Align frontend and backend with adapter_finetuning paper

Replace all LLM-specific references with real EEG benchmark data:
- 8 EEG datasets (BCIC-2a, PhysioNet, ISRUC, TUAB, TUEV, CHB-MIT, FACED, SEED-V)
- 7 adapter methods (LoRA, IA3, AdaLoRA, DoRA, OFT, Probe, Full Fine-tune)
- 7 foundation models (LaBraM, EEGPT, BIOT, BENDR, SignalJEPA, CBraMod, REVE)
- Remove official providers, MoE, flagged, merged features
- Update params from billions to millions scale
- Add real citations and dataset descriptions from the paper

backend/app/main.py

@@ -6,7 +6,7 @@ import logging
 setup_logging()
 logger = logging.getLogger(__name__)
 
-app = FastAPI(title="Open LLM Leaderboard API")
+app = FastAPI(title="EEG Finetune Arena API")
 
 @app.on_event("startup")
 async def startup_event():

backend/app/services/leaderboard.py

@@ -96,27 +96,71 @@ class LeaderboardService:
             raise HTTPException(status_code=500, detail=str(e))
 
     async def transform_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
-        """Transform raw data into the format expected by the frontend"""
+        """Transform raw data into the format expected by the frontend.
+
+        Evaluations correspond to EEG downstream datasets used in the
+        Parameter-Efficient Fine-Tuning benchmark for EEG Foundation Models:
+        - Motor Imagery: BCIC-2a (4-class), PhysioNet MI (4-class)
+        - Sleep Staging: ISRUC-SLEEP (5-class)
+        - Pathology Detection: TUAB (binary), TUEV (6-class)
+        - Seizure Detection: CHB-MIT (binary)
+        - Emotion Recognition: FACED (9-class), SEED-V (5-class)
+        """
         try:
-            # Extract model name for logging
             model_name = data.get("fullname", "Unknown")
             logger.debug(LogFormatter.info(f"Transforming data for model: {model_name}"))
 
-            # Create unique ID combining model name, precision and sha
-            unique_id = f"{data.get('fullname', 'Unknown')}_{data.get('Precision', 'Unknown')}_{data.get('Model sha', 'Unknown')}"
+            # Create unique ID combining model name, adapter method and dataset
+            unique_id = (
+                f"{data.get('fullname', 'Unknown')}"
+                f"_{data.get('adapter', 'Unknown')}"
+                f"_{data.get('Precision', 'Unknown')}"
+                f"_{data.get('Model sha', 'Unknown')}"
+            )
 
-            # EEG benchmark evaluations (placeholders matching current dataset schema)
+            # EEG benchmark evaluations — each dataset is a separate evaluation
+            # Scores are accuracy (0-1 raw) and normalized to 0-100 scale
             evaluations = {
-                "anli": {
-                    "name": "ANLI",
-                    "value": data.get("ANLI Raw", 0),
-                    "normalized_score": data.get("ANLI", 0)
+                "bcic2a": {
+                    "name": "BCIC-2a",
+                    "value": data.get("bcic2a_accuracy", 0),
+                    "normalized_score": data.get("bcic2a_accuracy", 0) * 100 if data.get("bcic2a_accuracy") else 0,
+                },
+                "physionet": {
+                    "name": "PhysioNet MI",
+                    "value": data.get("physionet_accuracy", 0),
+                    "normalized_score": data.get("physionet_accuracy", 0) * 100 if data.get("physionet_accuracy") else 0,
+                },
+                "isruc_sleep": {
+                    "name": "ISRUC-SLEEP",
+                    "value": data.get("isruc_sleep_accuracy", 0),
+                    "normalized_score": data.get("isruc_sleep_accuracy", 0) * 100 if data.get("isruc_sleep_accuracy") else 0,
+                },
+                "tuab": {
+                    "name": "TUAB",
+                    "value": data.get("tuab_accuracy", 0),
+                    "normalized_score": data.get("tuab_accuracy", 0) * 100 if data.get("tuab_accuracy") else 0,
+                },
+                "tuev": {
+                    "name": "TUEV",
+                    "value": data.get("tuev_accuracy", 0),
+                    "normalized_score": data.get("tuev_accuracy", 0) * 100 if data.get("tuev_accuracy") else 0,
+                },
+                "chbmit": {
+                    "name": "CHB-MIT",
+                    "value": data.get("chbmit_accuracy", 0),
+                    "normalized_score": data.get("chbmit_accuracy", 0) * 100 if data.get("chbmit_accuracy") else 0,
+                },
+                "faced": {
+                    "name": "FACED",
+                    "value": data.get("faced_accuracy", 0),
+                    "normalized_score": data.get("faced_accuracy", 0) * 100 if data.get("faced_accuracy") else 0,
+                },
+                "seedv": {
+                    "name": "SEED-V",
+                    "value": data.get("seedv_accuracy", 0),
+                    "normalized_score": data.get("seedv_accuracy", 0) * 100 if data.get("seedv_accuracy") else 0,
                 },
-                "logiqa": {
-                    "name": "LogiQA",
-                    "value": data.get("LogiQA Raw", 0),
-                    "normalized_score": data.get("LogiQA", 0)
-                }
             }
 
             features = {
@@ -126,39 +170,49 @@ class LeaderboardService:
             metadata = {
                 "upload_date": data.get("Upload To Hub Date"),
                 "submission_date": data.get("Submission Date"),
-                "generation": data.get("Generation"),
                 "base_model": data.get("Base Model"),
                 "hub_license": data.get("Hub License"),
-                "hub_hearts": data.get("Hub ❤️"),
+                "hub_hearts": data.get("Hub \u2764\uFE0F"),
                 "params_millions": data.get("#Params (M)"),
+                "adapter_method": data.get("adapter"),
+                "embed_dim": data.get("embed_dim"),
+                "trainable_params": data.get("trainable_params"),
             }
 
-            # Clean model type by removing emojis if present
-            original_type = data.get("Type", "")
-            model_type = original_type.lower().strip()
-
-            # Remove emojis and parentheses
-            if "(" in model_type:
-                model_type = model_type.split("(")[0].strip()
-            model_type = ''.join(c for c in model_type if not c in '🟢🔶🧪🏗️ ')
-
-            # Map model types for EEG domain
-            model_type_mapping = {
-                "fine-tuned": "fine-tuned",
-                "fine tuned": "fine-tuned",
-                "finetuned": "fine-tuned",
-                "fine_tuned": "fine-tuned",
-                "ft": "fine-tuned",
-                "pretrained": "pretrained",
-                "pre-trained": "pretrained",
-                "task-specific": "task-specific",
-                "foundation": "foundation",
+            # Adapter method / fine-tuning approach determines the model "type"
+            original_adapter = data.get("adapter", "")
+            adapter_lower = original_adapter.lower().strip() if original_adapter else ""
+
+            adapter_type_mapping = {
+                "lora": "lora",
+                "ia3": "ia3",
+                "adalora": "adalora",
+                "dora": "dora",
+                "oft": "oft",
+                "probe": "probe",
+                "full_finetune": "full_finetune",
+                "full": "full_finetune",
             }
 
-            mapped_type = model_type_mapping.get(model_type.lower().strip(), model_type)
+            mapped_type = adapter_type_mapping.get(adapter_lower, adapter_lower)
+
+            # Architecture is the foundation model name
+            original_arch = data.get("Architecture", "") or data.get("model", "")
+            arch_lower = original_arch.lower().strip() if original_arch else ""
+
+            arch_mapping = {
+                "labram": "LaBraM",
+                "labram_small": "LaBraM-Small",
+                "eegpt": "EEGPT",
+                "biot": "BIOT",
+                "bendr": "BENDR",
+                "signal_jepa": "SignalJEPA",
+                "signaljepa": "SignalJEPA",
+                "cbramod": "CBraMod",
+                "reve": "REVE",
+            }
 
-            if mapped_type != model_type:
-                logger.debug(LogFormatter.info(f"Model type mapped: {original_type} -> {mapped_type}"))
+            architecture = arch_mapping.get(arch_lower, original_arch)
 
             transformed_data = {
                 "id": unique_id,
@@ -168,12 +222,12 @@ class LeaderboardService:
                     "precision": data.get("Precision"),
                     "type": mapped_type,
                     "weight_type": data.get("Weight type"),
-                    "architecture": data.get("Architecture"),
-                    "average_score": data.get("Average ⬆️"),
+                    "architecture": architecture,
+                    "average_score": data.get("Average \u2B06\uFE0F"),
                 },
                 "evaluations": evaluations,
                 "features": features,
-                "metadata": metadata
+                "metadata": metadata,
             }
 
             logger.debug(LogFormatter.success(f"Successfully transformed data for {model_name}"))

frontend/src/pages/AboutPage/AboutPage.js

@@ -7,7 +7,7 @@ function AboutPage() {
     <Box sx={{ width: "100%", maxWidth: 1200, margin: "0 auto", py: 4, px: 0 }}>
       <PageHeader
         title="About the EEG Finetune Arena"
-        subtitle="Understanding our benchmarks and methodology"
+        subtitle="Parameter-Efficient Fine-Tuning Benchmark for EEG Foundation Models"
       />
 
       <Paper
@@ -24,24 +24,40 @@ function AboutPage() {
           What is the EEG Finetune Arena?
         </Typography>
         <Typography variant="body1" color="text.secondary" paragraph>
-          The EEG Finetune Arena is an open leaderboard for evaluating and
-          comparing EEG (electroencephalography) models. We provide a
-          standardized evaluation pipeline to assess how well different models
-          perform on EEG-related tasks, enabling fair and reproducible
-          comparisons across the community.
+          The EEG Finetune Arena is an open leaderboard for comparing
+          parameter-efficient fine-tuning (PEFT) methods applied to EEG
+          foundation models. We provide a standardized evaluation pipeline
+          across diverse EEG downstream tasks, enabling fair and reproducible
+          comparisons of how well different adapter methods can adapt
+          pre-trained EEG models.
         </Typography>
         <Typography variant="body1" color="text.secondary" paragraph>
-          Built on top of the{" "}
+          Built on top of{" "}
           <Link
             href="https://braindecode.org"
             target="_blank"
             rel="noopener noreferrer"
           >
             braindecode
+          </Link>
+          ,{" "}
+          <Link
+            href="https://moabb.neurotechx.com"
+            target="_blank"
+            rel="noopener noreferrer"
+          >
+            MOABB
+          </Link>
+          , and the{" "}
+          <Link
+            href="https://huggingface.co/docs/peft"
+            target="_blank"
+            rel="noopener noreferrer"
+          >
+            HuggingFace PEFT
           </Link>{" "}
-          library, the arena supports a variety of model architectures including
-          pretrained models, fine-tuned models, task-specific models, and
-          foundation models.
+          library, the arena evaluates combinations of 7 foundation models with
+          7 adapter methods across 14 EEG datasets.
         </Typography>
       </Paper>
 
@@ -56,31 +72,117 @@ function AboutPage() {
         }}
       >
         <Typography variant="h5" sx={{ mb: 2 }}>
-          Benchmarks
-        </Typography>
-        <Typography variant="body1" color="text.secondary" paragraph>
-          Models are currently evaluated on the following benchmarks:
+          Foundation Models
         </Typography>
         <Box component="ul" sx={{ pl: 3 }}>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>ANLI (Adversarial Natural Language Inference)</strong>:
-              Tests the model's ability to perform natural language inference on
-              adversarially constructed examples, evaluating robustness and
-              reasoning capabilities.
+              <strong>LaBraM</strong> - Vision Transformer for EEG with neural
+              tokenization (12 layers, 200D embedding). Pre-trained on large-scale
+              EEG data.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>EEGPT</strong> - Transformer with patch-based EEG
+              tokenization (~10M params, 8 layers, 512D embedding).
             </Typography>
           </li>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>LogiQA (Logical Reasoning QA)</strong>: Evaluates logical
-              reasoning abilities through multiple-choice questions covering
-              categorical, conditional, disjunctive, and conjunctive reasoning.
+              <strong>BIOT</strong> - Linear Attention Transformer for efficient
+              EEG processing (4 layers, 256D embedding).
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>BENDR</strong> - CNN + BERT-inspired Transformer encoder
+              (8 layers, 512D).
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>SignalJEPA</strong> - CNN + Transformer with JEPA-style
+              predictive self-supervised learning.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>CBraMod</strong> - Criss-Cross Transformer with separate
+              spatial and temporal attention (~4M params, 12 layers, 200D).
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>REVE</strong> - Vision Transformer with GEGLU and visual
+              encoding (22 layers, 512D embedding).
             </Typography>
           </li>
         </Box>
-        <Typography variant="body2" color="text.secondary">
-          Additional EEG-specific benchmarks will be added as the arena evolves.
+      </Paper>
+
+      <Paper
+        elevation={0}
+        sx={{
+          p: 4,
+          mb: 4,
+          border: "1px solid",
+          borderColor: "grey.200",
+          borderRadius: 2,
+        }}
+      >
+        <Typography variant="h5" sx={{ mb: 2 }}>
+          Adapter Methods
         </Typography>
+        <Box component="ul" sx={{ pl: 3 }}>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F9E9}"} LoRA</strong> - Low-Rank Adaptation (r=16,
+              alpha=32): injects trainable low-rank matrices, ~98% parameter
+              reduction.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F4A1}"} IA3</strong> - Infused Adapter by Inhibiting
+              and Amplifying Inner Activations: only learns scaling vectors,
+              ~99.5% parameter reduction.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F3AF}"} AdaLoRA</strong> - Adaptive Low-Rank
+              Adaptation: dynamic rank allocation across layers for optimal
+              budget distribution.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F52C}"} DoRA</strong> - Weight-Decomposed Low-Rank
+              Adaptation: decomposes weights into magnitude and direction
+              components.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F504}"} OFT</strong> - Orthogonal Fine-Tuning:
+              applies orthogonal transformations to preserve pre-trained
+              features.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F50D}"} Probe</strong> - Linear probing baseline:
+              freezes the encoder and trains only the classification head.
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>{"\u{1F527}"} Full Fine-tune</strong> - Updates all model
+              parameters (baseline for comparison).
+            </Typography>
+          </li>
+        </Box>
       </Paper>
 
       <Paper
@@ -94,32 +196,88 @@ function AboutPage() {
         }}
       >
         <Typography variant="h5" sx={{ mb: 2 }}>
-          Model Types
+          EEG Benchmarks
+        </Typography>
+        <Typography variant="body1" color="text.secondary" paragraph>
+          Models are evaluated across 8 primary downstream datasets spanning
+          diverse EEG tasks:
+        </Typography>
+
+        <Typography variant="h6" sx={{ mt: 2, mb: 1 }}>
+          Motor Imagery
         </Typography>
         <Box component="ul" sx={{ pl: 3 }}>
+          <li>
+            <Typography variant="body1" color="text.secondary">
+              <strong>BCIC-2a</strong> (BCI Competition IV 2a) - 4-class MI
+              (left hand, right hand, feet, tongue), 9 subjects, 22 channels
+            </Typography>
+          </li>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>{"\u{1F7E2}"} Pretrained</strong>: Base EEG models trained
-              with self-supervised learning on raw EEG data.
+              <strong>PhysioNet MI</strong> - 4-class MI (left hand, right hand,
+              feet, both hands), 109 subjects, 64 channels
+            </Typography>
+          </li>
+        </Box>
+
+        <Typography variant="h6" sx={{ mt: 2, mb: 1 }}>
+          Sleep Staging
+        </Typography>
+        <Box component="ul" sx={{ pl: 3 }}>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <strong>ISRUC-SLEEP</strong> (Group I) - 5-class sleep staging
+              (W, N1, N2, N3, REM), ~100 subjects, 6 channels, 30s windows
+            </Typography>
+          </li>
+        </Box>
+
+        <Typography variant="h6" sx={{ mt: 2, mb: 1 }}>
+          Pathology Detection
+        </Typography>
+        <Box component="ul" sx={{ pl: 3 }}>
+          <li>
+            <Typography variant="body1" color="text.secondary">
+              <strong>TUAB</strong> (TUH Abnormal v3.0.1) - Binary (normal /
+              abnormal), 290+ subjects, 16 channels, 10s windows
             </Typography>
           </li>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>{"\u{1F536}"} Fine-tuned</strong>: Models fine-tuned on
-              specific EEG datasets for particular downstream tasks.
+              <strong>TUEV</strong> (TUH Events v2.0.1) - 6-class event
+              classification (SPSW, GPED, PLED, EYEM, ARTF, BCKG), 200+
+              subjects, 21 channels
             </Typography>
           </li>
+        </Box>
+
+        <Typography variant="h6" sx={{ mt: 2, mb: 1 }}>
+          Seizure Detection
+        </Typography>
+        <Box component="ul" sx={{ pl: 3 }}>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>{"\u{1F9EA}"} Task-specific</strong>: Models designed for
-              specific EEG tasks such as sleep staging, motor imagery, or seizure
-              detection.
+              <strong>CHB-MIT</strong> - Binary seizure detection, 23 pediatric
+              subjects, 17 channels, 10s windows
+            </Typography>
+          </li>
+        </Box>
+
+        <Typography variant="h6" sx={{ mt: 2, mb: 1 }}>
+          Emotion Recognition
+        </Typography>
+        <Box component="ul" sx={{ pl: 3 }}>
+          <li>
+            <Typography variant="body1" color="text.secondary">
+              <strong>FACED</strong> - 9-class discrete emotion recognition, 123
+              subjects, 26 channels
             </Typography>
           </li>
           <li>
             <Typography variant="body1" color="text.secondary" paragraph>
-              <strong>{"\u{1F3D7}\u{FE0F}"} Foundation</strong>: Large-scale EEG
-              foundation models trained on diverse EEG datasets.
+              <strong>SEED-V</strong> - 5-class emotion recognition (Happy, Sad,
+              Neutral, Disgust, Fear), 62 channels
             </Typography>
           </li>
         </Box>
@@ -145,9 +303,33 @@ function AboutPage() {
                 target="_blank"
                 rel="noopener noreferrer"
               >
-                Braindecode Documentation
+                Braindecode
+              </Link>{" "}
+              - Deep learning toolbox for EEG decoding
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <Link
+                href="https://moabb.neurotechx.com"
+                target="_blank"
+                rel="noopener noreferrer"
+              >
+                MOABB
+              </Link>{" "}
+              - Mother of All BCI Benchmarks
+            </Typography>
+          </li>
+          <li>
+            <Typography variant="body1" color="text.secondary" paragraph>
+              <Link
+                href="https://huggingface.co/docs/peft"
+                target="_blank"
+                rel="noopener noreferrer"
+              >
+                HuggingFace PEFT
               </Link>{" "}
-              - The deep learning toolbox for EEG decoding
+              - Parameter-Efficient Fine-Tuning library
             </Typography>
           </li>
           <li>

frontend/src/pages/AddModelPage/components/ModelSubmissionForm/ModelSubmissionForm.js

@@ -56,13 +56,16 @@ const HELP_TEXTS = {
   modelType: (
     <Box sx={{ p: 1 }}>
       <Typography variant="subtitle2" sx={{ fontWeight: 600, mb: 0.5 }}>
-        Model Category
+        Adapter / Fine-tuning Method
       </Typography>
       <Typography variant="body2" sx={{ opacity: 0.9, lineHeight: 1.4 }}>
-        {"\u{1F7E2}"} Pretrained: Base EEG models trained with self-supervised learning{" "}
-        {"\u{1F536}"} Fine-tuned: Models fine-tuned on specific EEG datasets{" "}
-        {"\u{1F9EA}"} Task-specific: Models designed for specific EEG tasks{" "}
-        {"\u{1F3D7}\u{FE0F}"} Foundation: Large-scale EEG foundation models
+        {"\u{1F9E9}"} LoRA: Low-Rank Adaptation (~98% param reduction){" "}
+        {"\u{1F4A1}"} IA3: Scaling vectors only (~99.5% reduction){" "}
+        {"\u{1F3AF}"} AdaLoRA: Adaptive rank allocation{" "}
+        {"\u{1F52C}"} DoRA: Weight-decomposed adaptation{" "}
+        {"\u{1F504}"} OFT: Orthogonal fine-tuning{" "}
+        {"\u{1F50D}"} Probe: Linear probing (head only){" "}
+        {"\u{1F527}"} Full Fine-tune: All parameters updated
       </Typography>
     </Box>
   ),
@@ -117,7 +120,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
   const [formData, setFormData] = useState({
     modelName: "",
     revision: "main",
-    modelType: "fine-tuned",
+    modelType: "lora",
     precision: "float16",
     weightsType: "Original",
     baseModel: "",
@@ -298,7 +301,7 @@ function ModelSubmissionForm({ user, isAuthenticated }) {
                 setFormData({
                   modelName: "",
                   revision: "main",
-                  modelType: "fine-tuned",
+                  modelType: "lora",
                   precision: "float16",
                   weightsType: "Original",
                   baseModel: "",

frontend/src/pages/AddModelPage/components/SubmissionGuide/SubmissionGuide.js

@@ -58,8 +58,9 @@ const TUTORIAL_STEPS = [
         <Typography variant="body2" color="text.secondary">
           Your model should be <strong>public</strong> on the Hub and follow the{" "}
           <strong>username/model-id</strong> format (e.g.
-          braindecode/EEGNetv4). Specify the <strong>revision</strong>{" "}
-          (commit hash or branch) and <strong>model type</strong>.
+          braindecode/labram-lora-bcic2a). Specify the <strong>revision</strong>{" "}
+          (commit hash or branch) and <strong>adapter method</strong> used
+          for fine-tuning.
         </Typography>
         <DocLink href="https://huggingface.co/docs/hub/models-uploading">
           Model uploading guide
@@ -73,7 +74,8 @@ const TUTORIAL_STEPS = [
       <Stack spacing={2}>
         <Typography variant="body2" color="text.secondary">
           Make sure your model can be <strong>loaded locally</strong> before
-          submitting:
+          submitting. The arena evaluates EEG foundation models with PEFT
+          adapters:
         </Typography>
         <Box
           sx={{
@@ -92,17 +94,20 @@ const TUTORIAL_STEPS = [
           }}
         >
           <pre>
-            {`import braindecode
-from braindecode.models import EEGNetv4
+            {`from braindecode.models import LaBraM
+from peft import get_peft_model, LoraConfig
 
-# Load your model
-model = EEGNetv4(
-    n_chans=22,
-    n_outputs=4,
-    n_times=1000,
+# Load the foundation model
+model = LaBraM(n_chans=22, n_outputs=4, n_times=1000)
+
+# Apply a PEFT adapter (e.g., LoRA)
+peft_config = LoraConfig(
+    r=16, lora_alpha=32,
+    target_modules=["qkv"],
+    lora_dropout=0.1,
 )
-# Or from Hugging Face Hub
-# model = braindecode.models.load("your-username/your-model", revision="main")`}
+model = get_peft_model(model, peft_config)
+print(model.print_trainable_parameters())`}
           </pre>
         </Box>
         <DocLink href="https://braindecode.org/stable/api.html">
@@ -131,11 +136,11 @@ model = EEGNetv4(
     content: (
       <Stack spacing={2}>
         <Typography variant="body2" color="text.secondary">
-          Your model card must include: <strong>architecture</strong>,{" "}
+          Your model card must include: <strong>foundation model</strong> (e.g.
+          LaBraM, EEGPT, BIOT), <strong>adapter method</strong> (LoRA, IA3, etc.),{" "}
           <strong>training details</strong>,{" "}
           <strong>dataset information</strong> (EEG paradigm, number of channels,
-          sampling rate), intended use, limitations, and{" "}
-          <strong>performance metrics</strong>.
+          sampling rate), and <strong>performance metrics</strong>.
         </Typography>
         <DocLink href="https://huggingface.co/docs/hub/model-cards">
           Model cards guide
@@ -151,10 +156,10 @@ model = EEGNetv4(
           Ensure your model is <strong>public</strong>, uses{" "}
           <strong>safetensors</strong> format, has a{" "}
           <strong>license tag</strong>, and <strong>loads correctly</strong>{" "}
-          with the provided code.
+          with braindecode + PEFT.
         </Typography>
-        <DocLink href="https://huggingface.co/docs/hub/repositories-getting-started">
-          Sharing best practices
+        <DocLink href="https://huggingface.co/docs/peft">
+          HuggingFace PEFT documentation
         </DocLink>
       </Stack>
     ),

frontend/src/pages/LeaderboardPage/components/Leaderboard/Leaderboard.js

@@ -221,8 +221,7 @@ const Leaderboard = () => {
     const hasValidFilterCounts =
       state.countsReady &&
       state.filterCounts &&
-      state.filterCounts.normal &&
-      state.filterCounts.officialOnly;
+      state.filterCounts.normal;
 
     return {
       isInitialLoading,

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/FilteredModelCount.js

@@ -5,8 +5,6 @@ import { useLeaderboard } from "../../context/LeaderboardContext";
 
 const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
   const { state } = useLeaderboard();
-  const isOfficialProviderActive = state.filters.isOfficialProviderActive;
-  const { officialOnly: officialOnlyCounts } = state.filterCounts;
 
   return useMemo(() => {
     if (loading) {
@@ -15,12 +13,9 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
         currentFilteredCount: 0,
         totalPinnedCount: 0,
         filteredPinnedCount: 0,
-        isOfficialProviderActive,
       };
     }
-    const displayCount = isOfficialProviderActive
-      ? officialOnlyCounts.officialProviders
-      : totalCount;
+    const displayCount = totalCount;
 
     // Calculate total number of pinned models
     const totalPinnedCount =
@@ -33,7 +28,6 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
       paramsRange: state.filters.paramsRange,
       searchValue: state.filters.search,
       selectedBooleanFilters: state.filters.booleanFilters,
-      isOfficialProviderActive: state.filters.isOfficialProviderActive,
     };
 
     // Check each pinned model if it would pass filters without its pinned status
@@ -41,18 +35,6 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
       data?.filter((model) => {
         if (!model.isPinned) return false;
 
-        // Check each filter criteria
-
-        // Filter by official providers
-        if (filterConfig.isOfficialProviderActive) {
-          if (
-            !model.features?.is_official_provider &&
-            !model.metadata?.is_official_provider
-          ) {
-            return false;
-          }
-        }
-
         // Filter by precision
         if (filterConfig.selectedPrecisions.length > 0) {
           if (
@@ -74,8 +56,8 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
           }
         }
 
-        // Filter by parameters
-        const params = model.metadata.params_billions;
+        // Filter by parameters (in millions for EEG)
+        const params = model.metadata.params_millions;
         if (
           params < filterConfig.paramsRange[0] ||
           params >= filterConfig.paramsRange[1]
@@ -99,12 +81,6 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
               const filterValue =
                 typeof filter === "object" ? filter.value : filter;
 
-              // Maintainer's Highlight keeps positive logic
-              if (filterValue === "is_official_provider") {
-                return model.features[filterValue];
-              }
-
-              // For all other filters, invert the logic
               if (filterValue === "is_not_available_on_hub") {
                 return model.features[filterValue];
               }
@@ -125,7 +101,6 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
       currentFilteredCount: filteredCount,
       totalPinnedCount,
       filteredPinnedCount,
-      isOfficialProviderActive,
     };
   }, [
     loading,
@@ -133,8 +108,6 @@ const useModelCount = ({ totalCount, filteredCount, data, table, loading }) => {
     filteredCount,
     data,
     state.filters,
-    isOfficialProviderActive,
-    officialOnlyCounts.officialProviders,
   ]);
 };
 
@@ -205,7 +178,6 @@ const FilteredModelCount = React.memo(
       currentFilteredCount,
       totalPinnedCount,
       filteredPinnedCount,
-      isOfficialProviderActive,
     } = useModelCount({
       totalCount,
       filteredCount,
@@ -233,7 +205,7 @@ const FilteredModelCount = React.memo(
         <CountTypography value="/" loading={loading} />
         <CountTypography
           value={displayCount}
-          color={isOfficialProviderActive ? "secondary.main" : "text.primary"}
+          color="text.primary"
           loading={loading}
         />
       </Box>

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/Filters.js

@@ -31,7 +31,7 @@ import { COLUMN_TOOLTIPS } from "../../constants/tooltips";
 
 const getTooltipContent = (title) => {
   switch (title) {
-    case "Model Type":
+    case "Adapter Method":
       return COLUMN_TOOLTIPS.ARCHITECTURE;
     case "Precision format":
       return COLUMN_TOOLTIPS.PRECISION;
@@ -64,7 +64,7 @@ const FilterGroup = ({
   const handleInputChange = useCallback(
     (index) => (event) => {
       const value = event.target.value === "" ? "" : Number(event.target.value);
-      if (value === "" || (value >= -1 && value <= 140)) {
+      if (value === "" || (value >= -1 && value <= 500)) {
         const newRange = [...localParamsRange];
         newRange[index] = value;
         setLocalParamsRange(newRange);
@@ -130,7 +130,7 @@ const FilterGroup = ({
                 type="number"
                 inputProps={{
                   min: -1,
-                  max: 140,
+                  max: 500,
                   style: {
                     width: "45px",
                     textAlign: "center",
@@ -153,7 +153,7 @@ const FilterGroup = ({
                 type="number"
                 inputProps={{
                   min: -1,
-                  max: 140,
+                  max: 500,
                   style: {
                     width: "45px",
                     textAlign: "center",
@@ -175,15 +175,16 @@ const FilterGroup = ({
             onChange={handleLocalRangeChange}
             valueLabelDisplay="auto"
             min={-1}
-            max={140}
+            max={500}
             step={1}
             marks={[
               { value: -1, label: "All" },
-              { value: 7, label: "7" },
-              { value: 70, label: "70" },
-              { value: 140, label: "140" },
+              { value: 10, label: "10" },
+              { value: 50, label: "50" },
+              { value: 200, label: "200" },
+              { value: 500, label: "500" },
             ]}
-            valueLabelFormat={(value) => (value === -1 ? "All" : `${value}B`)}
+            valueLabelFormat={(value) => (value === -1 ? "All" : `${value}M`)}
             sx={{
               "& .MuiSlider-rail": {
                 height: 10,
@@ -238,7 +239,7 @@ const FilterGroup = ({
               "& .MuiSlider-markLabel": {
                 fontSize: "0.875rem",
                 "&::after": {
-                  content: '"B"',
+                  content: '"M"',
                   marginLeft: "1px",
                   opacity: 0.5,
                 },
@@ -336,7 +337,7 @@ const LeaderboardFilters = ({
   onPrecisionsChange = () => {},
   selectedTypes = MODEL_TYPE_ORDER,
   onTypesChange = () => {},
-  paramsRange = [-1, 140],
+  paramsRange = [-1, 500],
   onParamsRangeChange = () => {},
   selectedBooleanFilters = [],
   onBooleanFiltersChange = () => {},
@@ -348,13 +349,7 @@ const LeaderboardFilters = ({
   const [localParamsRange, setLocalParamsRange] = useState(paramsRange);
   const stableTimerRef = useRef(null);
   const { state, actions } = useLeaderboard();
-  const { normal: filterCounts, officialOnly: officialOnlyCounts } =
-    state.filterCounts;
-  const isOfficialProviderActive = state.filters.isOfficialProviderActive;
-  const currentCounts = useMemo(
-    () => (isOfficialProviderActive ? officialOnlyCounts : filterCounts),
-    [isOfficialProviderActive, officialOnlyCounts, filterCounts]
-  );
+  const { normal: filterCounts } = state.filterCounts;
 
   useEffect(() => {
     setLocalParamsRange(paramsRange);
@@ -403,17 +398,10 @@ const LeaderboardFilters = ({
   };
 
   // Filter options based on their hide property
-  const showFilterOptions = BOOLEAN_FILTER_OPTIONS.filter(
-    (option) => !option.hide
-  );
   const hideFilterOptions = BOOLEAN_FILTER_OPTIONS.filter(
     (option) => option.hide
   );
 
-  const handleOfficialProviderToggle = () => {
-    actions.toggleOfficialProvider();
-  };
-
   return loading ? null : (
     <Box>
       <Accordion
@@ -443,403 +431,246 @@ const LeaderboardFilters = ({
           }}
         >
           <Box>
-            <Grid container spacing={3}>
-              <Grid item xs={12} md={9} sx={{ display: "flex" }}>
-                <Box
-                  sx={{
-                    backgroundColor: (theme) =>
-                      alpha(theme.palette.primary.main, 0.02),
-                    border: "1px solid",
-                    borderColor: (theme) =>
-                      alpha(theme.palette.primary.main, 0.2),
-                    borderRadius: 1,
-                    p: 3,
-                    position: "relative",
-                    width: "100%",
-                    display: "flex",
-                    flexDirection: "column",
-                    "&:hover": {
-                      borderColor: (theme) =>
-                        alpha(theme.palette.primary.main, 0.3),
-                      backgroundColor: (theme) =>
-                        alpha(theme.palette.primary.main, 0.03),
-                    },
-                    transition: (theme) =>
-                      theme.transitions.create(
-                        ["border-color", "background-color"],
-                        {
-                          duration: theme.transitions.duration.short,
-                        }
-                      ),
-                  }}
-                >
-                  <Typography
-                    variant="h6"
-                    sx={{
-                      mb: 3,
-                      fontWeight: 600,
-                      color: "text.primary",
-                      fontSize: "1.1rem",
-                    }}
-                  >
-                    Advanced Filters
-                  </Typography>
-                  <Box sx={{ flex: 1 }}>
-                    <Grid container spacing={3} sx={{ flex: 1 }}>
-                      <Grid item xs={12} md={6}>
-                        <Box>
-                          <FilterGroup
-                            title="Precision format"
-                            tooltip={COLUMN_TOOLTIPS.PRECISION}
-                          >
-                            {FILTER_PRECISIONS.map((precision) => (
-                              <FilterTag
-                                key={precision}
-                                label={precision}
-                                checked={selectedPrecisions.includes(precision)}
-                                onChange={() =>
-                                  handlePrecisionToggle(precision)
-                                }
-                                count={currentCounts.precisions[precision]}
-                                showCheckbox={true}
-                              />
-                            ))}
-                          </FilterGroup>
-                        </Box>
-                      </Grid>
-
-                      <Grid item xs={12} md={6}>
-                        <Box sx={{ position: "relative" }}>
-                          <FilterGroup
-                            title="Parameters"
-                            tooltip={COLUMN_TOOLTIPS.PARAMETERS}
-                            paramsRange={paramsRange}
-                            onParamsRangeChange={onParamsRangeChange}
-                          >
-                            <Box
-                              sx={{
-                                width: "100%",
-                                display: "flex",
-                                alignItems: "center",
-                                gap: 2,
-                              }}
-                            >
-                              <Box sx={{ flex: 1 }}>
-                                <Slider
-                                  value={localParamsRange}
-                                  onChange={handleParamsRangeChange}
-                                  onChangeCommitted={
-                                    handleParamsRangeChangeCommitted
-                                  }
-                                  valueLabelDisplay="auto"
-                                  min={-1}
-                                  max={140}
-                                  step={1}
-                                  marks={[
-                                    { value: -1, label: "" },
-                                    { value: 0, label: "0" },
-                                    { value: 7, label: "7" },
-                                    { value: 70, label: "70" },
-                                    { value: 140, label: "140" },
-                                  ]}
-                                  sx={{
-                                    "& .MuiSlider-rail": {
-                                      height: 10,
-                                      backgroundColor: "background.paper",
-                                      border: "1px solid",
-                                      borderColor: "divider",
-                                      opacity: 1,
-                                    },
-                                    "& .MuiSlider-track": {
-                                      height: 10,
-                                      border: "1px solid",
-                                      borderColor: (theme) =>
-                                        alpha(
-                                          theme.palette.primary.main,
-                                          theme.palette.mode === "light"
-                                            ? 0.3
-                                            : 0.5
-                                        ),
-                                      backgroundColor: (theme) =>
-                                        alpha(
-                                          theme.palette.primary.main,
-                                          theme.palette.mode === "light"
-                                            ? 0.1
-                                            : 0.2
-                                        ),
-                                    },
-                                    "& .MuiSlider-thumb": {
-                                      width: 20,
-                                      height: 20,
-                                      backgroundColor: "background.paper",
-                                      border: "1px solid",
-                                      borderColor: "primary.main",
-                                      "&:hover, &.Mui-focusVisible": {
-                                        boxShadow: (theme) =>
-                                          `0 0 0 8px ${alpha(
-                                            theme.palette.primary.main,
-                                            theme.palette.mode === "light"
-                                              ? 0.08
-                                              : 0.16
-                                          )}`,
-                                      },
-                                      "&.Mui-active": {
-                                        boxShadow: (theme) =>
-                                          `0 0 0 12px ${alpha(
-                                            theme.palette.primary.main,
-                                            theme.palette.mode === "light"
-                                              ? 0.08
-                                              : 0.16
-                                          )}`,
-                                      },
-                                    },
-                                    "& .MuiSlider-mark": {
-                                      backgroundColor: "text.disabled",
-                                      height: 2,
-                                      width: 2,
-                                      borderRadius: "50%",
-                                    },
-                                    "& .MuiSlider-markLabel": {
-                                      color: "text.secondary",
-                                    },
-                                  }}
-                                />
-                              </Box>
-                            </Box>
-                          </FilterGroup>
-                        </Box>
-                      </Grid>
-
-                      {/* Deuxième ligne */}
-                      <Grid item xs={12} md={6}>
-                        <Box>
-                          <FilterGroup
-                            title="Model Type"
-                            tooltip={COLUMN_TOOLTIPS.ARCHITECTURE}
-                          >
-                            {MODEL_TYPE_ORDER.sort(
-                              (a, b) =>
-                                MODEL_TYPES[a].order - MODEL_TYPES[b].order
-                            ).map((type) => (
-                              <FilterTag
-                                key={type}
-                                label={`${MODEL_TYPES[type]?.icon} ${
-                                  MODEL_TYPES[type]?.label || type
-                                }`}
-                                checked={selectedTypes.includes(type)}
-                                onChange={() => {
-                                  const newTypes = selectedTypes.includes(type)
-                                    ? selectedTypes.filter((t) => t !== type)
-                                    : [...selectedTypes, type];
-                                  onTypesChange(newTypes);
-                                }}
-                                count={currentCounts.modelTypes[type]}
-                                variant="tag"
-                                showCheckbox={true}
-                              />
-                            ))}
-                          </FilterGroup>
-                        </Box>
-                      </Grid>
-
-                      <Grid item xs={12} md={6}>
-                        <Box>
-                          <FilterGroup
-                            title="Flags"
-                            tooltip={COLUMN_TOOLTIPS.FLAGS}
-                          >
-                            {hideFilterOptions.map((filter) => (
-                              <FilterTag
-                                key={filter.value}
-                                label={filter.label}
-                                checked={
-                                  !selectedBooleanFilters.includes(filter.value)
-                                }
-                                onChange={() => {
-                                  const newFilters =
-                                    selectedBooleanFilters.includes(
-                                      filter.value
-                                    )
-                                      ? selectedBooleanFilters.filter(
-                                          (f) => f !== filter.value
-                                        )
-                                      : [
-                                          ...selectedBooleanFilters,
-                                          filter.value,
-                                        ];
-                                  onBooleanFiltersChange(newFilters);
-                                }}
-                                count={
-                                  filter.value === "is_moe"
-                                    ? currentCounts.mixtureOfExperts
-                                    : filter.value === "is_flagged"
-                                    ? currentCounts.flagged
-                                    : filter.value === "is_merged"
-                                    ? currentCounts.merged
-                                    : filter.value === "is_not_available_on_hub"
-                                    ? currentCounts.notOnHub
-                                    : 0
-                                }
-                                isHideFilter={false}
-                                totalCount={data.length}
-                                showCheckbox={true}
-                              />
-                            ))}
-                          </FilterGroup>
-                        </Box>
-                      </Grid>
-                    </Grid>
-                  </Box>
-                </Box>
-              </Grid>
-
-              <Grid item xs={12} md={3} sx={{ display: "flex" }}>
-                <Box
-                  sx={{
-                    backgroundColor: (theme) =>
-                      alpha(theme.palette.secondary.main, 0.02),
-                    border: "1px solid",
-                    borderColor: (theme) =>
-                      alpha(theme.palette.secondary.main, 0.15),
-                    borderRadius: 1,
-                    p: 3,
-                    position: "relative",
-                    width: "100%",
-                    display: "flex",
-                    flexDirection: "column",
-                    alignItems: "center",
-                    justifyContent: "center",
-                    textAlign: "center",
-                    minHeight: "100%",
-                    "&:hover": {
-                      borderColor: (theme) =>
-                        alpha(theme.palette.secondary.main, 0.25),
-                      backgroundColor: (theme) =>
-                        alpha(theme.palette.secondary.main, 0.03),
-                    },
-                    transition: (theme) =>
-                      theme.transitions.create(
-                        ["border-color", "background-color"],
-                        {
-                          duration: theme.transitions.duration.short,
-                        }
-                      ),
-                  }}
-                >
-                  <Box
-                    sx={{
-                      display: "flex",
-                      flexDirection: "column",
-                      alignItems: "center",
-                      gap: 2,
-                    }}
-                  >
-                    <Typography
-                      variant="h6"
-                      sx={{
-                        fontWeight: 600,
-                        color: "text.primary",
-                        fontSize: "1.1rem",
-                        display: "flex",
-                        alignItems: "center",
-                        gap: 1,
-                      }}
-                    >
-                      Official Models
-                    </Typography>
-                    <Typography
-                      variant="body2"
-                      sx={{
-                        color: "text.secondary",
-                        fontSize: "0.8rem",
-                        lineHeight: 1.4,
-                        maxWidth: "280px",
-                      }}
-                    >
-                      Show only models that are officially provided and
-                      maintained by their original creators.
-                    </Typography>
-                    <Box
-                      sx={{
-                        display: "flex",
-                        flexDirection: "column",
-                        gap: 1,
-                        width: "100%",
-                        alignItems: "center",
-                      }}
-                    >
-                      {showFilterOptions.map((filter) => (
+            <Box
+              sx={{
+                backgroundColor: (theme) =>
+                  alpha(theme.palette.primary.main, 0.02),
+                border: "1px solid",
+                borderColor: (theme) =>
+                  alpha(theme.palette.primary.main, 0.2),
+                borderRadius: 1,
+                p: 3,
+                position: "relative",
+                width: "100%",
+                display: "flex",
+                flexDirection: "column",
+                "&:hover": {
+                  borderColor: (theme) =>
+                    alpha(theme.palette.primary.main, 0.3),
+                  backgroundColor: (theme) =>
+                    alpha(theme.palette.primary.main, 0.03),
+                },
+                transition: (theme) =>
+                  theme.transitions.create(
+                    ["border-color", "background-color"],
+                    {
+                      duration: theme.transitions.duration.short,
+                    }
+                  ),
+              }}
+            >
+              <Typography
+                variant="h6"
+                sx={{
+                  mb: 3,
+                  fontWeight: 600,
+                  color: "text.primary",
+                  fontSize: "1.1rem",
+                }}
+              >
+                Advanced Filters
+              </Typography>
+              <Box sx={{ flex: 1 }}>
+                <Grid container spacing={3} sx={{ flex: 1 }}>
+                  <Grid item xs={12} md={6}>
+                    <Box>
+                      <FilterGroup
+                        title="Precision format"
+                        tooltip={COLUMN_TOOLTIPS.PRECISION}
+                      >
+                        {FILTER_PRECISIONS.map((precision) => (
+                          <FilterTag
+                            key={precision}
+                            label={precision}
+                            checked={selectedPrecisions.includes(precision)}
+                            onChange={() =>
+                              handlePrecisionToggle(precision)
+                            }
+                            count={filterCounts.precisions[precision]}
+                            showCheckbox={true}
+                          />
+                        ))}
+                      </FilterGroup>
+                    </Box>
+                  </Grid>
+
+                  <Grid item xs={12} md={6}>
+                    <Box sx={{ position: "relative" }}>
+                      <FilterGroup
+                        title="Parameters"
+                        tooltip={COLUMN_TOOLTIPS.PARAMETERS}
+                        paramsRange={paramsRange}
+                        onParamsRangeChange={onParamsRangeChange}
+                      >
                         <Box
-                          key={filter.value}
                           sx={{
+                            width: "100%",
                             display: "flex",
-                            flexDirection: "column",
                             alignItems: "center",
-                            gap: 1,
+                            gap: 2,
                           }}
                         >
+                          <Box sx={{ flex: 1 }}>
+                            <Slider
+                              value={localParamsRange}
+                              onChange={handleParamsRangeChange}
+                              onChangeCommitted={
+                                handleParamsRangeChangeCommitted
+                              }
+                              valueLabelDisplay="auto"
+                              min={-1}
+                              max={500}
+                              step={1}
+                              marks={[
+                                { value: -1, label: "" },
+                                { value: 0, label: "0" },
+                                { value: 10, label: "10" },
+                                { value: 50, label: "50" },
+                                { value: 200, label: "200" },
+                                { value: 500, label: "500" },
+                              ]}
+                              sx={{
+                                "& .MuiSlider-rail": {
+                                  height: 10,
+                                  backgroundColor: "background.paper",
+                                  border: "1px solid",
+                                  borderColor: "divider",
+                                  opacity: 1,
+                                },
+                                "& .MuiSlider-track": {
+                                  height: 10,
+                                  border: "1px solid",
+                                  borderColor: (theme) =>
+                                    alpha(
+                                      theme.palette.primary.main,
+                                      theme.palette.mode === "light"
+                                        ? 0.3
+                                        : 0.5
+                                    ),
+                                  backgroundColor: (theme) =>
+                                    alpha(
+                                      theme.palette.primary.main,
+                                      theme.palette.mode === "light"
+                                        ? 0.1
+                                        : 0.2
+                                    ),
+                                },
+                                "& .MuiSlider-thumb": {
+                                  width: 20,
+                                  height: 20,
+                                  backgroundColor: "background.paper",
+                                  border: "1px solid",
+                                  borderColor: "primary.main",
+                                  "&:hover, &.Mui-focusVisible": {
+                                    boxShadow: (theme) =>
+                                      `0 0 0 8px ${alpha(
+                                        theme.palette.primary.main,
+                                        theme.palette.mode === "light"
+                                          ? 0.08
+                                          : 0.16
+                                      )}`,
+                                  },
+                                  "&.Mui-active": {
+                                    boxShadow: (theme) =>
+                                      `0 0 0 12px ${alpha(
+                                        theme.palette.primary.main,
+                                        theme.palette.mode === "light"
+                                          ? 0.08
+                                          : 0.16
+                                      )}`,
+                                  },
+                                },
+                                "& .MuiSlider-mark": {
+                                  backgroundColor: "text.disabled",
+                                  height: 2,
+                                  width: 2,
+                                  borderRadius: "50%",
+                                },
+                                "& .MuiSlider-markLabel": {
+                                  color: "text.secondary",
+                                },
+                              }}
+                            />
+                          </Box>
+                        </Box>
+                      </FilterGroup>
+                    </Box>
+                  </Grid>
+
+                  <Grid item xs={12} md={6}>
+                    <Box>
+                      <FilterGroup
+                        title="Adapter Method"
+                        tooltip={COLUMN_TOOLTIPS.ARCHITECTURE}
+                      >
+                        {MODEL_TYPE_ORDER.sort(
+                          (a, b) =>
+                            MODEL_TYPES[a].order - MODEL_TYPES[b].order
+                        ).map((type) => (
                           <FilterTag
+                            key={type}
+                            label={`${MODEL_TYPES[type]?.icon} ${
+                              MODEL_TYPES[type]?.label || type
+                            }`}
+                            checked={selectedTypes.includes(type)}
+                            onChange={() => {
+                              const newTypes = selectedTypes.includes(type)
+                                ? selectedTypes.filter((t) => t !== type)
+                                : [...selectedTypes, type];
+                              onTypesChange(newTypes);
+                            }}
+                            count={filterCounts.modelTypes[type]}
+                            variant="tag"
+                            showCheckbox={true}
+                          />
+                        ))}
+                      </FilterGroup>
+                    </Box>
+                  </Grid>
+
+                  <Grid item xs={12} md={6}>
+                    <Box>
+                      <FilterGroup
+                        title="Flags"
+                        tooltip={COLUMN_TOOLTIPS.FLAGS}
+                      >
+                        {hideFilterOptions.map((filter) => (
+                          <FilterTag
+                            key={filter.value}
                             label={filter.label}
                             checked={
-                              filter.value === "is_official_provider"
-                                ? isOfficialProviderActive
-                                : selectedBooleanFilters.includes(filter.value)
-                            }
-                            onChange={
-                              filter.value === "is_official_provider"
-                                ? handleOfficialProviderToggle
-                                : () => handleBooleanFilterToggle(filter.value)
+                              !selectedBooleanFilters.includes(filter.value)
                             }
+                            onChange={() => {
+                              const newFilters =
+                                selectedBooleanFilters.includes(
+                                  filter.value
+                                )
+                                  ? selectedBooleanFilters.filter(
+                                      (f) => f !== filter.value
+                                    )
+                                  : [
+                                      ...selectedBooleanFilters,
+                                      filter.value,
+                                    ];
+                              onBooleanFiltersChange(newFilters);
+                            }}
                             count={
-                              filter.value === "is_official_provider"
-                                ? currentCounts.officialProviders
+                              filter.value === "is_not_available_on_hub"
+                                ? filterCounts.notOnHub
                                 : 0
                             }
+                            isHideFilter={false}
+                            totalCount={data.length}
                             showCheckbox={true}
-                            variant="secondary"
                           />
-                          <Box
-                            sx={{
-                              display: "flex",
-                              alignItems: "center",
-                              gap: 0.5,
-                              color: "text.secondary",
-                              fontSize: "0.75rem",
-                            }}
-                          >
-                            <Box
-                              component="span"
-                              sx={{
-                                width: 6,
-                                height: 6,
-                                borderRadius: "50%",
-                                backgroundColor: (
-                                  filter.value ===
-                                  "is_official_provider"
-                                    ? isOfficialProviderActive
-                                    : selectedBooleanFilters.includes(
-                                        filter.value
-                                      )
-                                )
-                                  ? "success.main"
-                                  : "text.disabled",
-                              }}
-                            />
-                            {(
-                              filter.value === "is_official_provider"
-                                ? isOfficialProviderActive
-                                : selectedBooleanFilters.includes(filter.value)
-                            )
-                              ? "Filter active"
-                              : "Filter inactive"}
-                          </Box>
-                        </Box>
-                      ))}
+                        ))}
+                      </FilterGroup>
                     </Box>
-                  </Box>
-                </Box>
-              </Grid>
-            </Grid>
+                  </Grid>
+                </Grid>
+              </Box>
+            </Box>
           </Box>
         </AccordionDetails>
       </Accordion>

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/QuickFilters.js

@@ -65,41 +65,17 @@ export const QuickFiltersSkeleton = () => (
           }}
         />
       ))}
-      <Skeleton
-        height={32}
-        sx={{
-          width: { xs: "100%", md: 150 },
-          borderRadius: 1,
-          ml: 2,
-        }}
-      />
     </Box>
   </Box>
 );
 
 const QuickFilters = ({ totalCount = 0, loading = false }) => {
   const { state, actions } = useLeaderboard();
-  const { normal: filterCounts, officialOnly: officialOnlyCounts } =
-    state.filterCounts;
-  const isOfficialProviderActive = state.filters.isOfficialProviderActive;
+  const { normal: filterCounts } = state.filterCounts;
   const currentParams = state.filters.paramsRange;
 
-  const currentCounts = useMemo(
-    () => (isOfficialProviderActive ? officialOnlyCounts : filterCounts),
-    [isOfficialProviderActive, officialOnlyCounts, filterCounts]
-  );
-
   const modelSizePresets = useMemo(
-    () =>
-      QUICK_FILTER_PRESETS.filter(
-        (preset) => preset.id !== "official_providers"
-      ),
-    []
-  );
-
-  const officialProvidersPreset = useMemo(
-    () =>
-      QUICK_FILTER_PRESETS.find((preset) => preset.id === "official_providers"),
+    () => QUICK_FILTER_PRESETS,
     []
   );
 
@@ -110,7 +86,7 @@ const QuickFilters = ({ totalCount = 0, loading = false }) => {
         currentParams[1] === preset.filters.paramsRange[1];
 
       if (isActive) {
-        actions.setFilter("paramsRange", [-1, 140]); // Reset to default
+        actions.setFilter("paramsRange", [-1, 500]); // Reset to default
       } else {
         actions.setFilter("paramsRange", preset.filters.paramsRange);
       }
@@ -121,15 +97,11 @@ const QuickFilters = ({ totalCount = 0, loading = false }) => {
   const getPresetCount = useCallback(
     (preset) => {
       const range = preset.id.split("_")[0];
-      return currentCounts.parameterRanges[range] || 0;
+      return filterCounts.parameterRanges[range] || 0;
     },
-    [currentCounts]
+    [filterCounts]
   );
 
-  const handleOfficialProviderToggle = useCallback(() => {
-    actions.toggleOfficialProvider();
-  }, [actions]);
-
   if (loading) {
     return <QuickFiltersSkeleton />;
   }
@@ -194,28 +166,6 @@ const QuickFilters = ({ totalCount = 0, loading = false }) => {
             />
           ))}
         </Box>
-
-        <Box
-          sx={{
-            width: { xs: "100%", md: "100%", lg: "auto" },
-            display: "flex",
-          }}
-        >
-          {officialProvidersPreset && (
-            <FilterTag
-              label={officialProvidersPreset.label}
-              checked={isOfficialProviderActive}
-              onChange={handleOfficialProviderToggle}
-              count={currentCounts.officialProviders}
-              totalCount={totalCount}
-              showCheckbox={true}
-              variant="secondary"
-              sx={{
-                width: { xs: "100%", md: "100%", lg: "auto" },
-              }}
-            />
-          )}
-        </Box>
       </Box>
     </Box>
   );

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/SearchBar.js

@@ -148,8 +148,7 @@ const SearchBar = ({
     state.filters.precisions.length !== FILTER_PRECISIONS.length ||
     state.filters.types.length !== MODEL_TYPE_ORDER.length ||
     state.filters.paramsRange[0] !== -1 ||
-    state.filters.paramsRange[1] !== 140 ||
-    state.filters.isOfficialProviderActive;
+    state.filters.paramsRange[1] !== 500;
 
   const shouldShowReset = localValue || hasActiveFilters;
 
@@ -185,7 +184,7 @@ const SearchBar = ({
         <InputBase
           value={localValue}
           onChange={handleLocalChange}
-          placeholder='Search by model name • try "meta @architecture:llama @license:mit"'
+          placeholder='Search by model name • try "labram @type:lora @architecture:labram"'
           sx={{
             flex: 1,
             fontSize: "1rem",
@@ -207,8 +206,6 @@ const SearchBar = ({
             filteredCount={data.length}
             hasFilterChanges={hasActiveFilters}
             loading={loading}
-            isOfficialProviderActive={state.filters.isOfficialProviderActive}
-            officialProvidersCount={state.filters.officialProvidersCount}
             size="large"
             data={data}
             table={table}

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Filters/hooks/useOfficialProvidersMode.js

@@ -1,130 +1,9 @@
-import { useCallback, useState, useEffect, useRef } from "react";
-import { useSearchParams } from "react-router-dom";
-
-const useRouterSearchParams = () => {
-  try {
-    return useSearchParams();
-  } catch {
-    return [null, () => {}];
-  }
-};
+// Official providers mode is not used in the EEG Finetune Arena.
+// This hook is kept as a no-op for compatibility with any remaining references.
 
 export const useOfficialProvidersMode = () => {
-  const [isOfficialProviderActive, setIsOfficialProviderActive] =
-    useState(false);
-  const [searchParams, setSearchParams] = useRouterSearchParams();
-  const normalFiltersRef = useRef(null);
-  const isInitialLoadRef = useRef(true);
-  const lastToggleSourceRef = useRef(null);
-
-  // Effect to handle initial state and updates
-  useEffect(() => {
-    if (!searchParams) return;
-
-    const filters = searchParams.get("filters");
-    const isHighlighted =
-      filters?.includes("is_official_provider") || false;
-
-    // On initial load
-    if (isInitialLoadRef.current) {
-      isInitialLoadRef.current = false;
-
-      // If official mode is active at start, store filters without the highlightFilter
-      if (isHighlighted && filters) {
-        const initialNormalFilters = filters
-          .split(",")
-          .filter((f) => f !== "is_official_provider" && f !== "")
-          .filter(Boolean);
-        if (initialNormalFilters.length > 0) {
-          normalFiltersRef.current = initialNormalFilters.join(",");
-        }
-      }
-
-      // Update state without triggering URL change
-      setIsOfficialProviderActive(isHighlighted);
-      return;
-    }
-
-    // For subsequent changes
-    if (!isHighlighted && filters) {
-      normalFiltersRef.current = filters;
-    }
-
-    setIsOfficialProviderActive(isHighlighted);
-  }, [searchParams]);
-
-  const toggleOfficialProviderMode = useCallback(
-    (source = null) => {
-      if (!searchParams || !setSearchParams) return;
-
-      // If source is the same as last time and last change was less than 100ms ago, ignore
-      const now = Date.now();
-      if (
-        source &&
-        source === lastToggleSourceRef.current?.source &&
-        now - (lastToggleSourceRef.current?.timestamp || 0) < 100
-      ) {
-        return;
-      }
-
-      const currentFiltersStr = searchParams.get("filters");
-      const currentFilters =
-        currentFiltersStr?.split(",").filter(Boolean) || [];
-      const highlightFilter = "is_official_provider";
-      const newSearchParams = new URLSearchParams(searchParams);
-
-      if (currentFilters.includes(highlightFilter)) {
-        // Deactivating official provider mode
-        if (normalFiltersRef.current) {
-          const normalFilters = normalFiltersRef.current
-            .split(",")
-            .filter((f) => f !== highlightFilter && f !== "")
-            .filter(Boolean);
-
-          if (normalFilters.length > 0) {
-            newSearchParams.set("filters", normalFilters.join(","));
-          } else {
-            newSearchParams.delete("filters");
-          }
-        } else {
-          const newFilters = currentFilters.filter(
-            (f) => f !== highlightFilter && f !== ""
-          );
-          if (newFilters.length === 0) {
-            newSearchParams.delete("filters");
-          } else {
-            newSearchParams.set("filters", newFilters.join(","));
-          }
-        }
-      } else {
-        // Activating official provider mode
-        if (currentFiltersStr) {
-          normalFiltersRef.current = currentFiltersStr;
-        }
-
-        const filtersToSet = [
-          ...new Set([...currentFilters, highlightFilter]),
-        ].filter(Boolean);
-        newSearchParams.set("filters", filtersToSet.join(","));
-      }
-
-      // Update state immediately
-      setIsOfficialProviderActive(!currentFilters.includes(highlightFilter));
-
-      // Save source and timestamp of last change
-      lastToggleSourceRef.current = {
-        source,
-        timestamp: now,
-      };
-
-      // Update search params and let HashRouter handle the URL
-      setSearchParams(newSearchParams);
-    },
-    [searchParams, setSearchParams]
-  );
-
   return {
-    isOfficialProviderActive,
-    toggleOfficialProviderMode,
+    isOfficialProviderActive: false,
+    toggleOfficialProviderMode: () => {},
   };
 };

frontend/src/pages/LeaderboardPage/components/Leaderboard/components/Table/hooks/useDataProcessing.js

@@ -28,8 +28,7 @@ export const useDataProcessing = (
   scoreDisplay,
   pinnedModels,
   onTogglePin,
-  setSorting,
-  isOfficialProviderActive
+  setSorting
 ) => {
   // Call hooks directly at root level
   const { minAverage, maxAverage } = useAverageRange(data);
@@ -47,7 +46,6 @@ export const useDataProcessing = (
       selectedBooleanFilters,
       rankingMode,
       pinnedModels,
-      isOfficialProviderActive,
     }),
     [
       selectedPrecisions,
@@ -57,7 +55,6 @@ export const useDataProcessing = (
       selectedBooleanFilters,
       rankingMode,
       pinnedModels,
-      isOfficialProviderActive,
     ]
   );
 
@@ -70,8 +67,7 @@ export const useDataProcessing = (
     filterConfig.searchValue,
     filterConfig.selectedBooleanFilters,
     filterConfig.rankingMode,
-    filterConfig.pinnedModels,
-    filterConfig.isOfficialProviderActive
+    filterConfig.pinnedModels
   );
 
   // Memoize columns creation

frontend/src/pages/LeaderboardPage/components/Leaderboard/constants/defaults.js

@@ -60,12 +60,14 @@ const COLUMN_SIZES = {
   LICENSE: 160,
   UPLOAD_DATE: 160,
   SUBMISSION_DATE: 200,
-  GENERATION: 160,
   BASE_MODEL: 390,
   HUB_AVAILABILITY: 180,
+  ADAPTER_METHOD: 160,
+  EMBED_DIM: 140,
 };
 
-// Column definitions with organized structure
+// Column definitions — evaluation columns map to the 8 EEG downstream
+// datasets from the PEFT benchmark for EEG Foundation Models.
 const COLUMNS = {
   FIXED: {
     rank: {
@@ -78,7 +80,7 @@ const COLUMNS = {
       group: "fixed",
       size: COLUMN_SIZES.TYPE_ICON,
       defaultVisible: true,
-      label: "Type",
+      label: "Adapter",
     },
     id: {
       group: "fixed",
@@ -94,17 +96,58 @@ const COLUMNS = {
     },
   },
   EVALUATION: {
-    "evaluations.anli.normalized_score": {
+    // Motor Imagery
+    "evaluations.bcic2a.normalized_score": {
       group: "evaluation",
       size: COLUMN_SIZES.BENCHMARK,
       defaultVisible: true,
-      label: "ANLI",
+      label: "BCIC-2a",
     },
-    "evaluations.logiqa.normalized_score": {
+    "evaluations.physionet.normalized_score": {
       group: "evaluation",
       size: COLUMN_SIZES.BENCHMARK,
       defaultVisible: true,
-      label: "LogiQA",
+      label: "PhysioNet",
+    },
+    // Sleep Staging
+    "evaluations.isruc_sleep.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: true,
+      label: "ISRUC",
+    },
+    // Pathology Detection
+    "evaluations.tuab.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: true,
+      label: "TUAB",
+    },
+    "evaluations.tuev.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: true,
+      label: "TUEV",
+    },
+    // Seizure Detection
+    "evaluations.chbmit.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: true,
+      label: "CHB-MIT",
+    },
+    // Emotion Recognition
+    "evaluations.faced.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: false,
+      label: "FACED",
+    },
+    "evaluations.seedv.normalized_score": {
+      group: "evaluation",
+      size: COLUMN_SIZES.BENCHMARK,
+      defaultVisible: false,
+      label: "SEED-V",
     },
   },
   MODEL_INFO: {
@@ -117,8 +160,14 @@ const COLUMNS = {
     "model.architecture": {
       group: "model_info",
       size: COLUMN_SIZES.ARCHITECTURE,
+      defaultVisible: true,
+      label: "Foundation Model",
+    },
+    "metadata.adapter_method": {
+      group: "model_info",
+      size: COLUMN_SIZES.ADAPTER_METHOD,
       defaultVisible: false,
-      label: "Architecture",
+      label: "Adapter Method",
     },
     "model.precision": {
       group: "model_info",
@@ -132,6 +181,12 @@ const COLUMNS = {
       defaultVisible: false,
       label: "Parameters (M)",
     },
+    "metadata.embed_dim": {
+      group: "model_info",
+      size: COLUMN_SIZES.EMBED_DIM,
+      defaultVisible: false,
+      label: "Embed Dim",
+    },
     "metadata.hub_license": {
       group: "model_info",
       size: COLUMN_SIZES.LICENSE,
@@ -152,12 +207,6 @@ const COLUMNS = {
       defaultVisible: false,
       label: "Submission Date",
     },
-    "metadata.generation": {
-      group: "additional_info",
-      size: COLUMN_SIZES.GENERATION,
-      defaultVisible: false,
-      label: "Generation",
-    },
     "metadata.base_model": {
       group: "additional_info",
       size: COLUMN_SIZES.BASE_MODEL,
@@ -281,9 +330,13 @@ export const HIGHLIGHT_COLORS = [
 export const SKELETON_COLUMNS = [
   40, // Checkbox
   COLUMN_SIZES.RANK, // Rank
-  COLUMN_SIZES.TYPE_ICON, // Type icon
+  COLUMN_SIZES.TYPE_ICON, // Adapter icon
   COLUMN_SIZES.MODEL, // Model name
   COLUMN_SIZES.AVERAGE_SCORE, // Average score
-  COLUMN_SIZES.BENCHMARK, // Benchmark 1 (ANLI)
-  COLUMN_SIZES.BENCHMARK, // Benchmark 2 (LogiQA)
+  COLUMN_SIZES.BENCHMARK, // BCIC-2a
+  COLUMN_SIZES.BENCHMARK, // PhysioNet
+  COLUMN_SIZES.BENCHMARK, // ISRUC
+  COLUMN_SIZES.BENCHMARK, // TUAB
+  COLUMN_SIZES.BENCHMARK, // TUEV
+  COLUMN_SIZES.BENCHMARK, // CHB-MIT
 ];

frontend/src/pages/LeaderboardPage/components/Leaderboard/constants/modelTypes.js

@@ -1,34 +1,59 @@
+// Model "types" in the EEG Finetune Arena represent adapter / fine-tuning
+// methods used with EEG foundation models (LaBraM, EEGPT, BIOT, BENDR,
+// SignalJEPA, CBraMod, REVE).
+
 export const MODEL_TYPE_ORDER = [
-  'pretrained',
-  'fine-tuned',
-  'task-specific',
-  'foundation'
+  'lora',
+  'ia3',
+  'adalora',
+  'dora',
+  'oft',
+  'probe',
+  'full_finetune'
 ];
 
 export const MODEL_TYPES = {
-  'pretrained': {
-    icon: '\u{1F7E2}',
-    label: 'Pretrained',
-    description: 'Base EEG models trained with self-supervised learning',
+  'lora': {
+    icon: '\u{1F9E9}',
+    label: 'LoRA',
+    description: 'Low-Rank Adaptation: injects trainable low-rank matrices into transformer layers (~98% parameter reduction)',
     order: 0
   },
-  'fine-tuned': {
-    icon: '\u{1F536}',
-    label: 'Fine-tuned',
-    description: 'Models fine-tuned on specific EEG datasets',
+  'ia3': {
+    icon: '\u{1F4A1}',
+    label: 'IA3',
+    description: 'Infused Adapter by Inhibiting and Amplifying Inner Activations (~99.5% parameter reduction)',
     order: 1
   },
-  'task-specific': {
-    icon: '\u{1F9EA}',
-    label: 'Task-specific',
-    description: 'Models designed for specific EEG tasks (e.g., sleep staging, motor imagery)',
+  'adalora': {
+    icon: '\u{1F3AF}',
+    label: 'AdaLoRA',
+    description: 'Adaptive Low-Rank Adaptation with dynamic rank allocation across layers',
     order: 2
   },
-  'foundation': {
-    icon: '\u{1F3D7}\u{FE0F}',
-    label: 'Foundation',
-    description: 'Large-scale EEG foundation models',
+  'dora': {
+    icon: '\u{1F52C}',
+    label: 'DoRA',
+    description: 'Weight-Decomposed Low-Rank Adaptation: decomposes weights into magnitude and direction',
     order: 3
+  },
+  'oft': {
+    icon: '\u{1F504}',
+    label: 'OFT',
+    description: 'Orthogonal Fine-Tuning: applies orthogonal transformations to preserve pre-trained features',
+    order: 4
+  },
+  'probe': {
+    icon: '\u{1F50D}',
+    label: 'Probe',
+    description: 'Linear probing: freezes the encoder and trains only the classification head',
+    order: 5
+  },
+  'full_finetune': {
+    icon: '\u{1F527}',
+    label: 'Full Fine-tune',
+    description: 'Full fine-tuning baseline: all model parameters are updated during training',
+    order: 6
   }
 };
 
@@ -53,7 +78,7 @@ export const getModelTypeDescription = (type) => {
   const matchedType = Object.entries(MODEL_TYPES).find(([key]) =>
     cleanType.includes(key)
   );
-  return matchedType ? matchedType[1].description : 'Unknown model type';
+  return matchedType ? matchedType[1].description : 'Unknown adapter method';
 };
 
 export const getModelTypeOrder = (type) => {

frontend/src/pages/LeaderboardPage/components/Leaderboard/constants/tooltips.js

@@ -29,72 +29,157 @@ const createTooltipContent = (title, items) => (
 );
 
 export const COLUMN_TOOLTIPS = {
-  AVERAGE: createTooltipContent("Average score across all benchmarks:", [
+  AVERAGE: createTooltipContent("Average score across all EEG benchmarks:", [
     {
       label: "Calculation",
-      description: "Weighted average of normalized scores from all benchmarks",
+      description: "Mean accuracy across all downstream EEG datasets",
       subItems: [
-        "Each benchmark is normalized to a 0-100 scale",
-        "All normalised benchmarks are then averaged together",
+        "Each benchmark score is accuracy normalized to 0-100",
+        "All benchmark scores are averaged together",
       ],
     },
   ]),
 
-  ANLI: createTooltipContent("Adversarial Natural Language Inference (ANLI):", [
+  BCIC2A: createTooltipContent("BCI Competition IV Dataset 2a (BCIC-2a):", [
     {
-      label: "Purpose",
-      description:
-        "Tests the model's ability to perform natural language inference on adversarially constructed examples",
+      label: "Task",
+      description: "4-class motor imagery classification",
       subItems: [
-        "Entailment, contradiction, and neutral classification",
-        "Adversarially-mined examples for robustness",
+        "Classes: left hand, right hand, feet, tongue",
+        "9 subjects, 22 EEG channels, 200 Hz",
+        "4-second trials with event-related windowing",
       ],
     },
     {
       label: "Scoring: Accuracy",
-      description: "Was the correct label predicted for each example.",
+      description: "Proportion of correctly classified motor imagery trials.",
     },
   ]),
 
-  LOGIQA: createTooltipContent("Logical Reasoning QA (LogiQA):", [
+  PHYSIONET: createTooltipContent("PhysioNet Motor Imagery (PhysioNet MI):", [
     {
-      label: "Purpose",
-      description:
-        "Evaluates logical reasoning abilities through multiple-choice questions",
+      label: "Task",
+      description: "4-class motor imagery classification",
       subItems: [
-        "Categorical reasoning",
-        "Sufficient conditional reasoning",
-        "Necessary conditional reasoning",
-        "Disjunctive reasoning",
-        "Conjunctive reasoning",
+        "Classes: left hand, right hand, feet, both hands",
+        "109 subjects, 64 EEG channels, 200 Hz",
+        "3-second trials with event-related windowing",
       ],
     },
     {
       label: "Scoring: Accuracy",
-      description:
-        "Was the correct choice selected among the options.",
+      description: "Proportion of correctly classified imagery trials.",
     },
   ]),
 
-  ARCHITECTURE: createTooltipContent("Model Architecture Information:", [
+  ISRUC_SLEEP: createTooltipContent("ISRUC-SLEEP Group I:", [
     {
-      label: "Definition",
-      description: "The fundamental structure and design of the EEG model",
+      label: "Task",
+      description: "5-class sleep staging",
+      subItems: [
+        "Classes: Wake, N1, N2, N3, REM",
+        "~100 subjects, 6 EEG channels, 200 Hz",
+        "30-second epoch windows",
+      ],
+    },
+    {
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly classified 30s sleep epochs.",
+    },
+  ]),
+
+  TUAB: createTooltipContent("TUH EEG Abnormal Corpus (TUAB v3.0.1):", [
+    {
+      label: "Task",
+      description: "Binary pathology detection (normal vs abnormal)",
+      subItems: [
+        "Classes: normal, abnormal",
+        "290+ subjects, 16 EEG channels, 200 Hz",
+        "10-second fixed-length windows",
+      ],
+    },
+    {
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly classified recordings.",
+    },
+  ]),
+
+  TUEV: createTooltipContent("TUH EEG Events Corpus (TUEV v2.0.1):", [
+    {
+      label: "Task",
+      description: "6-class EEG event classification",
       subItems: [
-        "Pretrained: Base EEG models trained with self-supervised learning on raw EEG data.",
-        "Fine-tuned: Models fine-tuned on specific EEG datasets for particular downstream tasks.",
-        "Task-specific: Models designed for specific EEG tasks such as sleep staging, motor imagery, or seizure detection.",
-        "Foundation: Large-scale EEG foundation models trained on diverse EEG datasets.",
+        "Classes: SPSW, GPED, PLED, EYEM, ARTF, BCKG",
+        "200+ subjects, 21 EEG channels, 200 Hz",
+        "5-second event windows",
       ],
     },
     {
-      label: "Impact",
-      description: "How architecture affects model capabilities",
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly classified EEG events.",
+    },
+  ]),
+
+  CHBMIT: createTooltipContent("CHB-MIT Scalp EEG Database:", [
+    {
+      label: "Task",
+      description: "Binary seizure detection",
       subItems: [
-        "Pretrained models provide general EEG representations but may need fine-tuning for specific tasks.",
-        "Fine-tuned models are optimized for particular datasets or paradigms.",
-        "Task-specific models achieve strong performance on their target task but may not generalize.",
-        "Foundation models aim for broad generalization across EEG tasks.",
+        "Classes: seizure, background (non-seizure)",
+        "23 pediatric subjects, 17 EEG channels, 200 Hz",
+        "10-second windows from bipolar montage",
+      ],
+    },
+    {
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly detected seizure and background windows.",
+    },
+  ]),
+
+  FACED: createTooltipContent("FACED Emotion Recognition Dataset:", [
+    {
+      label: "Task",
+      description: "9-class discrete emotion recognition from EEG",
+      subItems: [
+        "Classes: Anger, Disgust, Fear, Sadness, Neutral, Amusement, Inspiration, Joy, Tenderness",
+        "123 subjects, 26 EEG channels, 200 Hz",
+        "10-second event-related windows",
+      ],
+    },
+    {
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly classified emotion labels.",
+    },
+  ]),
+
+  SEEDV: createTooltipContent("SEED-V Emotion Recognition Dataset:", [
+    {
+      label: "Task",
+      description: "5-class emotion recognition from EEG",
+      subItems: [
+        "Classes: Happy, Sad, Neutral, Disgust, Fear",
+        "62 EEG channels, 200 Hz",
+        "1-second windows, session-based splits",
+      ],
+    },
+    {
+      label: "Scoring: Accuracy",
+      description: "Proportion of correctly classified emotion labels.",
+    },
+  ]),
+
+  ARCHITECTURE: createTooltipContent("EEG Foundation Model:", [
+    {
+      label: "Definition",
+      description: "The pre-trained foundation model that is being adapted",
+      subItems: [
+        "LaBraM: Vision Transformer for EEG (12 layers, 200D embedding)",
+        "EEGPT: Transformer with patch-based tokenization (8 layers, 512D)",
+        "BIOT: Linear Attention Transformer (4 layers, 256D)",
+        "BENDR: CNN + BERT-inspired Transformer (8 layers, 512D encoder)",
+        "SignalJEPA: CNN + Transformer with JEPA-style predictive learning",
+        "CBraMod: Criss-Cross Transformer with separate spatial/temporal attention (12 layers, 200D)",
+        "REVE: Vision Transformer with GEGLU (22 layers, 512D)",
       ],
     },
   ]),
@@ -108,16 +193,6 @@ export const COLUMN_TOOLTIPS = {
         "bfloat16: Half precision (Brain Float format), good for stability",
         "float16: Half precision",
         "8bit/4bit: Quantized formats, for efficiency",
-        "GPTQ/AWQ: Quantized methods",
-      ],
-    },
-    {
-      label: "Impact",
-      description: "How precision affects model deployment",
-      subItems: [
-        "Higher precision = better accuracy but more memory usage",
-        "Lower precision = faster inference and smaller size",
-        "Trade-off between model quality and resource usage",
       ],
     },
   ]),
@@ -136,9 +211,10 @@ export const COLUMN_TOOLTIPS = {
       label: "Measurement",
       description: "Total number of trainable parameters in millions",
       subItems: [
-        "Indicates model capacity and complexity",
-        "Correlates with computational requirements",
-        "Influences memory usage and inference speed",
+        "Foundation model total parameters (before adapter)",
+        "Adapter methods reduce trainable params by 90-99.5%",
+        "LoRA r=16: ~98% reduction",
+        "IA3: ~99.5% reduction (only scaling vectors)",
       ],
     },
   ]),
@@ -151,7 +227,6 @@ export const COLUMN_TOOLTIPS = {
         "Commercial vs non-commercial use",
         "Attribution requirements",
         "Modification and redistribution rights",
-        "Liability and warranty terms",
       ],
     },
   ]),
@@ -176,7 +251,7 @@ export const UI_TOOLTIPS = {
       label: "Name Search",
       description: "Search directly by model name",
       subItems: [
-        "Supports regular expressions (e.g., ^eegnet.*v4)",
+        "Supports regular expressions (e.g., ^labram.*lora)",
         "Case sensitive",
       ],
     },
@@ -184,23 +259,23 @@ export const UI_TOOLTIPS = {
       label: "Field Search",
       description: "Use @field:value syntax for precise filtering",
       subItems: [
-        "@architecture:eegnet - Filter by architecture",
+        "@architecture:labram - Filter by foundation model",
         "@license:mit - Filter by license",
         "@precision:float16 - Filter by precision",
-        "@type:pretrained - Filter by model type",
+        "@type:lora - Filter by adapter method",
       ],
     },
     {
       label: "Multiple Searches",
       description: "Combine multiple criteria using semicolons",
       subItems: [
-        "braindecode @license:mit; @architecture:eegnet",
-        "^shallow.*net; @precision:float16",
+        "labram @type:lora; @architecture:eegpt",
+        "^biot.*ia3; @precision:float16",
       ],
     },
   ]),
   QUICK_FILTERS: createTooltipContent(
-    "Filter models based on their size:",
+    "Filter models based on their total parameter count:",
     [
       {
         label: "Tiny (Up to 10M)",
@@ -210,17 +285,17 @@ export const UI_TOOLTIPS = {
       {
         label: "Small (10M-50M)",
         description:
-          "Compact models balancing performance and efficiency.",
+          "Compact models balancing performance and efficiency (e.g., CBraMod ~4M, EEGPT ~10M).",
       },
       {
         label: "Medium (50M-200M)",
         description:
-          "Mid-range models with good capacity for complex EEG tasks.",
+          "Mid-range foundation models with good capacity.",
       },
       {
         label: "Large (200M+)",
         description:
-          "Large-scale models offering the best performance but requiring more resources.",
+          "Large foundation models offering broad generalization (e.g., REVE, LaBraM).",
       },
     ]
   ),
@@ -232,7 +307,7 @@ export const UI_TOOLTIPS = {
   SCORE_DISPLAY: {
     title: "Score Display",
     description:
-      "Choose between normalized scores (0-100% scale for easy comparison) or raw scores (actual benchmark results). Normalized scores help compare performance across different benchmarks, while raw scores show actual benchmark outputs.",
+      "Choose between normalized scores (0-100% scale for easy comparison) or raw scores (actual accuracy values 0-1). Normalized scores help compare performance across different benchmarks.",
   },
   RANKING_MODE: {
     title: "Ranking Mode",

frontend/src/pages/LeaderboardPage/components/Leaderboard/hooks/useDataUtils.js

@@ -57,10 +57,6 @@ export const useProcessedData = (data, averageMode, visibleColumns) => {
       // Boolean standardization
       const standardizedFeatures = {
         ...item.features,
-        is_moe: Boolean(item.features.is_moe),
-        is_flagged: Boolean(item.features.is_flagged),
-        is_official_provider: Boolean(item.features.is_official_provider),
-        is_merged: Boolean(item.features.is_merged),
         is_not_available_on_hub: Boolean(item.features.is_not_available_on_hub),
       };
 
@@ -69,7 +65,6 @@ export const useProcessedData = (data, averageMode, visibleColumns) => {
         features: standardizedFeatures,
         model: {
           ...item.model,
-          has_chat_template: Boolean(item.model.has_chat_template),
           average_score: average,
         },
       };
@@ -101,8 +96,7 @@ export const useFilteredData = (
   searchValue,
   selectedBooleanFilters,
   rankingMode,
-  pinnedModels = [],
-  isOfficialProviderActive = false
+  pinnedModels = []
 ) => {
   return useMemo(() => {
     const pinnedData = processedData.filter((row) => {
@@ -115,15 +109,6 @@ export const useFilteredData = (
 
     let filteredUnpinned = unpinnedData;
 
-    // Filter by official providers
-    if (isOfficialProviderActive) {
-      filteredUnpinned = filteredUnpinned.filter(
-        (row) =>
-          row.features?.is_official_provider ||
-          row.metadata?.is_official_provider
-      );
-    }
-
     // Filter by precision
     if (selectedPrecisions.length > 0) {
       filteredUnpinned = filteredUnpinned.filter((row) =>
@@ -131,7 +116,7 @@ export const useFilteredData = (
       );
     }
 
-    // Filter by type
+    // Filter by type (adapter method)
     if (
       selectedTypes.length > 0 &&
       selectedTypes.length < MODEL_TYPE_ORDER.length
@@ -142,11 +127,11 @@ export const useFilteredData = (
       });
     }
 
-    // Filter by parameters
-    if (!(paramsRange[0] === -1 && paramsRange[1] === 140)) {
+    // Filter by parameters (in millions for EEG)
+    if (!(paramsRange[0] === -1 && paramsRange[1] === 500)) {
       filteredUnpinned = filteredUnpinned.filter((row) => {
         const params =
-          row.metadata?.params_billions || row.features?.params_billions;
+          row.metadata?.params_millions || row.features?.params_millions;
         if (params === undefined || params === null) return false;
         return params >= paramsRange[0] && params < paramsRange[1];
       });
@@ -200,12 +185,6 @@ export const useFilteredData = (
           const filterValue =
             typeof filter === "object" ? filter.value : filter;
 
-          // Maintainer's Highlight keeps positive logic
-          if (filterValue === "is_official_provider") {
-            return row.features[filterValue];
-          }
-
-          // For all other filters, invert the logic
           if (filterValue === "is_not_available_on_hub") {
             return row.features[filterValue];
           }
@@ -280,7 +259,6 @@ export const useFilteredData = (
     selectedBooleanFilters,
     rankingMode,
     pinnedModels,
-    isOfficialProviderActive,
   ]);
 };
 

frontend/src/pages/LeaderboardPage/components/Leaderboard/hooks/useLeaderboardData.js

@@ -73,7 +73,6 @@ export const useLeaderboardProcessing = () => {
       types: state.filters.types,
       paramsRange: state.filters.paramsRange,
       booleanFilters: state.filters.booleanFilters,
-      isOfficialProviderActive: state.filters.isOfficialProviderActive,
     }),
     [
       state.filters.search,
@@ -81,7 +80,6 @@ export const useLeaderboardProcessing = () => {
       state.filters.types,
       state.filters.paramsRange,
       state.filters.booleanFilters,
-      state.filters.isOfficialProviderActive,
     ]
   );
 
@@ -108,8 +106,7 @@ export const useLeaderboardProcessing = () => {
     state.display.scoreDisplay,
     state.pinnedModels,
     actions.togglePinnedModel,
-    setSorting,
-    memoizedFilters.isOfficialProviderActive
+    setSorting
   );
 
   return {

frontend/src/pages/LeaderboardPage/components/Leaderboard/utils/columnUtils.js

@@ -276,7 +276,7 @@ const RankIndicator = ({ rank, previousRank, mode }) => {
 
 const getDetailsUrl = (modelName) => {
   const formattedName = modelName.replace("/", "__");
-  return `https://huggingface.co/datasets/open-llm-leaderboard/${formattedName}-details`;
+  return `https://huggingface.co/datasets/braindecode/${formattedName}-details`;
 };
 
 const HeaderLabel = ({ label, tooltip, className, isSorted }) => (
@@ -366,7 +366,7 @@ const createHeaderCell = (label, tooltip) => (header) =>
   );
 
 const createModelHeader =
-  (totalModels, officialProvidersCount = 0, isOfficialProviderActive = false) =>
+  (totalModels) =>
   ({ table }) => {
     return (
       <Box
@@ -751,57 +751,75 @@ export const createColumns = (
 
   const evaluationColumns = [
     {
-      accessorKey: "evaluations.ifeval.normalized_score",
-      header: createHeaderCell("IFEval", COLUMN_TOOLTIPS.IFEVAL),
+      accessorKey: "evaluations.bcic2a.normalized_score",
+      header: createHeaderCell("BCIC-2a", COLUMN_TOOLTIPS.BCIC2A),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.ifeval.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.bcic2a.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.ifeval.normalized_score"
+        "evaluations.bcic2a.normalized_score"
       ],
     },
     {
-      accessorKey: "evaluations.bbh.normalized_score",
-      header: createHeaderCell("BBH", COLUMN_TOOLTIPS.BBH),
+      accessorKey: "evaluations.physionet.normalized_score",
+      header: createHeaderCell("PhysioNet", COLUMN_TOOLTIPS.PHYSIONET),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.bbh.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.physionet.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.bbh.normalized_score"
+        "evaluations.physionet.normalized_score"
       ],
     },
     {
-      accessorKey: "evaluations.math.normalized_score",
-      header: createHeaderCell("MATH", COLUMN_TOOLTIPS.MATH),
+      accessorKey: "evaluations.isruc_sleep.normalized_score",
+      header: createHeaderCell("ISRUC", COLUMN_TOOLTIPS.ISRUC_SLEEP),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.math.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.isruc_sleep.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.math.normalized_score"
+        "evaluations.isruc_sleep.normalized_score"
       ],
     },
     {
-      accessorKey: "evaluations.gpqa.normalized_score",
-      header: createHeaderCell("GPQA", COLUMN_TOOLTIPS.GPQA),
+      accessorKey: "evaluations.tuab.normalized_score",
+      header: createHeaderCell("TUAB", COLUMN_TOOLTIPS.TUAB),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.gpqa.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.tuab.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.gpqa.normalized_score"
+        "evaluations.tuab.normalized_score"
       ],
     },
     {
-      accessorKey: "evaluations.musr.normalized_score",
-      header: createHeaderCell("MUSR", COLUMN_TOOLTIPS.MUSR),
+      accessorKey: "evaluations.tuev.normalized_score",
+      header: createHeaderCell("TUEV", COLUMN_TOOLTIPS.TUEV),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.musr.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.tuev.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.musr.normalized_score"
+        "evaluations.tuev.normalized_score"
       ],
     },
     {
-      accessorKey: "evaluations.mmlu_pro.normalized_score",
-      header: createHeaderCell("MMLU-PRO", COLUMN_TOOLTIPS.MMLU_PRO),
+      accessorKey: "evaluations.chbmit.normalized_score",
+      header: createHeaderCell("CHB-MIT", COLUMN_TOOLTIPS.CHBMIT),
       cell: ({ row, getValue }) =>
-        createScoreCell(getValue, row, "evaluations.mmlu_pro.normalized_score"),
+        createScoreCell(getValue, row, "evaluations.chbmit.normalized_score"),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "evaluations.mmlu_pro.normalized_score"
+        "evaluations.chbmit.normalized_score"
+      ],
+    },
+    {
+      accessorKey: "evaluations.faced.normalized_score",
+      header: createHeaderCell("FACED", COLUMN_TOOLTIPS.FACED),
+      cell: ({ row, getValue }) =>
+        createScoreCell(getValue, row, "evaluations.faced.normalized_score"),
+      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
+        "evaluations.faced.normalized_score"
+      ],
+    },
+    {
+      accessorKey: "evaluations.seedv.normalized_score",
+      header: createHeaderCell("SEED-V", COLUMN_TOOLTIPS.SEEDV),
+      cell: ({ row, getValue }) =>
+        createScoreCell(getValue, row, "evaluations.seedv.normalized_score"),
+      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
+        "evaluations.seedv.normalized_score"
       ],
     },
   ];
@@ -830,8 +848,8 @@ export const createColumns = (
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["model.precision"],
     },
     {
-      accessorKey: "metadata.params_billions",
-      header: createHeaderCell("Parameters", COLUMN_TOOLTIPS.PARAMETERS),
+      accessorKey: "metadata.params_millions",
+      header: createHeaderCell("Parameters (M)", COLUMN_TOOLTIPS.PARAMETERS),
       cell: ({ row }) => (
         <Box
           sx={{
@@ -841,12 +859,12 @@ export const createColumns = (
           }}
         >
           <Typography variant="body2">
-            {row.original.metadata.params_billions}
-            <span style={{ opacity: 0.6 }}>B</span>
+            {row.original.metadata.params_millions}
+            <span style={{ opacity: 0.6 }}>M</span>
           </Typography>
         </Box>
       ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.params_billions"],
+      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.params_millions"],
     },
     {
       accessorKey: "metadata.hub_license",
@@ -924,19 +942,6 @@ export const createColumns = (
       ),
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.submission_date"],
     },
-    {
-      accessorKey: "metadata.generation",
-      header: createHeaderCell(
-        "Generation",
-        "The generation or version number of the model"
-      ),
-      cell: ({ row }) => (
-        <Typography variant="body2">
-          {row.original.metadata.generation}
-        </Typography>
-      ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.generation"],
-    },
     {
       accessorKey: "metadata.base_model",
       header: createHeaderCell(
@@ -960,34 +965,39 @@ export const createColumns = (
       size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.base_model"],
     },
     {
-      accessorKey: "metadata.co2_cost",
-      header: createHeaderCell("CO₂ Cost", COLUMN_TOOLTIPS.CO2_COST),
+      accessorKey: "metadata.adapter_method",
+      header: createHeaderCell(
+        "Adapter Method",
+        "The PEFT adapter method used for fine-tuning"
+      ),
       cell: ({ row }) => (
-        <Box
-          sx={{
-            display: "flex",
-            alignItems: "center",
-            justifyContent: "flex-start",
-          }}
-        >
-          <Typography variant="body2">
-            {row.original.metadata.co2_cost?.toFixed(2) || "0"}
-            <span style={{ opacity: 0.6 }}> kg</span>
+        <Tooltip title={row.original.metadata.adapter_method || "-"}>
+          <Typography
+            variant="body2"
+            sx={{
+              overflow: "hidden",
+              textOverflow: "ellipsis",
+              whiteSpace: "nowrap",
+            }}
+          >
+            {row.original.metadata.adapter_method || "-"}
           </Typography>
-        </Box>
+        </Tooltip>
       ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.co2_cost"],
+      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.adapter_method"],
     },
     {
-      accessorKey: "model.has_chat_template",
+      accessorKey: "metadata.embed_dim",
       header: createHeaderCell(
-        "Chat Template",
-        "Whether this model has a chat template defined"
+        "Embed Dim",
+        "Embedding dimension of the foundation model"
       ),
       cell: ({ row }) => (
-        <BooleanValue value={row.original.model.has_chat_template} />
+        <Typography variant="body2">
+          {row.original.metadata.embed_dim || "-"}
+        </Typography>
       ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["model.has_chat_template"],
+      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["metadata.embed_dim"],
     },
     {
       accessorKey: "features.is_not_available_on_hub",
@@ -1002,42 +1012,6 @@ export const createColumns = (
         "features.is_not_available_on_hub"
       ],
     },
-    {
-      accessorKey: "features.is_official_provider",
-      header: createHeaderCell(
-        "Official Providers",
-        "Models that are officially provided and maintained by their original creators or organizations"
-      ),
-      cell: ({ row }) => (
-        <BooleanValue
-          value={row.original.features.is_official_provider}
-        />
-      ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES[
-        "features.is_official_provider"
-      ],
-      enableSorting: true,
-    },
-    {
-      accessorKey: "features.is_moe",
-      header: createHeaderCell(
-        "Mixture of Experts",
-        "Whether this model uses a Mixture of Experts architecture"
-      ),
-      cell: ({ row }) => <BooleanValue value={row.original.features.is_moe} />,
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["features.is_moe"],
-    },
-    {
-      accessorKey: "features.is_flagged",
-      header: createHeaderCell(
-        "Flag Status",
-        "Whether this model has been flagged for any issues"
-      ),
-      cell: ({ row }) => (
-        <BooleanValue value={row.original.features.is_flagged} />
-      ),
-      size: TABLE_DEFAULTS.COLUMNS.COLUMN_SIZES["features.is_flagged"],
-    },
   ];
 
   // Utiliser directement columnVisibility
@@ -1051,19 +1025,15 @@ export const createColumns = (
         const order = {
           "model.architecture": 1,
           "model.precision": 2,
-          "metadata.params_billions": 3,
+          "metadata.params_millions": 3,
           "metadata.hub_license": 4,
-          "metadata.co2_cost": 5,
-          "metadata.hub_hearts": 6,
-          "metadata.upload_date": 7,
-          "metadata.submission_date": 8,
-          "metadata.generation": 9,
+          "metadata.hub_hearts": 5,
+          "metadata.adapter_method": 6,
+          "metadata.embed_dim": 7,
+          "metadata.upload_date": 8,
+          "metadata.submission_date": 9,
           "metadata.base_model": 10,
-          "model.has_chat_template": 11,
-          "features.is_not_available_on_hub": 12,
-          "features.is_official_provider": 13,
-          "features.is_moe": 14,
-          "features.is_flagged": 15,
+          "features.is_not_available_on_hub": 11,
         };
         return order[a.accessorKey] - order[b.accessorKey];
       }),

frontend/src/pages/QuotePage/QuotePage.js

@@ -33,28 +33,120 @@ const citations = [
 
 const benchmarks = [
   {
-    title: "ANLI: Adversarial Natural Language Inference",
-    authors: "Nie et al.",
-    citation: `@inproceedings{nie2020adversarial,
-  title={Adversarial NLI: A New Benchmark for Natural Language Understanding},
-  author={Nie, Yixin and Williams, Adina and Dinan, Emily and Bansal, Mohit and Weston, Jason and Kiela, Douwe},
-  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
-  pages={4885--4901},
-  year={2020}
+    title: "BCIC-2a: BCI Competition IV Dataset 2a",
+    authors: "Brunner et al.",
+    citation: `@article{brunner2008bci,
+  title={BCI Competition 2008--Graz data set A},
+  author={Brunner, Clemens and Leeb, Robert and M{\\\"u}ller-Putz, Gernot and Schl{\\\"o}gl, Alois and Pfurtscheller, Gert},
+  journal={Institute for Knowledge Discovery (Laboratory of Brain-Computer Interfaces), Graz University of Technology},
+  volume={16},
+  pages={1--6},
+  year={2008}
 }`,
-    url: "https://arxiv.org/abs/1910.14599",
   },
   {
-    title: "LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning",
+    title: "PhysioNet Motor Imagery",
+    authors: "Schalk et al.",
+    citation: `@article{schalk2004bci2000,
+  title={BCI2000: a general-purpose brain-computer interface (BCI) system},
+  author={Schalk, Gerwin and McFarland, Dennis J and Hinterberger, Thilo and Birbaumer, Niels and Wolpaw, Jonathan R},
+  journal={IEEE Transactions on biomedical engineering},
+  volume={51},
+  number={6},
+  pages={1034--1043},
+  year={2004},
+  publisher={IEEE}
+}`,
+  },
+  {
+    title: "ISRUC-SLEEP: Sleep Staging Dataset",
+    authors: "Khalighi et al.",
+    citation: `@article{khalighi2016isruc,
+  title={ISRUC-Sleep: A comprehensive public dataset for sleep researchers},
+  author={Khalighi, Sirvan and Sousa, Teresa and Santos, Jo{\\~a}o M and Nunes, Urbano},
+  journal={Computer methods and programs in biomedicine},
+  volume={124},
+  pages={180--192},
+  year={2016},
+  publisher={Elsevier}
+}`,
+  },
+  {
+    title: "TUAB: TUH EEG Abnormal Corpus",
+    authors: "Lopez de Diego et al.",
+    citation: `@inproceedings{lopez2017automated,
+  title={Automated interpretation of abnormal adult electroencephalograms},
+  author={Lopez de Diego, Silvia and Obeid, Iyad and Bhatt, Priyanka and Bhatt, Sagar and Jacobson, Douglas and Picone, Joseph},
+  year={2017}
+}`,
+  },
+  {
+    title: "CHB-MIT: Scalp EEG Database",
+    authors: "Shoeb",
+    citation: `@phdthesis{shoeb2009application,
+  title={Application of machine learning to epileptic seizure onset detection and treatment},
+  author={Shoeb, Ali Hossam},
+  year={2009},
+  school={Massachusetts Institute of Technology}
+}`,
+  },
+  {
+    title: "FACED: Emotion Recognition Dataset",
+    authors: "Chen et al.",
+    citation: `@article{chen2023large,
+  title={A large finer-grained affective computing EEG dataset},
+  author={Chen, Jingjing and Wang, Xiaobin and Huang, Chen and Hu, Xin and Shen, Xinke and Li, Dan},
+  journal={Scientific Data},
+  volume={10},
+  number={1},
+  pages={740},
+  year={2023},
+  publisher={Nature Publishing Group UK London}
+}`,
+    url: "https://www.nature.com/articles/s41597-023-02650-w",
+  },
+  {
+    title: "SEED-V: Emotion Recognition with Video Stimuli",
     authors: "Liu et al.",
-    citation: `@inproceedings{liu2020logiqa,
-  title={LogiQA: A Challenge Dataset for Machine Reading Comprehension with Logical Reasoning},
-  author={Liu, Jian and Cui, Leyang and Liu, Hanmeng and Huang, Dandan and Wang, Yile and Zhang, Yue},
-  booktitle={Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence},
-  pages={3622--3628},
-  year={2020}
+    citation: `@article{liu2021comparing,
+  title={Comparing recognition performance and robustness of multimodal deep learning models for multimodal emotion recognition},
+  author={Liu, Wei and Qiu, Jie-Lin and Zheng, Wei-Long and Lu, Bao-Liang},
+  journal={IEEE Transactions on Cognitive and Developmental Systems},
+  volume={14},
+  number={2},
+  pages={715--729},
+  year={2021},
+  publisher={IEEE}
 }`,
-    url: "https://arxiv.org/abs/2007.08124",
+  },
+];
+
+const frameworks = [
+  {
+    title: "MOABB: Mother of All BCI Benchmarks",
+    authors: "Jayaram, Barachant",
+    citation: `@article{jayaram2018moabb,
+  title={MOABB: trustworthy algorithm benchmarking for BCIs},
+  author={Jayaram, Vinay and Barachant, Alexandre},
+  journal={Journal of neural engineering},
+  volume={15},
+  number={6},
+  pages={066011},
+  year={2018},
+  publisher={IOP Publishing}
+}`,
+    url: "https://moabb.neurotechx.com",
+  },
+  {
+    title: "PEFT: Parameter-Efficient Fine-Tuning",
+    authors: "Mangrulkar et al.",
+    citation: `@misc{peft2024,
+  title={PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods},
+  author={Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan},
+  year={2024},
+  howpublished={\\url{https://github.com/huggingface/peft}}
+}`,
+    url: "https://huggingface.co/docs/peft",
   },
 ];
 
@@ -147,8 +239,9 @@ function QuotePage() {
 
       <Alert severity="info" sx={{ mb: 4 }}>
         <Typography variant="body2">
-          The citations below include both the EEG Finetune Arena and the
-          individual benchmarks used in our evaluation suite.
+          The citations below include the core library, the individual EEG
+          benchmark datasets, and the evaluation frameworks used in our
+          benchmark suite.
         </Typography>
       </Alert>
 
@@ -163,9 +256,9 @@ function QuotePage() {
         </Box>
       </Box>
 
-      <Box>
+      <Box sx={{ mb: 6 }}>
         <Typography variant="h5" sx={{ mb: 3 }}>
-          Benchmarks
+          EEG Benchmark Datasets
         </Typography>
         <Box sx={{ display: "flex", flexDirection: "column", gap: 3 }}>
           {benchmarks.map((benchmark, index) => (
@@ -173,6 +266,17 @@ function QuotePage() {
           ))}
         </Box>
       </Box>
+
+      <Box>
+        <Typography variant="h5" sx={{ mb: 3 }}>
+          Evaluation Frameworks
+        </Typography>
+        <Box sx={{ display: "flex", flexDirection: "column", gap: 3 }}>
+          {frameworks.map((framework, index) => (
+            <CitationBlock key={index} {...framework} />
+          ))}
+        </Box>
+      </Box>
     </Box>
   );
 }