Kumo-Tabular-M/-S, one Encode column (#248, #249)

data/cost.csv

@@ -16,16 +16,16 @@ LightGBM (Frozen),LightGBM,frozen,0.2751,0.0197,0.0,0.7333,6.7195,,INTEL(R) XEON
 TabDPT-1.3 (Frozen),TabDPT-1.3,frozen,0.2847,0.2223,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 Causilo (Frozen),Causilo,frozen,0.3145,0.5456,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 Causilo (TAR),Causilo,tar,0.3228,0.5502,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
-Kumo-Tabular-Small (TAR),Kumo-Tabular-Small,tar,0.3672,0.1174,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
+Kumo-Tabular-S (TAR),Kumo-Tabular-S,tar,0.3672,0.1174,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 EXAONE-Tabular (Frozen),EXAONE-Tabular,frozen,0.4243,2.7887,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 EXAONE-Tabular (TAR),EXAONE-Tabular,tar,0.4305,2.7526,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 TabPFN-v2p5 (TAR),TabPFN-v2p5,tar,0.4337,1.3744,100.6949,0.7399,6.9181,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 Xiaomi-TabLDM (TAR),Xiaomi-TabLDM,tar,0.4742,0.5767,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 XGBoost (TAR),XGBoost,tar,0.4845,0.0214,101.5254,0.7333,6.7195,,INTEL(R) XEON(R) PLATINUM 8581C CPU @ 2.30GHz
-Kumo-Tabular-Small (Frozen),Kumo-Tabular-Small,frozen,0.4932,0.1156,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
+Kumo-Tabular-S (Frozen),Kumo-Tabular-S,frozen,0.4932,0.1156,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 TabPFN-v2p5 (Frozen),TabPFN-v2p5,frozen,0.501,1.3786,0.0,0.7399,6.9181,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 Xiaomi-TabLDM (Frozen),Xiaomi-TabLDM,frozen,0.521,0.5792,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
-Kumo-Tabular-Medium (TAR),Kumo-Tabular-Medium,tar,0.5263,0.2165,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
+Kumo-Tabular-M (TAR),Kumo-Tabular-M,tar,0.5263,0.2165,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 XGBoost (Frozen),XGBoost,frozen,0.5454,0.0254,0.0,0.7333,6.7195,,INTEL(R) XEON(R) PLATINUM 8581C CPU @ 2.30GHz
 LimiX-2 (TAR),LimiX-2,tar,0.5789,16.6645,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 RandomForest (TAR),RandomForest,tar,0.5832,0.038,101.5254,0.7333,6.7195,,INTEL(R) XEON(R) PLATINUM 8581C CPU @ 2.30GHz
@@ -33,7 +33,7 @@ LimiX-2 (Frozen),LimiX-2,frozen,0.5883,17.6665,0.0,0.7333,6.7195,NVIDIA H100 80G
 TabM (Frozen),TabM,frozen,0.6253,0.0519,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 RandomForest (Frozen),RandomForest,frozen,0.6489,0.0348,0.0,0.7333,6.7195,,INTEL(R) XEON(R) PLATINUM 8581C CPU @ 2.30GHz
 TabM (TAR),TabM,tar,0.6497,0.0574,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
-Kumo-Tabular-Medium (Frozen),Kumo-Tabular-Medium,frozen,0.6741,0.2237,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
+Kumo-Tabular-M (Frozen),Kumo-Tabular-M,frozen,0.6741,0.2237,0.0,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 TabPFN-v2 (TAR),TabPFN-v2,tar,0.738,1.7376,100.6949,0.7399,6.9181,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 Kumo-Tabular (TAR),Kumo-Tabular,tar,0.7969,0.4124,101.5254,0.7333,6.7195,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz
 TabPFN-v2 (Frozen),TabPFN-v2,frozen,0.8065,1.6817,0.0,0.7399,6.9181,NVIDIA H100 80GB HBM3,Intel(R) Xeon(R) Platinum 8481C CPU @ 2.70GHz

data/models.csv

@@ -18,8 +18,8 @@ TabSTAR-v1.1,Technion,technion.svg,https://github.com/alanarazi7/TabSTAR#tabstar
 Mitra-v2,Amazon,amazon.png,https://arxiv.org/abs/2609.04540
 LimiX-2,Stable AI & Tsinghua University,limix.png,https://arxiv.org/abs/2609.17488
 Kumo-Tabular,NVIDIA,nvidia.png,https://huggingface.co/blog/nvidia/kumo-tabular
-Kumo-Tabular-Medium,NVIDIA,nvidia.png,https://huggingface.co/blog/nvidia/kumo-tabular
-Kumo-Tabular-Small,NVIDIA,nvidia.png,https://huggingface.co/blog/nvidia/kumo-tabular
+Kumo-Tabular-M,NVIDIA,nvidia.png,https://huggingface.co/blog/nvidia/kumo-tabular
+Kumo-Tabular-S,NVIDIA,nvidia.png,https://huggingface.co/blog/nvidia/kumo-tabular
 EXAONE-Tabular,LG AI Research,lg.svg,https://arxiv.org/abs/2608.25774
 Causilo,Nums AI,nums-ai.png,https://arxiv.org/abs/2609.22866
 TabFM,Google Research,google.png,https://arxiv.org/abs/2609.37959

index.html

@@ -780,13 +780,13 @@ function renderCost() {
   const order = orderOf();
   const rows = D.cost.filter(r => order.includes(r.entry)).sort((a, b) => order.indexOf(a.entry) - order.indexOf(b.entry));
   const maxT = Math.max(...rows.map(r => timeOf[r.entry]));
-  const what = 'Train + fine-tune + predict' + (withEnc ? ' + encoding' : '');
+  const what = 'Train + finetune' + (withEnc ? ' + encode' : '') + ' + predict';
   const t = document.getElementById('costTable');
-  t.innerHTML = `<thead><tr><th>Encoding</th><th>Model</th><th>Device</th><th>${what} s / 1K rows</th><th class="num">Train</th><th class="num" title="TAR's LoRA fine-tuning, per 1K training rows">Fine-tune</th><th class="num">Predict</th><th class="num" title="Estimate, per 1K rows">Text enc.</th><th class="num" title="Estimate, per 1K rows">Image enc.</th></tr></thead>`;
+  t.innerHTML = `<thead><tr><th>Encoding</th><th>Model</th><th>Device</th><th>${what} s / 1K rows</th><th class="num">Train</th><th class="num" title="TAR's LoRA fine-tuning, per 1K training rows">Finetune</th><th class="num" title="Text and image encoding, estimate per 1K rows">Encode</th><th class="num">Predict</th></tr></thead>`;
   const tb = el('tbody');
   rows.forEach(r => {
     const total = timeOf[r.entry];
-    tb.appendChild(el('tr', {}, `${encodingCell(r.encoding)}<td class="model">${modelName(r.model)}</td><td><span class="dev">${r.gpu ? 'H100' : 'CPU'}</span></td><td class="mono"><span class="bar" style="width:${Math.max(2, total / maxT * 140)}px"></span>${fmt(total, 2)}</td><td class="num">${fmt(r.train_s_per_1k, 2)}</td><td class="num">${r.finetune_s_per_1k ? fmt(r.finetune_s_per_1k, 2) : '—'}</td><td class="num">${fmt(r.inference_s_per_1k, 2)}</td><td class="num">${fmt(r.text_encode_s_per_1k, 2)}</td><td class="num">${fmt(r.image_encode_s_per_1k, 2)}</td>`));
+    tb.appendChild(el('tr', {}, `${encodingCell(r.encoding)}<td class="model">${modelName(r.model)}</td><td><span class="dev">${r.gpu ? 'H100' : 'CPU'}</span></td><td class="mono"><span class="bar" style="width:${Math.max(2, total / maxT * 140)}px"></span>${fmt(total, 2)}</td><td class="num">${fmt(r.train_s_per_1k, 2)}</td><td class="num">${r.finetune_s_per_1k ? fmt(r.finetune_s_per_1k, 2) : '—'}</td><td class="num">${fmt(r.text_encode_s_per_1k + r.image_encode_s_per_1k, 2)}</td><td class="num">${fmt(r.inference_s_per_1k, 2)}</td>`));
   });
   t.appendChild(tb);
 }