Fix HuggingFace upload for N3 and deploy

- Fix generate_model_card: handle None quant_acc for N3 entries,
  fix dataset tag lookup for _n3 suffixed keys, add N3 paper DOI
- Update leaderboard README: replace "coming soon" with N3 results
- Deployed: leaderboard Space, SHD/SSC/NMNIST N3 model cards

Author: Henry Barnes

huggingface/leaderboard/README.md

@@ -22,7 +22,7 @@ Community benchmark comparison for spiking neural networks across neuromorphic h
 
 Covers SHD, SSC, N-MNIST, GSC KWS, and DVS128 Gesture benchmarks. Results from Catalyst N2/N3, Intel Loihi 1/2, BrainChip Akida 2, SpiNNaker 2, and software baselines.
 
-**Catalyst N3 benchmark results** (with precision sweeps, FACTOR compression, and N3 hardware feature demonstrations) coming soon.
+**Catalyst N3 results**: SHD 91.0% (#1 SOTA), SSC 76.4% (#1 SOTA), N-MNIST 99.2%.
 
 **Submit your results**: Open a discussion or PR on this Space to add your platform's numbers.
 

huggingface/upload.py

@@ -126,16 +126,56 @@ def generate_model_card(benchmark):
     float_acc = info['float_acc']
     quant_acc = info['quant_acc']
     params = info['params']
+    generation = info.get('generation', 'N2')
     neuron_model = info.get('neuron_model', 'Adaptive Leaky Integrate-and-Fire (adLIF)')
 
+    # Strip generation suffix for dataset/display names
+    base_benchmark = benchmark.replace('_n3', '').replace('_n2', '')
+
     # Dataset tag mapping
     dataset_tags = {
         'shd': 'shd',
         'ssc': 'ssc',
         'nmnist': 'n-mnist',
         'gsc': 'google-speech-commands',
     }
-    dataset_tag = dataset_tags.get(benchmark, benchmark.replace('_', '-'))
+    dataset_tag = dataset_tags.get(base_benchmark, base_benchmark.replace('_', '-'))
+
+    # Build metrics YAML (skip quant if None)
+    metrics_yaml = f"""          - name: Float Accuracy ({generation})
+            type: accuracy
+            value: {float_acc}"""
+    if quant_acc is not None:
+        metrics_yaml += f"""
+          - name: Quantized Accuracy (int16)
+            type: accuracy
+            value: {quant_acc}"""
+
+    # Build results table (skip quant row if None)
+    results_rows = f"| Float accuracy | {float_acc}% |\n| Parameters | {params} |"
+    if quant_acc is not None:
+        results_rows = (
+            f"| Float accuracy | {float_acc}% |\n"
+            f"| Quantized accuracy (int16) | {quant_acc}% |\n"
+            f"| Parameters | {params} |\n"
+            f"| Quantization loss | {float_acc - quant_acc:.1f}% |"
+        )
+
+    # Reproduce command
+    train_dir = base_benchmark
+    train_cmd = f"python {train_dir}/train.py --device cuda:0"
+    if generation == 'N3' and base_benchmark == 'shd':
+        train_cmd = f"python {train_dir}/train.py --neuron adlif --hidden 1536 --epochs 200 --device cuda:0 --amp"
+
+    # Paper links
+    paper_links = """- **Benchmark repo**: [catalyst-neuromorphic/catalyst-benchmarks](https://github.com/catalyst-neuromorphic/catalyst-benchmarks)
+- **Cloud API**: [catalyst-neuromorphic.com](https://catalyst-neuromorphic.com)"""
+    if generation == 'N3':
+        paper_links += """
+- **N3 paper**: [Zenodo DOI 10.5281/zenodo.18881283](https://zenodo.org/records/18881283)"""
+    paper_links += """
+- **N2 paper**: [Zenodo DOI 10.5281/zenodo.18728256](https://zenodo.org/records/18728256)
+- **N1 paper**: [Zenodo DOI 10.5281/zenodo.18727094](https://zenodo.org/records/18727094)"""
 
     return f"""---
 language: en
@@ -147,13 +187,13 @@ def generate_model_card(benchmark):
   - surrogate-gradient
   - benchmark
   - catalyst
-  - {benchmark.replace('_', '-')}
+  - {base_benchmark}
 datasets:
   - {dataset_tag}
 metrics:
   - accuracy
 model-index:
-  - name: Catalyst {benchmark.upper()} SNN Benchmark
+  - name: Catalyst {base_benchmark.upper()} SNN Benchmark ({generation})
     results:
       - task:
           type: {info['task']}
@@ -162,63 +202,38 @@ def generate_model_card(benchmark):
           name: {info['dataset']}
           type: {dataset_tag}
         metrics:
-          - name: Float Accuracy
-            type: accuracy
-            value: {float_acc}
-          - name: Quantized Accuracy (int16)
-            type: accuracy
-            value: {quant_acc}
+{metrics_yaml}
 ---
 
-# Catalyst {benchmark.upper()} SNN Benchmark
+# Catalyst {base_benchmark.upper()} SNN Benchmark ({generation})
 
 {info['description']}
 
 ## Model Description
 
-- **Architecture**: {info['architecture']}
+- **Architecture ({generation})**: {info['architecture']}
 - **Neuron model**: {neuron_model}
 - **Training**: Surrogate gradient BPTT, fast-sigmoid surrogate (scale=25)
-- **Hardware target**: Catalyst N1/N2/N3 neuromorphic processors
-- **Quantization**: Float32 weights -> int16, membrane decay -> 12-bit fixed-point
+- **Hardware target**: Catalyst {generation} neuromorphic processor
 
 ## Results
 
 | Metric | Value |
 |--------|-------|
-| Float accuracy | {float_acc}% |
-| Quantized accuracy (int16) | {quant_acc}% |
-| Parameters | {params} |
-| Quantization loss | {float_acc - quant_acc:.1f}% |
+{results_rows}
 
 ## Reproduce
 
 ```bash
 git clone https://github.com/catalyst-neuromorphic/catalyst-benchmarks.git
 cd catalyst-benchmarks
 pip install -e .
-python {benchmark}/train.py --device cuda:0
-```
-
-## Deploy to Catalyst Hardware
-
-```python
-import catalyst_cloud
-
-client = catalyst_cloud.Client()
-result = client.simulate(
-    model="catalyst-neuromorphic/{benchmark}-snn-benchmark",
-    input_data=your_spikes,
-    processor="n2"
-)
+{train_cmd}
 ```
 
 ## Links
 
-- **Benchmark repo**: [catalyst-neuromorphic/catalyst-benchmarks](https://github.com/catalyst-neuromorphic/catalyst-benchmarks)
-- **Cloud API**: [catalyst-neuromorphic.com](https://catalyst-neuromorphic.com)
-- **N2 paper**: [Zenodo DOI 10.5281/zenodo.18728256](https://zenodo.org/records/18728256)
-- **N1 paper**: [Zenodo DOI 10.5281/zenodo.18727094](https://zenodo.org/records/18727094)
+{paper_links}
 
 ## Citation