Upload data.json

data.json

@@ -93,6 +93,36 @@
               "smolscript": "b4e8f5fa96434823307edee7e14b66fe8b9fc9be048ebcf7c00800ae4474270a"
             }
           }
+        },
+        {
+          "id": "HuggingFaceTB/SmolLM-135M",
+          "name": "SmolLM-135M",
+          "parameters": "135M",
+          "model_card_url": "https://huggingface.co/HuggingFaceTB/SmolLM-135M",
+          "model_type": "pretrained",
+          "evaluation_status": "evaluated",
+          "date_submitted": "2026-11-06",
+          "date_added": "2026-11-06",
+          "author_note": "A compact SmolLM foundation model pretrained on educational and web text, designed for accessible small-model experimentation.",
+          "comment_source": "dotlabs",
+          "checkpoint_revision": "1d461723eec654e65efdc40cf49301c89c0c92f4",
+          "scores": {
+            "smolcua": 0.0,
+            "marc": 1.5625,
+            "procedural_pile": 0.007275037924876215,
+            "ttm": 47.3,
+            "smolscript": 0.5374255374255374
+          },
+          "cognirank_index": 6.412700904942976,
+          "evaluation": {
+            "protocol": "cognirank2-v5",
+            "requested_context": 2048,
+            "component_protocols": {
+              "smolcua": "smolcua-v1.4-three-passes",
+              "smolcua_scoring": "v2-outer-whitespace-insensitive"
+            },
+            "smolcua_dataset_sha256": "74fb0a12a8dd75cbd8675d76e8bcf4a16e1b424668a195f4ff9a31da2becf065"
+          }
         }
       ]
     },
@@ -349,7 +379,7 @@
           "evaluation_status": "evaluated",
           "date_submitted": "2026-11-04",
           "date_added": "2026-11-04",
-          "author_note": "IBM\u2019s compact, creator-post-trained Granite model for practical language, code, and tool-use tasks.",
+          "author_note": "IBM’s compact, creator-post-trained Granite model for practical language, code, and tool-use tasks.",
           "comment_source": "dotlabs",
           "checkpoint_revision": "bd8a1497065c0d6ba1ef19af6b0d2b14bacf71c2",
           "scores": {
@@ -382,7 +412,7 @@
           "evaluation_status": "evaluated",
           "date_submitted": "2026-11-04",
           "date_added": "2026-11-04",
-          "author_note": "IBM\u2019s hybrid attention\u2013Mamba2 Granite model, post-trained for efficient instruction and tool-oriented tasks.",
+          "author_note": "IBM’s hybrid attention–Mamba2 Granite model, post-trained for efficient instruction and tool-oriented tasks.",
           "comment_source": "dotlabs",
           "checkpoint_revision": "3b17b717b8f2f5d305b0a92c1491e239aeda19c8",
           "scores": {
@@ -596,10 +626,86 @@
           }
         }
       ]
+    },
+    {
+      "id": "bananamind",
+      "name": "BananaMind",
+      "link": "https://huggingface.co/BananaMind",
+      "color": "#EEC880",
+      "models": [
+        {
+          "id": "BananaMind/BananaMind-2-Pro",
+          "name": "BananaMind-2-Pro",
+          "parameters": "139M",
+          "model_card_url": "https://huggingface.co/BananaMind/BananaMind-2-Pro",
+          "model_type": "pretrained",
+          "evaluation_status": "evaluated",
+          "date_submitted": "2026-11-06",
+          "date_added": "2026-11-06",
+          "author_note": "A 139M-parameter decoder-only base model trained from scratch with a digit-aware byte-level BPE tokenizer and a completed 100B-token pretraining curriculum.",
+          "comment_source": "dotlabs",
+          "checkpoint_revision": "846592429ab5f01390f6fa7340921823603c3c3a",
+          "scores": {
+            "smolcua": 0.0,
+            "marc": 4.0625,
+            "procedural_pile": 0.003975155279503095,
+            "ttm": 46.55,
+            "smolscript": 0.5626780626780626
+          },
+          "cognirank_index": 6.848635645226034,
+          "evaluation": {
+            "protocol": "cognirank2-v5",
+            "requested_context": 3072,
+            "component_protocols": {
+              "smolcua": "smolcua-v1.4-three-passes",
+              "smolcua_scoring": "v2-outer-whitespace-insensitive"
+            },
+            "smolcua_dataset_sha256": "74fb0a12a8dd75cbd8675d76e8bcf4a16e1b424668a195f4ff9a31da2becf065"
+          }
+        }
+      ]
+    },
+    {
+      "id": "opencerebral",
+      "name": "OpenCerebral",
+      "link": "https://huggingface.co/opencerebral",
+      "color": "#98CCD6",
+      "models": [
+        {
+          "id": "opencerebral/Boris-2-Preview-2",
+          "name": "Boris-2",
+          "parameters": "144M",
+          "model_card_url": "https://huggingface.co/opencerebral/Boris-2-Preview-2",
+          "model_type": "pretrained",
+          "evaluation_status": "evaluated",
+          "date_submitted": "2026-11-06",
+          "date_added": "2026-11-06",
+          "author_note": "A 144M-parameter dense Boris 2 language-model checkpoint from a 100B-token pretraining run, evaluated with a 2K-token context.",
+          "comment_source": "dotlabs",
+          "checkpoint_revision": "f2615d19c87b92b5d64a611030d7bbfb7c1da5b1",
+          "scores": {
+            "smolcua": 0.0,
+            "marc": 8.125,
+            "procedural_pile": 0.01105108843754413,
+            "ttm": 44.2,
+            "smolscript": 0.0
+          },
+          "cognirank_index": 7.296010755460537,
+          "evaluation": {
+            "protocol": "cognirank2-v5",
+            "requested_context": 2048,
+            "component_protocols": {
+              "smolcua": "smolcua-v1.4-three-passes",
+              "smolcua_scoring": "v2-outer-whitespace-insensitive"
+            },
+            "smolcua_dataset_sha256": "74fb0a12a8dd75cbd8675d76e8bcf4a16e1b424668a195f4ff9a31da2becf065"
+          }
+        }
+      ]
     }
   ],
   "evaluation_protocol": "cognirank-2026-11-update",
-  "updated_at": "2026-11-05",
+  "updated_at": "2026-11-06",
   "reproduction_url": "https://huggingface.co/datasets/dotlabs/cognirank",
   "evaluation_context": 4096
 }