1e-8]
 
             # L2 should keep most weights active (no sparsity from L2)
-            assert (
-                len(active_l2) >= N * 0.9
-            ), f"Pure L2 should not induce sparsity, got {len(active_l2)}/{N}"
+            assert len(active_l2) >= N * 0.9, (
+                f"Pure L2 should not induce sparsity, got {len(active_l2)}/{N}"
+            )
 
             # L2 norm should be smaller with regularization
             l2_norm_no_reg = (weights_no_reg**2).sum().sqrt()
             l2_norm_with_l2 = (weights_l2**2).sum().sqrt()
-            assert (
-                l2_norm_with_l2 < l2_norm_no_reg
-            ), f"L2 regularization should reduce L2 norm: {l2_norm_with_l2:.2f} vs {l2_norm_no_reg:.2f}"
+            assert l2_norm_with_l2 < l2_norm_no_reg, (
+                f"L2 regularization should reduce L2 norm: {l2_norm_with_l2:.2f} vs {l2_norm_no_reg:.2f}"
+            )
 
             # Check coefficient of variation (CV = std/mean) is lower with L2
             # This captures that L2 shrinks weights toward each other
             if active_no_reg.mean() > 1e-6 and active_l2.mean() > 1e-6:
                 cv_no_reg = active_no_reg.std() / active_no_reg.mean()
                 cv_l2 = active_l2.std() / active_l2.mean()
-                assert (
-                    cv_l2 < cv_no_reg * 1.2
-                ), f"L2 should reduce relative variation: CV {cv_l2:.2f} vs {cv_no_reg:.2f}"
+                assert cv_l2 < cv_no_reg * 1.2, (
+                    f"L2 should reduce relative variation: CV {cv_l2:.2f} vs {cv_no_reg:.2f}"
+                )
 
             # L2 should prevent extreme weights
-            assert (
-                weights_l2.max() < weights_no_reg.max() * 1.5
-            ), f"L2 should limit max weights: {weights_l2.max():.2f} vs {weights_no_reg.max():.2f}"
+            assert weights_l2.max() < weights_no_reg.max() * 1.5, (
+                f"L2 should limit max weights: {weights_l2.max():.2f} vs {weights_no_reg.max():.2f}"
+            )
 
             # Both should still fit reasonably well
             y_pred_no_reg = model_no_reg.predict(M).cpu().numpy()
@@ -334,9 +330,9 @@ def test_pure_l2_penalty(self):
             error_l2 = np.abs((y - y_pred_l2) / y).mean()
 
             # L2 model may have slightly worse fit due to regularization
-            assert (
-                error_l2 < 0.5
-            ), f"L2 model should still fit reasonably: {error_l2:.3f}"
+            assert error_l2 < 0.5, (
+                f"L2 model should still fit reasonably: {error_l2:.3f}"
+            )
             # But the trade-off is worth it for regularization
 
     def test_l0_l2_combination(self):
@@ -412,19 +408,19 @@ def test_l0_l2_combination(self):
             active_l0_l2 = (weights_l0_l2 > 1e-6).sum().item()
 
             # L0-only should have sparsity
-            assert (
-                active_l0_only < N * 0.8
-            ), f"L0 should induce sparsity: {active_l0_only}/{N} active"
+            assert active_l0_only < N * 0.8, (
+                f"L0 should induce sparsity: {active_l0_only}/{N} active"
+            )
 
             # L2-only should have no/little sparsity
-            assert (
-                active_l2_only > N * 0.9
-            ), f"L2 alone shouldn't induce sparsity: {active_l2_only}/{N} active"
+            assert active_l2_only > N * 0.9, (
+                f"L2 alone shouldn't induce sparsity: {active_l2_only}/{N} active"
+            )
 
             # L0+L2 should have sparsity (from L0)
-            assert (
-                active_l0_l2 < N * 0.8
-            ), f"L0+L2 should have sparsity: {active_l0_l2}/{N} active"
+            assert active_l0_l2 < N * 0.8, (
+                f"L0+L2 should have sparsity: {active_l0_l2}/{N} active"
+            )
 
             # Among active weights, L0+L2 should have smaller magnitudes than L0-only (from L2)
             active_mask_l0_only = weights_l0_only > 1e-6
@@ -435,15 +431,13 @@ def test_l0_l2_combination(self):
                 l2_norm_l0_only = (
                     (weights_l0_only[active_mask_l0_only] ** 2).sum().sqrt()
                 )
-                l2_norm_l0_l2 = (
-                    (weights_l0_l2[active_mask_l0_l2] ** 2).sum().sqrt()
-                )
+                l2_norm_l0_l2 = (weights_l0_l2[active_mask_l0_l2] ** 2).sum().sqrt()
 
                 # L0+L2 should have smaller weight norms than L0-only
                 # (L2 regularization effect on top of sparsity)
-                assert (
-                    l2_norm_l0_l2 < l2_norm_l0_only * 1.2
-                ), f"L0+L2 should have controlled weights: {l2_norm_l0_l2:.2f} vs L0-only {l2_norm_l0_only:.2f}"
+                assert l2_norm_l0_l2 < l2_norm_l0_only * 1.2, (
+                    f"L0+L2 should have controlled weights: {l2_norm_l0_l2:.2f} vs L0-only {l2_norm_l0_only:.2f}"
+                )
 
             # Check prediction quality for all models
             y_pred_l0_only = model_l0_only.predict(M).cpu().numpy()
@@ -455,22 +449,12 @@ def test_l0_l2_combination(self):
             error_l0_l2 = np.abs((y - y_pred_l0_l2) / (y + 1)).mean()
 
             # All should fit reasonably well (L2-only may have slightly worse fit due to regularization)
-            assert (
-                error_l0_only < 0.35
-            ), f"L0-only should fit well: {error_l0_only:.3f}"
-            assert (
-                error_l2_only < 0.35
-            ), f"L2-only should fit well: {error_l2_only:.3f}"
-            assert (
-                error_l0_l2 < 0.35
-            ), f"L0+L2 should fit well: {error_l0_l2:.3f}"
-
-            print(
-                f"\nL0-only: {active_l0_only}/{N} active, error={error_l0_only:.3f}"
-            )
-            print(
-                f"L2-only: {active_l2_only}/{N} active, error={error_l2_only:.3f}"
-            )
+            assert error_l0_only < 0.35, f"L0-only should fit well: {error_l0_only:.3f}"
+            assert error_l2_only < 0.35, f"L2-only should fit well: {error_l2_only:.3f}"
+            assert error_l0_l2 < 0.35, f"L0+L2 should fit well: {error_l0_l2:.3f}"
+
+            print(f"\nL0-only: {active_l0_only}/{N} active, error={error_l0_only:.3f}")
+            print(f"L2-only: {active_l2_only}/{N} active, error={error_l2_only:.3f}")
             print(f"L0+L2: {active_l0_l2}/{N} active, error={error_l0_l2:.3f}")
 
     def test_group_wise_averaging(self):
@@ -569,8 +553,7 @@ def test_group_wise_averaging(self):
 
             # Errors should be within an order of magnitude of each other
             assert max_err < min_err * 10, (
-                f"Group errors should be balanced: "
-                f"min={min_err:.4f}, max={max_err:.4f}"
+                f"Group errors should be balanced: min={min_err:.4f}, max={max_err:.4f}"
             )
 
     def test_group_wise_averaging_edge_cases(self):
@@ -602,9 +585,9 @@ def test_group_wise_averaging_edge_cases(self):
         with torch.no_grad():
             y_pred = model.predict(M).cpu().numpy()
             rel_err = np.mean(np.abs((y - y_pred) / (y + 1)))
-            assert (
-                rel_err < 0.5
-            ), f"Single group should still converge, got {rel_err:.4f}"
+            assert rel_err < 0.5, (
+                f"Single group should still converge, got {rel_err:.4f}"
+            )
 
         # Test 2: Each target in its own group (like all singletons)
         target_groups_all_singleton = np.arange(Q)
@@ -623,9 +606,9 @@ def test_group_wise_averaging_edge_cases(self):
         with torch.no_grad():
             y_pred = model_new.predict(M).cpu().numpy()
             rel_err = np.mean(np.abs((y - y_pred) / (y + 1)))
-            assert (
-                rel_err < 0.5
-            ), f"All singleton groups should converge, got {rel_err:.4f}"
+            assert rel_err < 0.5, (
+                f"All singleton groups should converge, got {rel_err:.4f}"
+            )
 
         # Test 3: Unbalanced groups (1 huge group, several small)
         target_groups_unbalanced = np.array([0] * 7 + [1, 2, 3])
@@ -645,9 +628,9 @@ def test_group_wise_averaging_edge_cases(self):
             y_pred = model_unbalanced.predict(M).cpu().numpy()
             # Check that small groups aren't ignored
             small_group_errors = np.abs((y[7:] - y_pred[7:]) / (y[7:] + 1))
-            assert (
-                np.mean(small_group_errors) < 0.5
-            ), "Small groups should not be ignored"
+            assert np.mean(small_group_errors) < 0.5, (
+                "Small groups should not be ignored"
+            )
 
     def test_init_weights_options(self):
         """Test different weight initialization options."""
@@ -667,9 +650,7 @@ def test_init_weights_options(self):
 
         # Test 3: Array initialization
         init_array = np.random.uniform(0.5, 2.0, size=N)
-        model_array = SparseCalibrationWeights(
-            n_features=N, init_weights=init_array
-        )
+        model_array = SparseCalibrationWeights(n_features=N, init_weights=init_array)
         with torch.no_grad():
             weights = torch.exp(model_array.log_weight).cpu().numpy()
             np.testing.assert_allclose(weights, init_array, rtol=1e-5)
@@ -718,9 +699,7 @@ def test_weight_jitter(self):
         # After 1 epoch, change should be small without jitter
         weights_after_1_epoch = model_no_jitter.log_weight.data
         # The change is due to gradient updates only
-        change = (
-            (weights_after_1_epoch - initial_weights_no_jitter).abs().max()
-        )
+        change = (weights_after_1_epoch - initial_weights_no_jitter).abs().max()
         assert change < 1.0, "Without jitter, initial change should be small"
 
     def test_init_keep_prob_options(self):
@@ -820,12 +799,8 @@ def test_group_multipliers_without_normalization(self):
             y_pred_mult = model_mult.predict(M).cpu().numpy()
 
             # Singleton relative errors
-            sing_err_base = np.abs(
-                (y[:3] - y_pred_base[:3]) / (y[:3] + 1)
-            ).mean()
-            sing_err_mult = np.abs(
-                (y[:3] - y_pred_mult[:3]) / (y[:3] + 1)
-            ).mean()
+            sing_err_base = np.abs((y[:3] - y_pred_base[:3]) / (y[:3] + 1)).mean()
+            sing_err_mult = np.abs((y[:3] - y_pred_mult[:3]) / (y[:3] + 1)).mean()
 
             assert sing_err_mult < sing_err_base * 1.1, (
                 f"10x multiplier should improve singleton fit: "
@@ -882,12 +857,8 @@ def test_group_multipliers_with_normalization(self):
             y_pred_mult = model_norm_mult.predict(M).cpu().numpy()
 
             # Group 3 error should improve with multiplier
-            g3_err_norm = np.abs(
-                (y[3:21] - y_pred_norm[3:21]) / (y[3:21] + 1)
-            ).mean()
-            g3_err_mult = np.abs(
-                (y[3:21] - y_pred_mult[3:21]) / (y[3:21] + 1)
-            ).mean()
+            g3_err_norm = np.abs((y[3:21] - y_pred_norm[3:21]) / (y[3:21] + 1)).mean()
+            g3_err_mult = np.abs((y[3:21] - y_pred_mult[3:21]) / (y[3:21] + 1)).mean()
 
             assert g3_err_mult < g3_err_norm * 1.1, (
                 f"5x multiplier should improve group 3 fit: "
diff --git a/tests/test_distributions.py b/tests/test_distributions.py
index a8315cc..bfac63b 100644
--- a/tests/test_distributions.py
+++ b/tests/test_distributions.py
@@ -153,9 +153,7 @@ def test_temperature_effect(self):
         assert high_var > 0 and low_var > 0
         # Optional: lower temp typically has lower variance but not guaranteed with stretch
         # We just verify both are producing variable outputs
-        assert (
-            high_var < 0.5 and low_var < 0.5
-        )  # Both should have reasonable variance
+        assert high_var < 0.5 and low_var < 0.5  # Both should have reasonable variance
 
     def test_stretch_parameters(self):
         """Test stretch parameter effects."""
diff --git a/tests/test_gates.py b/tests/test_gates.py
index 544009e..b541685 100644
--- a/tests/test_gates.py
+++ b/tests/test_gates.py
@@ -86,8 +86,8 @@ def test_weighted_selection(self, sample_gate):
         weights = torch.rand(1000)
 
         sample_gate.eval()
-        selected_data, selected_weights, indices = (
-            sample_gate.select_weighted_samples(data, weights)
+        selected_data, selected_weights, indices = sample_gate.select_weighted_samples(
+            data, weights
         )
 
         assert selected_data.shape[0] == len(selected_weights)
@@ -162,8 +162,8 @@ def test_feature_names(self, feature_gate):
         feature_names = [f"feature_{i}" for i in range(100)]
 
         feature_gate.eval()
-        selected_data, selected_names = (
-            feature_gate.select_features_with_names(data, feature_names)
+        selected_data, selected_names = feature_gate.select_features_with_names(
+            data, feature_names
         )
 
         assert selected_data.shape[1] == len(selected_names)
diff --git a/tests/test_layers.py b/tests/test_layers.py
index 8ab0558..0bcaf2a 100644
--- a/tests/test_layers.py
+++ b/tests/test_layers.py
@@ -191,9 +191,7 @@ def test_depthwise_conv(self):
         """Test depthwise convolution with L0."""
         from l0.layers import L0DepthwiseConv2d
 
-        conv = L0DepthwiseConv2d(
-            in_channels=32, kernel_size=3, init_sparsity=0.7
-        )
+        conv = L0DepthwiseConv2d(in_channels=32, kernel_size=3, init_sparsity=0.7)
 
         x = torch.randn(4, 32, 16, 16)
         output = conv(x)
@@ -237,9 +235,7 @@ def test_total_l0_loss(self, model):
             if hasattr(module, "get_l0_penalty"):
                 expected += module.get_l0_penalty().item()
 
-        assert (
-            abs(l0_loss.item() - expected) < 0.1
-        )  # Allow small numerical difference
+        assert abs(l0_loss.item() - expected) < 0.1  # Allow small numerical difference
 
     def test_sparsity_stats(self, model):
         """Test getting sparsity statistics."""
diff --git a/tests/test_penalties.py b/tests/test_penalties.py
index 0de1b6c..b052aa4 100644
--- a/tests/test_penalties.py
+++ b/tests/test_penalties.py
@@ -42,9 +42,7 @@ def test_compute_l0_penalty(self, simple_model):
         expected += simple_model.fc1.get_l0_penalty().item()
         expected += simple_model.fc2.get_l0_penalty().item()
 
-        assert (
-            abs(penalty.item() - expected) < 0.1
-        )  # Allow small numerical difference
+        assert abs(penalty.item() - expected) < 0.1  # Allow small numerical difference
 
     def test_compute_l2_penalty(self, simple_model):
         """Test computing L2 penalty."""
@@ -63,9 +61,7 @@ def test_compute_l2_penalty(self, simple_model):
 
         # L2 penalty computation may have larger differences due to weight initialization
         # Allow for reasonable tolerance based on weight magnitudes
-        relative_diff = abs(penalty.item() - expected) / max(
-            penalty.item(), expected
-        )
+        relative_diff = abs(penalty.item() - expected) / max(penalty.item(), expected)
         assert (
             relative_diff < 0.75
         )  # Allow up to 75% relative difference due to init variance
@@ -78,9 +74,7 @@ def test_compute_l0l2_penalty(self, simple_model):
             compute_l2_penalty,
         )
 
-        penalty = compute_l0l2_penalty(
-            simple_model, l0_lambda=1e-3, l2_lambda=1e-4
-        )
+        penalty = compute_l0l2_penalty(simple_model, l0_lambda=1e-3, l2_lambda=1e-4)
 
         assert penalty.shape == ()
         assert penalty.item() > 0
diff --git a/tests/test_sparse.py b/tests/test_sparse.py
index 7d7f7c1..fa285f8 100644
--- a/tests/test_sparse.py
+++ b/tests/test_sparse.py
@@ -35,9 +35,7 @@ def test_sparse_data_recovery(self):
         num_nonzero = int(p * (1 - beta_sparsity))
         nonzero_indices = np.random.choice(p, num_nonzero, replace=False)
         b = np.zeros(p)
-        b[nonzero_indices] = np.random.choice(
-            [-3, -2, -1, 1, 2, 3], num_nonzero
-        )
+        b[nonzero_indices] = np.random.choice([-3, -2, -1, 1, 2, 3], num_nonzero)
 
         # Generate sparse X matrix
         X_dense = np.random.randn(n, p) * 0.5
@@ -66,9 +64,7 @@ def test_sparse_data_recovery(self):
         lambda_reg = 0.01 * sigma2_hat
 
         # Fit our sparse L0 model
-        model = SparseL0Linear(
-            n_features=p, fit_intercept=True, init_keep_prob=0.5
-        )
+        model = SparseL0Linear(n_features=p, fit_intercept=True, init_keep_prob=0.5)
 
         model.fit(
             X_sparse,
@@ -85,9 +81,7 @@ def test_sparse_data_recovery(self):
 
         # We expect to recover at least 60% of true features
         recall = (
-            true_positives / len(nonzero_indices)
-            if len(nonzero_indices) > 0
-            else 0
+            true_positives / len(nonzero_indices) if len(nonzero_indices) > 0 else 0
         )
         assert recall >= 0.6, f"Recall {recall:.2%} is too low"
 
@@ -125,21 +119,17 @@ def test_no_intercept(self):
         y = X_sparse @ b + 0.1 * np.random.randn(n)
 
         # Fit model without intercept
-        model = SparseL0Linear(
-            n_features=p, fit_intercept=False, init_keep_prob=0.5
-        )
+        model = SparseL0Linear(n_features=p, fit_intercept=False, init_keep_prob=0.5)
 
-        assert (
-            model.bias is None
-        ), "Bias should be None when fit_intercept=False"
+        assert model.bias is None, "Bias should be None when fit_intercept=False"
 
         model.fit(X_sparse, y, epochs=500, verbose=False)
 
         # Check that we find the right features
         selected = model.get_selected_features().cpu().numpy()
-        assert (
-            0 in selected or 1 in selected or 2 in selected
-        ), "Should select at least one true feature"
+        assert 0 in selected or 1 in selected or 2 in selected, (
+            "Should select at least one true feature"
+        )
 
         # Make predictions
         y_pred = model.predict(X_sparse)
@@ -169,12 +159,8 @@ def test_sparse_efficiency(self):
         model.fit(X_sparse, y, epochs=100, verbose=False)
 
         # Check that cached tensor is sparse
-        assert (
-            model._cached_X_torch is not None
-        ), "Should cache the sparse tensor"
-        assert (
-            model._cached_X_torch.is_sparse
-        ), "Cached tensor should be sparse"
+        assert model._cached_X_torch is not None, "Should cache the sparse tensor"
+        assert model._cached_X_torch.is_sparse, "Cached tensor should be sparse"
 
         # Multiple predictions should reuse cached tensor
         y_pred1 = model.predict(X_sparse)
@@ -235,6 +221,6 @@ def test_deterministic_vs_stochastic(self):
             y_stoch1 = model.forward(X_torch, deterministic=False)
             y_stoch2 = model.forward(X_torch, deterministic=False)
 
-        assert not torch.allclose(
-            y_stoch1, y_stoch2
-        ), "Stochastic predictions should differ"
+        assert not torch.allclose(y_stoch1, y_stoch2), (
+            "Stochastic predictions should differ"
+        )
diff --git a/uv.lock b/uv.lock
index bb3aeb2..080332d 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,9 +1,12 @@
 version = 1
-revision = 2
-requires-python = ">=3.13"
+revision = 3
+requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.14'",
-    "python_full_version < '3.14'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy'",
+    "python_full_version >= '3.14' and platform_python_implementation == 'PyPy'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.12'",
 ]
 
 [[package]]
@@ -13,6 +16,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
     { name = "sniffio" },
+    { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/f1/b4/636b3b65173d3ce9a38ef5f0522789614e590dab6a8d505340a4efe4c567/anyio-4.10.0.tar.gz", hash = "sha256:3f3fae35c96039744587aa5b8371e7e8e603c0702999535961dd336026973ba6", size = 213252, upload-time = "2025-08-04T08:54:26.451Z" }
 wheels = [
@@ -46,7 +50,8 @@ name = "argon2-cffi-bindings"
 version = "21.2.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version >= '3.14'",
+    "python_full_version >= '3.14' and platform_python_implementation != 'PyPy'",
+    "python_full_version >= '3.14' and platform_python_implementation == 'PyPy'",
 ]
 dependencies = [
     { name = "cffi", marker = "python_full_version >= '3.14'" },
@@ -70,7 +75,9 @@ name = "argon2-cffi-bindings"
 version = "25.1.0"
 source = { registry = "https://pypi.org/simple" }
 resolution-markers = [
-    "python_full_version < '3.14'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation != 'PyPy'",
+    "python_full_version >= '3.12' and python_full_version < '3.14' and platform_python_implementation == 'PyPy'",
+    "python_full_version < '3.12'",
 ]
 dependencies = [
     { name = "cffi", marker = "python_full_version < '3.14'" },
@@ -99,6 +106,15 @@ wheels = [
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-version = "0.1.0"
+name = "l0-python"
+version = "0.6.1"
 source = { editable = "." }
 dependencies = [
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+    { name = "scipy" },
     { name = "torch" },
 ]
 
 [package.optional-dependencies]
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-    { name = "black" },
-    { name = "mypy" },
-    { name = "pre-commit" },
+    { name = "build" },
     { name = "pytest" },
     { name = "pytest-cov" },
     { name = "ruff" },
+    { name = "scikit-learn" },
+    { name = "towncrier" },
+    { name = "twine" },
+    { name = "yaml-changelog" },
 ]
 docs = [
     { name = "ipykernel" },
     { name = "jupyter" },
-    { name = "mystmd" },
+    { name = "jupyter-book" },
     { name = "notebook" },
 ]
 examples = [
@@ -1026,28 +1390,39 @@ examples = [
     { name = "tqdm" },
 ]
 
+[package.dev-dependencies]
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+]
+
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     { name = "jupyter", marker = "extra == 'docs'", specifier = ">=1.0" },
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     { name = "torchvision", marker = "extra == 'examples'", specifier = ">=0.15" },
+    { name = "towncrier", marker = "extra == 'dev'", specifier = ">=24.8.0" },
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+    { name = "twine", marker = "extra == 'dev'", specifier = ">=4.0.0" },
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 provides-extras = ["dev", "docs", "examples"]
 
+[package.metadata.requires-dev]
+dev = [{ name = "build", specifier = ">=1.3.0" }]
+
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 version = "1.2.2"
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 ]
 
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+version = "4.0.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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